# Structure-Based Design and Pharmacophore-Based Virtual Screening of Combinatorial Library of Triclosan Analogues Active against Enoyl-Acyl Carrier Protein Reductase of Plasmodium falciparum with Favourable ADME Profiles

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## Abstract

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## 1. Introduction

**TCL1**, Figure 1), a common antibacterial agent incorporated into a variety of consumer products, is an uncompetitive inhibitor of purified PfENR, which has demonstrated inhibitory potency against P. falciparum parasites cultured in vitro $\left({\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=73\mathrm{n}\mathrm{M}\right)$ [16,17,18]. The

**TCL1**derivatives (Figure 1) are non-toxic to mammalian cells [17,19] which do not contain ENR homologues. The human exposure routes, metabolism, and toxicity of triclosan have recently been reviewed with a conclusion that adverse biological effects of triclosan and its degradation products need to be investigated [20]. Freundlich previously reported that

**TCL1**analogues inhibit both PfENR enzymes and Mycobacterium tuberculosis ENR enzymes [21,22,23,24,25]. PfENR is a well-known validated pharmacological target of antimalarials [26]. The existence of X-ray crystal structures of

**TCL1**and its analogues plus the NAD

^{+}cofactor bound to PfENR facilitate the possibility of computer-assisted drug design of antimalarial agents, which target lipid synthesis in the plasmodium parasite [18,21]. In the bound conformation, the aromatic rings of

**TCL1**are orthogonal to each other, the phenol ring mimicking the enolate intermediate of the reaction catalysed by the ENR. Simultaneously the

**TCL1**is face to face π-stacked with NAD+, T-shaped π-stacked with the catalytic Tyr 277, and forms hydrogen bonds to the cofactor and the tyrosine, respectively [21]. Replacements of chlorine atoms at positions 5, 2′ and 4′ on the

**TCL1**diarylether scaffold by larger groups resulted in energetically favourable interactions with both the enzyme and the co-factor [22,23,27,28]. Synthesis of 4′-derivatives of

**TCL1**caused minor gains in the inhibitory potencies towards PfENR [27], while the analogues substituted in the 2′ position reached only the micromolar range of the ${\mathrm{I}\mathrm{C}}_{50}\mathrm{s}$ [23]. Sullivan et al. [28] described the synthesis and inhibitory activity tests of 5-alkyl-substituted

**TCL1**derivatives toward purified PfENR. Chibber et al. [22] studied TCL analogues with mainly hydrophilic 5-substituents in carbon 5, which were less potent than

**TCL1**. Freundlich et al. [21] reported on the synthesis and activity tests of

**TCL1**analogues containing hydrophobic substituents at position 5 intended to better ‘root’ the inhibitor at the active site of PfENR. Their results demonstrated the possibility of improving the in vitro antimalarial potency of

**TCL1**by replacing the suboptimal 5-chloro group with larger hydrophobic moieties. The

**TCL1**scaffold has inspired numerous initiatives to design potent PfENR inhibitors in a search for new antimalarials [29,30,31]. In our previous work, a QSAR model of TCL binding to PfENR was built using the LUDI scoring function to allow the in silico screening of a virtual combinatorial library of 120

**TCL1**analogues with favourable predicted ADME profiles [32]. The best designed TCLs reached predicted ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ slightly over 10 nM (Figure 1). So far, few 3D-QSAR pharmacophore (PH4) studies have been reported for the inhibition of PfENR by TCL. The PH4 pharmacophore was used for

**TCL1**in complex with PfENR and based on receptor coordinates recovered from the X-ray structure 3F4B (Protein Data Bank) [33]. Unfortunately, this PH4 model is spatially limited, since 5′-Cl is not large enough to fill the surrounding hydrophobic pocket of the enzyme active site. The search for a reliable PH4 model taking into account the reported SAR of PfENR inhibition for the known TCL inhibitors [9,14,15,16,18] seems tricky, as illustrated in the following comparative analysis of the role of 5′-substituents:

**TCL1**(${\mathrm{R}}_{1},{\mathrm{R}}_{2},{\mathrm{R}}_{3}\equiv $ Cl, Cl, Cl; 73 nM),

**TCL10**(${\mathrm{R}}_{1},{\mathrm{R}}_{2},{\mathrm{R}}_{3}\equiv $ CH

_{2}–Ph, Cl, Cl; 71 nM),

**TCL11**(${\mathrm{R}}_{1},{\mathrm{R}}_{2},{\mathrm{R}}_{3}\equiv $ (CH

_{2})

_{2}–Ph, Cl, Cl; 76 nM), and

**TCL15**(${\mathrm{R}}_{1},{\mathrm{R}}_{2},{\mathrm{R}}_{3}\equiv $pF–Ph, Cl, Cl; 38 nM). Extension of the –(CH

_{2})

_{n}– linker to fill the hydrophobic pocket next to C-5 is not advantageous, while the p-fluorophenyl of

**TCL15**increases the potency twice, in contrast to linkers. Therefore, the design of new more potent TCLs against PfENR requires careful application of the 3D-QSAR PH4 model, attention to the active conformation, and also exploration of the pockets of the active site for the positions C2′ and C4′ of the TCL scaffold.

- -
- We built and validated a Hansch-type QSAR model to correlate the relative Gibbs free energy (rGFE) of TCL binding to PfENR with the observed inhibitory potencies $\left({\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}\right)$ (identification of the bound conformation of TCL inhibitors).
- -
- The robustness of the built model is confirmed by a 3D-QSAR pharmacophore (PH4) model based on the bound conformations of the training set of inhibitors (generation of the PH4 model).
- -
- In addition, we built a virtual library of 33,480 analogues of TCLs and screened them with the help of PH4 to identify hits for inhibition of PfENR (virtual screening).
- -
- Finally, the ADME profile of the best designed analogues was predicted and compared with those of current antimalarial drugs and compounds undergoing clinical trials (postprocessing step 1).
- -
- Each of the hits was cross-checked for predicted inhibitory potency by computed rGFE of the formation of the PfENR-TCLx complex, which led to the identification of a handful of prospective novel TCLs that exceeded the potency of known TCLs against PfENR (postprocessing step 2).
- -
- The top 5 TCL hits and
**TCL11**underwent molecular dynamics simulations to explore the stability of the PfENR-TCLx complexes (postprocessing step 3).

## 2. Results

#### 2.1. Training and Validation Sets of TCL Inhibitors

#### 2.2. QSAR Model of PfENR Inhibition

#### 2.2.1. Single-Descriptor QSAR Model of PfENR Inhibition by TCLs

**TCL11**(PDB entry code 2OOS [21]) as described in Section 4. Furthermore, the relative Gibbs free energy (rGFE) of the complex formation (${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$) and its components was calculated for each of the 24 geometry-optimised PfENR-TCLx complexes for the triclosan derivatives (Table 2; see footnote of Table 2 for definitions of the quantities). A QSAR model that correlates the computed rGFE with the experimental inhibitory potencies of TCLs (${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=-{\mathit{log}}_{10}{\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ [17]) explained 95% of the variation in the observed activities. The resulting linear regression equation is shown in Table 3, Equation (2). Relatively high values of the regression coefficient ${\mathrm{R}}^{2}$, leave-one-out cross-validated regression coefficient ${\mathrm{R}}_{\mathrm{x}\mathrm{v}}^{2}$ and Fischer F-test of the correlation suggest a strong relationship between the 3D model of inhibitor binding and the observed inhibitory potencies of TCL [21]. Therefore, structural information derived from the 3D models of PfENR-TCLx complexes can be expected to lead to a reliable prediction of the inhibitory potencies of PfENR for new TCL analogues based on QSAR Equation (2) (Table 3). To gain a better understanding of the binding affinity of TCLs to PfENR, we have analysed the enthalpy of complexation in gas phase ${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}$ by correlating it with the ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$. The validity of the resulting linear correlation (for statistical data from the regression, see Table 3, Equation (1)) allowed the assessment of the contribution of inhibitor-enzyme interactions (${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}$) when the solvent effect and loss of inhibitor entropy upon binding to the enzyme were neglected. This correlation explained about 94% of the ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ data variation and underlined the role of interatomic interactions in determining the binding affinity of the ligands. These statistically significant correlations allowed us to reveal the bound conformations of the TCLs at the PfENR binding site and allowed the definition of the PH4 pharmacophore.

**TCL1–20**.

#### 2.2.2. Binding Mode of TCLs to PfENR

**TCL11**complex (Figure 3, [21]), namely the 𝜋-𝜋 stacking between the phenol moiety and the nicotinamide ring of ${\mathrm{N}\mathrm{A}\mathrm{D}}^{+}$, are conserved. Furthermore, hydrogen bonds (HB) involving the hydroxyl group of TCL and Tyr277, as well as the ribose ring of ${\mathrm{N}\mathrm{A}\mathrm{D}}^{+}$ (Figure 3 and Figure 4), are also preserved.

**TCL15**the hydrophobic ${\mathrm{R}}_{1}$ substituent Ph $-$pF reaches the hydrophobic pocket and contributes significantly to the stabilising interactions at the active site of PfENR. The ${\mathrm{R}}_{2}$ group of TCL analogues is surrounded by active site residues Asn218, Val222, Met281, and substrate binding loop residues Ala319 and Ile323.

**TCL1**analogues with the same mode of PfENR binding will lead to improved potencies of the new structurally similar compounds, provided that the predictive power of correlation Equation (2) also extends beyond the range of activities of the training set. The predictive ability of our QSAR model relying on the computed ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$ was also confirmed by the PH4 pharmacophore model of the PfENR inhibitory activity.

#### 2.3. 3D-QSAR Pharmacophore Model of PfENR Inhibition

**TCL1–20**complexes that served to develop the QSAR model. Unlike direct pharmacophore generation from small free (not bound) molecules, the HypoGen algorithm in the 3D-QSAR pharmacophore protocol of Discovery Studio (DS) [34] generates a PH4 in order to estimate the biological activity (${\mathrm{I}\mathrm{C}}_{50}$) of the 20 training set compounds. Their ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ values span 3.42 orders of magnitude (from 38 to 10

^{5}nM), which is very close to the recommended 3.5 for reliable PH4 generation. To generate PH4, the constructive step of the algorithm selects compounds with ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}\le 1.25\times 38\mathrm{n}\mathrm{M}$ in order to build the starting PH4 features; only

**TCL15**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=$ 38 nM) is retained for this purpose. The 3.5 orders of magnitude criterion also served to identify the TCL compounds to be considered inactive in the subtractive step of the PH4 generation (${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}>38\times {10}^{3.5}\mathrm{n}\mathrm{M}=120167\mathrm{n}\mathrm{M}$). Therefore, none of the other compounds from

**TCL1–20**were subtracted, which means that all the PH4 features generated from

**TCL15**were conserved for the processing of the hypotheses. Finally, during the last PH4 generation step, namely the optimisation phase, the score of the PH4 hypotheses is improved. According to a detailed description in the literature [35] as well as in Section 4, four main features were identified: hydrophobic (HYD) complementary to enzyme lipid pockets, hydrogen bond donor (HBD), and acceptor (HBA) binding to active site residues by acceptor and donor hydrogen bonds, respectively. Virtually screened molecules were mapped to these four pharmacophore features. The output of the HypoGen process is the top 10 PH4 hypotheses ranked according to their statistical data as presented in Table 4. Among them, the hypothesis cost is one of the most pertinent: the metrics defines the hypothesis total cost as a linear combination of three components, namely the weight, the error, and the configuration cost (see Section 4). It spans a range from 409.3 (Hypo1) to 1231.8 (Hypo10), all in a monotonic increasing order. Since the total cost is 803.5 (almost twice that of Hypo1), Hypo1 is the closest to the fixed cost and is very far from the other nine models. For the entire set of 10 hypotheses, two extreme additional costs are computed: the fixed cost (lower limit) of 15.2 and the null cost (upper bound) of 5423.2; the difference Δ = 5408.1 between them is the first criterion of the analysis. The larger the value of Δ (Δ > 70), the more meaningful are the top hypothesis models with a statistical significance greater than 90%. The second indicator is the difference between the null cost and the total cost of each hypothesis which is higher than 4190, indicating that all the 10 hypothesis models are very far from the null cost. The third indicator is the configuration cost of 9.55, far below 17, which is the value it should not exceed [36]. The next descriptor is the root-mean-square deviation (RMSD) of each hypothesis. The RMSD value of Hypo1 is equal to 6.6, the lowest. Hypo2 reached a RMSD of 9.5. Finally, the correlation coefficient (R

^{2}) of the training set reached a value of 0.96 for Hypo1 and 0.93 for the following Hypo2. All generated hypotheses display HYD, HBA, and HBD features. Finally, the first hypothesis (Hypo1) was retained for the in silico screening of the virtual library of TCL analogues. The additional statistical parameters of the regression for ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ vs. ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ estimated from Hypo1 for the training set are: ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=0.9754\times {p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}+0.1596$ ($\mathrm{n}=20$, ${\mathrm{R}}^{2}=0.98$, ${\mathrm{R}}_{\mathrm{x}\mathrm{v}}^{2}=0.96$, $\mathrm{F}-\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}=713.56$, $\mathsf{\sigma}=0.145$, $\mathsf{\alpha}>98\mathrm{\%}$). The correlation is plotted in Figure 5 and is very close to a slope (1.02) of one and an intercept of 0.13 near the origin. The parameters reflect the guidance of the Organisation of Economic Cooperation and Development (OECD) about QSAR [37]. An additional verification is the mapping of the most potent inhibitor

**TCL15**to the Hypo1 features as shown in Figure 5. In the same figure, the detailed geometry and position of the Hypo1 PH4 features of PfENR inhibition are presented. PH4 was externally evaluated by a validation set of 4 derivatives,

**TCL21–24**, that belong to the same interval of inhibitory potencies as the compounds of the training set.

**TCL1–20**. From the lowest cost among the 49 hypotheses for each of the 10 PH4 (Hypo1–Hypo10) listed in Table 4 (closest random), none shows a cost lower than the 409.3 of Hypo1. Randomisation confirms that Hypo1 is not produced by chance and is a true correlation supported by a statistically robust pharmacophore model for inhibition of PfENR by TCL with a confidence level of 98%. It shows predictive power as the QSAR complexation model on the ligand affinity descriptor ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$.

#### 2.4. Virtual Library Generation and In Silico Screening of PfENR Inhibitors

#### 2.4.1. Virtual Library

#### 2.4.2. In Silico Screening

#### 2.5. Novel TCLs against PfENR

_{1}moiety of TCL that fills the hydrophobic pocket corresponding to the HYD pharmacophoric feature is a key determinant of improving the potency of TCLs against the PfENR. Five R

_{1}-groups emerged: No.

**55**(frequency: 12),

**56**(10),

**37**(9),

**40**(9) and

**59**(8) (Table 5, A). As we can see, these R

_{1}-groups include a linear heptyl chain or p-substituted phenyl ring connected to the TCL skeleton via a linker composed of 4 heavy atoms, similar to the

**TCL11**. The ${\mathrm{R}}_{2}$-groups are preferably occupied by medium-sized fragments such as:

**23**(40),

**8**(17), and

**16**(16), while small-size R

_{3}-groups

**8**(27),

**10**(21),

**11**(19) and

**1**(18) contain chiefly small primary, secondary, and tertiary amines (Figure 6).

**TCL-33**-

**08**-

**05**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 4.1 nM),

**TCL-58**-

**01**-

**01**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 13.3 nM),

**TCL-59**-

**01**-

**01**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 10.8 nM)

**TCL-60**-

**01**-

**01**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 9.1 nM),

**TCL-60**-

**16**-

**08**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 10 nM),

**TCL-58**-

**16**-

**08**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 9.2 nM),

**TCL-58**-

**23**-

**09**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 3.1 nM), and

**TCL-60**-

**08**-

**08**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 1.9 nM) (Table 5 and Table 6). They include the following fragments at ${\mathrm{R}}_{1}$-position:

**33**: ···CH

_{2}-cyclohexyl;

**58**: ···n-hexyl;

**59**: ···n-heptyl;

**60**: ···n-octyl. At ${\mathrm{R}}_{2}$-position: 1: ···H;

**8**: ···NH

_{3}

^{+};

**16**: ···trimethylamine;

**23**: ···Ph; and at ${\mathrm{R}}_{3}$-position

**1**: ···H;

**5**: ···OH;

**8**: ···NH

_{3}

^{+};

**9**: ···C

_{2}H

_{5}. Due to the amino acid composition of the larger hydrophobic pocket, the ${\mathrm{R}}_{1}$-groups display preferences for bulkier nonpolar building blocks of quite variable topology. The ${\mathrm{R}}_{2}$-groups do not show a common character either, while the ${\mathrm{R}}_{3}$-groups show preference mainly for small polar or cationic substituents. In the specific case of PfENR, the hydrophobic contacts are expected to root aryl-substituted inhibitors at the active site. The presence of small polar and cationic substituents in the ${\mathrm{R}}_{3}$-position resulted in strong electrostatic attraction with the proximate phosphate groups of the cofactor nicotinamide adenine dinucleotide. The cationic substituents in the partially solvent-exposed ${\mathrm{R}}_{3}$-position caused stabilization of the bound TCL analogues by the effect of hydration. These substitutions led to an overall predicted increase in the binding affinity of the new TCL analogues to PfENR. The best TCL analogues

**TCL-60**-

**08**-

**08**and

**TCL-33**-

**08**-

**05**(Figure 7) show predicted half-maximum inhibitory concentrations of (${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 1.9 nM and ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 4.1 nM), i.e., could be almost 20 times more potent than the most active compound in the training set, namely

**TCL15**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=38\mathrm{n}\mathrm{M}$). Molecular mechanics refinement of the 219 top-ranking TCL analogues and prediction of the half-maximal inhibitory concentrations by means of computed rGFE of the ligands led to the identification of potent virtual hits with chemical structures that reach beyond the requirements of the PH4 pharmacophore model, especially in the ${\mathrm{R}}_{2}$- and ${\mathrm{R}}_{3}$-positions where hydrophobic interactions were expected.

_{1}-groups of TCLs is a key determinant of the predicted increase in the affinity of virtual hits. To gain a deeper understanding of enzyme-inhibitor interactions, the calculated interaction energy (${\mathrm{E}}_{\mathrm{i}\mathrm{n}\mathrm{t}}$) of the most potent training set residues with the PfENR was analysed in terms of contributions from individual residues of the active site (Figure 8). To compare ${\mathrm{E}}_{\mathrm{i}\mathrm{n}\mathrm{t}}$ contributions of important active site residues to the total enzyme-inhibitor interaction energy for

**TCL1**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=73\mathrm{n}\mathrm{M}$) and

**TCL15**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=38\mathrm{n}\mathrm{M}$), the contributions of residues are listed: Tyr267 (−2.6 kcal·mol

^{−1}for

**TCL1**vs −3.9 kcal·mol

^{−1}for

**TCL15**), Tyr277 (−4.1 vs −5.1 kcal·mol

^{−1}), Gly313, Pro314 (−0.6 vs −1.3 kcal·mol

^{−1}), Phe368 (−1.0 vs −2.7 kcal·mol

^{−1}), Ile369 (−2.0 vs −4.1 kcal·mol

^{−1}), and Ala372 (−0.5 vs −1.0 kcal·mol

^{−1}). Taken together, this results in ${\mathrm{E}}_{\mathrm{i}\mathrm{n}\mathrm{t}}=$ −10.8 kcalvmol

^{−1}for

**TCL1**vs ${\mathrm{E}}_{\mathrm{i}\mathrm{n}\mathrm{t}}=$ −18.1 kcal·mol

^{−1}for

**TCL15**, approximately −7.3 kcal·mol

^{−1}in favour of the more potent inhibitor

**TCL15**(interactions with the cofactor were not considered). Analysis confirmed that the R

_{1}-groups contribute significantly to the inhibitor binding.

#### 2.6. Pharmacokinetic Profile of Novel TCL Analogues

#### 2.7. Molecular Dynamics Simulations

**TCL11**and five of the best-designed TCL analogues at the active site of PfENR. Thus, the complexes obtained from the modification in situ of the reference inhibitor

**TCL11**and subsequent refinement through molecular mechanics were used as starting geometries for the molecular dynamics calculations using the Desmond software [42] (see Section 4). Table 8 presents the averages of total energy $\u2329{\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}}\u232a$ and potential energy $\u2329{\mathrm{E}}_{\mathrm{p}\mathrm{o}\mathrm{t}}\u232a$ of the systems during the 200 ns simulation. Figure 9 illustrates the simulated system and Figure 10 shows the time evolution of bound inhibitor properties, such as the root mean square deviation (RMSD) from the initial conformation, the radius of gyration (rGyr), number of intramolecular hydrogen bonds (intraHB) molecular surface area (molSA), solvent-accessible surface area (SASA) and the polar surface area (PSA). Protein-ligand interactions were explored throughout the simulation trajectory to identify specific interactions maintained during computation (Figure 11). Finally, the interactions that occur more than 20.0% of the simulation time in the trajectory (0–200 ns) are shown on a detailed 2D schematic representation (Figure 12). Moreover, we have superimposed the conformations of the ligands obtained following the minimisation of the complexes from molecular dynamics on those obtained by in situ modification of the

**TCL11**and MM refinement. Figure 13 illustrates these superimpositions and the respective RMSDs. The RMSD, rGyr, SASA, and the remaining parameters document that the pose of the bound new TCLs in the complexes with PfENR remains stable within the MD simulation time (Figure 10). The higher contribution of hydrogen bonding interactions of the new TCL analogues with the active site residues as compared to the predominantly hydrophobic interaction of the

**TCL11**reference compound suggests an elevated specificity of these virtual hits for the PfENR target (Figure 11).

## 3. Discussion

#### 3.1. Binding Mode of TCL

**TCL1**) [21,23,27]. Characteristic intermolecular interactions of

**TCL1**and PfENR include 𝜋-𝜋 stacking interaction between the phenol moiety and the nicotinamide ring of ${\mathrm{N}\mathrm{A}\mathrm{D}}^{+}$, hydrogen bonds between the hydroxyl group of

**TCL1**and -OH group of Tyr277 and the C

_{3}-OH group of the ribose ring of ${\mathrm{N}\mathrm{A}\mathrm{D}}^{+}$. According to our analysis of the PfENR-TCLx complexes of predicted most potent inhibitors, these key interactions are conserved throughout this class of compounds and contribute also to predicted inhibitory potencies of novel triclosan derivatives. In addition, in the ${\mathrm{R}}_{3}$-position of triclosan, a H-bond with the backbone of residue Ala217 and with the ${\mathrm{N}\mathrm{A}\mathrm{D}}^{+}$ cofactor was reported by Kumar et al. [31] (Figure 7). The most potent analogues

**TCL-60-08-08**and

**TCL-33-08-05**with the ${\mathrm{R}}_{1}$-group

`···`CH

_{2}-cyclohexyl or the flexible

`···`octyl do not have the capacity to form stacking interactions but can still well fill the hydrophobic pocket formed by residues Tyr267, Pro314, Ile369, Phe368, and Ala372. These ${\mathrm{R}}_{1}$-groups, in combination with protonated amine or hydroxyl groups in the ${\mathrm{R}}_{2}$-position and ${\mathrm{R}}_{3}$-position that interact with the phosphate groups of ${\mathrm{N}\mathrm{A}\mathrm{D}}^{+}$ or with solvent, also contribute substantially to stabilisation of the PfENR-TCLx complexes. Thus, new TCL derivatives with potential to be developed into novel antimalarial agents that act on a new pharmacological target are proposed to medicinal chemistry laboratories for experimental verification.

#### 3.2. Molecular Dynamics Simulations

**TCL11**and MM refinement and those obtained as ensemble averages over 500 snapshots from MD trajectories (Figure 13), suggest that the modelled complexes PfENR-TCLx are stable. Moreover, MD simulations confirmed the validity of the binding mode of new triclosan analogues generated from the crystal structure of PfENR-

**TCL11**[21].

## 4. Materials and Methods

#### 4.1. Training and Validation Sets of TCL Inhibitors

#### 4.2. Model Building

**TCL11**(Protein Data Bank [43] entry code 2OOS at a resolution of 2.1 Å [21]) using Insight-II molecular modelling program [44]. The structures of the E and E–I complexes were considered at an appropriate physiologically relevant pH of 7 with capped neutral N- and C-terminal residues and all protonated and ionised residues charged. Crystallographic water molecules were included in the model. TS and VS were built into the structure of the reference complex PfENR-

**TCL11**by in situ replacement of the derivatised R groups of the TCL moiety. An exhaustive conformational search over all rotatable bonds of the replacing function group, coupled with a careful gradual energy optimisation of the modified inhibitor and the PfENR active site residues located in the vicinity of the inhibitor (within 5 Å distance), was employed to identify the low energy bound conformations of the modified inhibitor. The resulting low-energy structures of the E–I complexes were then carefully refined by minimisation of the whole protein. This procedure has previously been successfully used for model building of viral, bacterial, and protozoal protease inhibitor complex models and design of peptidomimetic, hydroxy naphthoic, and thymidine-based inhibitors [45,46,47,48,49,50].

#### 4.3. Molecular Mechanics

^{−1}·Å

^{−1}for the average gradient.

#### 4.4. Conformational Search

#### 4.5. Solvation Gibbs Free Energies

_{o}= 80) and the solute as charge distribution filling a low dielectric (ε

_{i}= 4) cavity with boundaries related to the molecular surface. The module numerically solves for the molecular electrostatic potential and reaction field around the solute using the finite difference method. DelPhi calculations were done on a (235 × 235 × 235) cubic lattice grid for the E–I complexes and free E and on a (65 × 65 × 65) grid for the free I. Full coulombic boundary conditions were used. Two subsequent focussing steps led to a similar final resolution of about 0.3 per grid unit at 70% filling of the grid by the solute. Physiological ionic strength of 0.145 mol·dm

^{−3}, partial atomic charges and radii defined in the set of CFF force field parameters [34] and a probe sphere radius of 1.4 were used. The electrostatic component of the Poisson–Boltzmann solvation Gibbs free energy was calculated as the reaction field energy [53,54,55].

#### 4.6. Calculation of the Entropic Contribution

#### 4.7. Calculation of Binding Affinity and QSAR Model

#### 4.8. Interaction Energy

#### 4.9. Pharmacophore (PH4) Generation

**TCL11**[21], so no new bioactive TCL conformations were generated during the process. Kurogi and Güner reported in a review the complete theory and successful applications of the PH4 generation process using the Catalyst software [62]. On the basis of the biological activities, HypoGen will optimise the pharmacophore hypotheses deduced from the training set inhibitors with the highest activity and omitting the least active molecules. The top scoring hypothesis of the pharmacophore will be built up in three main stages (constructive, subtractive and optimisation steps) from the set of most active inhibitors [62]. Inactive molecules serve for the definition of the excluded volume. The maximum number of five features allowed by the HypoGen algorithm was selected on the basis of the TCL scaffold and substituents during the pharmacophore generation, namely: hydrophobic aromatic (HYdAr), hydrophobic aliphatic (HYd), hydrogen-bond donor (HBD), hydrogen-bond acceptor (HBA) and ring aromatic (Ar). Many remaining features, among which positive charge (PC), positive ionisable (PIC) and negative charge (NC), negative ionisable (NIC) were not selected because of the neutral type of TCL molecules considered in this study. Additional features may be added if necessary [60]. Today, the concept of pharmacophore has been extended beyond the initial horizon of drug design to reach chemoinformatics and material-informatics QSPR as “Photovoltaphores” to identify metal-free dyes for dye-sensitized solar cells [63]. The adjustable parameters of the protocol were kept at their default values, except for the uncertainty of the activity, which was set to 1.25 instead of 3. Uncertainty is the range of the activity value with a default of <IC

_{50}/3, 3 IC

_{50}>. Our choice to bring the uncertainty interval for experimental activity to a relatively narrow <4 IC

_{50}/5, 5 IC

_{50}/4> takes into account the precision and homogeneity of the observed inhibitory activities measured by the same method in a single laboratory. Therefore, during the constructive step, the activity of the most active (MA) compound is multiplied by 1.25 instead of 3 to reach a value A = $\frac{{5\times \mathrm{I}\mathrm{C}}_{50}\left(\mathrm{M}\mathrm{A}\right)}{4}$ and the activity of the second most active (NMA) is multiplied by 4/5 to give a value B = $\frac{{4\times \mathrm{I}\mathrm{C}}_{50}\left(\mathrm{N}\mathrm{M}\mathrm{A}\right)}{5}$. In case B is smaller than A, NMA is included in the set of most active compounds. Assuming the set of default values of 3.0 A’ = ${3\times \mathrm{I}\mathrm{C}}_{50}\left(\mathrm{M}\mathrm{A}\right)$ and B’ = $\frac{{1\times \mathrm{I}\mathrm{C}}_{50}\left(\mathrm{N}\mathrm{M}\mathrm{A}\right)}{3}$ broadens the most active training set excessively. The subtractive step will consider as inactive those TCL compounds with activity 3.5 orders of magnitude beyond ${\mathrm{I}\mathrm{C}}_{50}\left(\mathrm{M}\mathrm{A}\right)$: ${\mathrm{I}\mathrm{C}}_{50}={10}^{3.5}{\mathrm{I}\mathrm{C}}_{50}\left(\mathrm{M}\mathrm{A}\right)$. Any pharmacophore that matches more than half of the set of inactive TCLs is removed. The default value of 3.5 is user adjustable if the range of experimental activities does not span 3.5 orders of magnitude [62]. For the optimisation phase, the hypothesis score will be improved for the remaining pharmacophore configurations by perturbations based on the estimated errors of the activity during the linear regression and complexity of each hypothesis. According to the law of parsimony (Occam’s razor) “the simpler hypothesis that accurately estimates the activity is considered the best” [62].

- -
- Cost(error) increases as the Root Mean Square difference between the estimated and experimental activities for the training set compounds;
- -
- $\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}\left(\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\right)$ increases in a Gaussian form as the feature weight deviates from an ideal value (2.0);
- -
- The configuration cost $\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}\left(\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{F}\mathrm{i}\mathrm{g}\mathrm{u}\mathrm{r}\mathrm{e}\right)$ is a fixed cost depending on the complexity of the hypothesis space being optimised. It is equal to the entropy of the hypothesis space (log
_{2}P, P: the number of hypotheses initially created in a constructive phase that emerged through the subtractive one). In the standard HypoGen mode, its value should not exceed 17.

#### 4.10. ADME Properties

#### 4.11. Virtual Library Generation

^{−4}kcal·mol

^{−1}, R.M.S. displacement of 10

^{−5}Å) and a dielectric constant of 4 using class II consistent force field CFF [34] as described in Section 4.3.

#### 4.12. ADME-Based Library Focussing

#### 4.13. Pharmacophore-Based Library Searching

#### 4.14. Inhibitory Potency Prediction

#### 4.15. Molecular Dynamics Simulations

**TCL11**) and the five selected top-ranking designed TCL analogues (

**TCL-33-08-05**,

**TCL-58-01-01**,

**TCL-58-16-08**,

**TCL-59-01-01**,

**TCL-60-01-01**obtained by in situ modification of the reference inhibitor

**TCL11**and refinement was evaluated by molecular dynamics (MD) simulations as described recently [67]. We have carried out 200 ns long simulations of these five explicitly solvated complexes in the NPT statistical ensemble (300 K, 1 bar) by using Desmond software [42]. A cubic periodic box with 10

`Å`buffer containing the E–I complex was filled with TIP3P water molecules and neutralised by adding the required number of counterions to reach electroneutrality (Table 9). During the simulation, OPLS4 force field [68], 1.5 fs integration step, and the coulombic interaction cut-off point of 14 Å, were used. The Nose–Hoover chains thermostat and Martyna–Tobias–Klein barostat methods were employed during the simulations [69,70]. After initial heating and relaxation, the productive simulation trajectory was recorded and analysed for ligand-receptor interactions every 400 ps, that is, 500 frames during each MD simulation.

## 5. Conclusions

**TCL11**complex [21] and their inhibitory potencies toward PfENR were predicted from computed rGFE of E–I complex formation, which correlated with the observed activities ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ of the training and validation set of TCLs. In silico screening, refinement of the structure of PfENR-TCLx complexes and prediction of ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ enabled identification of new TCL analogues with optimal substitutions in the ${\mathrm{R}}_{1}$, ${\mathrm{R}}_{2},$ and ${\mathrm{R}}_{3}$-positions of the TCL scaffold (Table 1). New triclosan analogues with predicted inhibitory potencies

**TCL-60-08-08**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 1.9 nM),

**TCL-33-08-05**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ = 4.1 nM), and

**TCL-33-16-08**with (${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$= 7.8 nM) display high predicted binding affinity to PfENR (Table 6), and favourable ADME-related properties (Table 7). Therefore, they could form new prospective candidates for reversible inhibitors aimed at the new validated pharmacological target of P. falciparum so far not connected with drug resistance to clinically used antimalarial therapeutics. Molecular dynamics simulations confirmed the stability of these enzyme–inhibitor complexes. Therefore, we can recommend these new molecules for synthesis and experimental testing of their inhibitory potency and antimalarial effect on the PfENR.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Chemical structure of

**TCL1**triclosan. (

**A**) scaffold atom numbering and the positions of the R-group are indicated. (

**B**) Structure of

**TCL11**(${\mathrm{I}\mathrm{C}}_{50}=$ 76 nM, Table 1) and 2D interaction diagram with residues from the active site of PfENR (PDB entry 2OOS) [21]. Atom colouring: C—grey, O—red, Cl—green. (

**C**) triclosan analogues that were previously predicted to be potent inhibitors of PfENR [6,32].

**Figure 2.**(

**A**) graph of the correlation Equation (1) (Table 3) between ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ and relative enthalpic contribution ${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}$ [kcal·mol

^{−1}] to the rGFE. (

**B**) plot of the rGFE of the PfENR-TCLs complex formation ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$ [kcal·mol

^{−1}] (Equation (2), Table 3) of the training and validation sets [21]. The data points from the validation set are shown in red.

**Figure 4.**(

**Left**) 3D structure of the active site with the most potent inhibitor

**TCL15**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=$ 38 nM, [21]). The carbon atoms are coloured yellow; the residue side chains and carbon atoms of the NAD cofactor are coloured cyan and purple, respectively. Interaction colour code: hydrogen bonds (green), hydrophobic contacts of alkyl groups (pink), $\mathsf{\pi}$ -$\mathsf{\pi}$ interactions (dark pink). (

**Right**) Molecular surface of the active site of PfENR. Surface colouring legend: red, hydrophobic; blue, hydrophilic; white, intermediate.

**Figure 5.**Features of the best PH4 model (Hypo1) of PfENR inhibitors generated by the 3D-QSAR pharmacophore module: (

**A**) Coordinates of the centres, (

**B**) mapping of Hypo1 with

**TCL15**(the most potent TCL molecule of the training set), (

**C**) Distances in

`Å`between the centres of the pharmacophoric features, (

**D**) Angles ($\mathrm{d}\mathrm{e}\mathrm{g}$) between the centres. Colour code of features: blue—hydrophobic aliphatic; green—hydrogen bond acceptor; cyan—hydrophobic aromatic. (

**E**) Plot of the linear correlation of experimental vs. predicted inhibitory activity (blue circles correspond to the training set and orange to the validation set).

**Figure 6.**Histograms of the frequency of occurrence of individual fragments for the R-groups (

**A**) R

_{1}, (

**B**) R

_{2}, and (

**C**) R

_{3}in the 219 virtual TCL hits targeting PfENR.

**Figure 7.**(

**A**) Close-up of one of the most active designed TCL analogues,

**TCL-33**-

**08**-

**05**(${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$= 4.1 nM) at the active site of PfENR. The side chains of the interacting active site residues are coloured cyan, and carbon atoms of the ligand and the cofactor ${\mathrm{N}\mathrm{A}\mathrm{D}}^{+}$ are colored yellow and purple, respectively. (

**B**) 2D schematic interaction diagrams of the

**TCL-33**-

**08**-

**05**at the active site of PfENR. Atom colouring: C-grey, N-blue, O-red. (

**C**) Mapping of

**TCL-33**-

**08**-

**05**to the pharmacophore model for PfENR.

**Figure 8.**Plot of the energy of the intermolecular interaction ${\mathrm{E}}_{\mathrm{i}\mathrm{n}\mathrm{t}}$ between TCL inhibitors and PfENR, calculated by splitting the molecular mechanics into the contributions of individual residues for the most active TCL inhibitors, Table 1 [21,23,25,27], in [kcal·mol

^{−1}].

**Figure 10.**Time-evolution of the properties of the six PfENR-TCLx complexes during 200 ns MD simulation. For each inhibitor, top to bottom: plot of the root mean square deviation (RMSD) with respect to the initial conformation vs. the simulation time, radius of gyration (rGyr), number of intramolecular hydrogen bonds (intraHB), molecular surface area (molSA), solvent-accessible surface area (SASA), and polar surface area (PSA).

**Figure 11.**Contribution of individual active site residues to inhibitor binding in PfENR-TCLx complexes present during MD simulations: HB (green); ionic interactions (magenta); hydrophobic contacts (purple); water bridges (blue).

**Figure 12.**2D representation of the most populated attractive interactions between the function groups of the six inhibitors and the individual residues at the active site of PfENR that occur in at least in 1/5 of the 500 analysed frames.

**Figure 13.**Superimposition of the ligand active conformation from complexes refined by molecular mechanics (yellow carbons, red oxygens, blue nitrogens) and averaged active conformations resulting from MD simulations (purple carbon atoms).

**Scheme 1.**Workflow describing the sequence of steps taken to virtually design novel TCL analogues with higher predicted potencies against PfENR.

Training Set (TS) | ${\mathbf{R}}_{1}$ | ${\mathbf{R}}_{2}$ | ${\mathbf{R}}_{3}$ | ${\mathbf{I}\mathbf{C}}_{50}^{\mathbf{e}\mathbf{x}\mathbf{p}}$ [nM] |
---|---|---|---|---|

TCL1 (Triclosan) | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 73 |

TCL2 | $\cdots {\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 200 |

TCL3 | ${\cdots \mathrm{C}\mathrm{H}}_{2}\mathrm{C}\mathrm{y}\mathrm{c}\mathrm{H}\mathrm{e}\mathrm{x}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 530 |

TCL4 | $\cdots {\left({\mathrm{C}\mathrm{H}}_{2}\right)}_{3}{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 480 |

TCL5 | $\cdots {\mathrm{C}\mathrm{H}}_{2}\mathrm{C}\mathrm{H}{\left({\mathrm{C}\mathrm{H}}_{3}\right)}_{2}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 180 |

TCL6 | $\cdots {\left({\mathrm{C}\mathrm{H}}_{2}\right)}_{2}\mathrm{C}\mathrm{H}{\left({\mathrm{C}\mathrm{H}}_{3}\right)}_{2}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 120 |

TCL7 | $\cdots {\mathrm{C}\mathrm{H}}_{2}\mathrm{C}\mathrm{H}\left({\mathrm{C}\mathrm{H}}_{3}\right){\mathrm{C}\mathrm{H}}_{2}{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 290 |

TCL8 | $\cdots {\mathrm{C}\mathrm{H}}_{2}-2-\mathrm{P}\mathrm{y}\mathrm{r}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 640 |

TCL9 | $\cdots {\mathrm{C}\mathrm{H}}_{2}-4-\mathrm{P}\mathrm{y}\mathrm{r}$ | $\cdots \mathrm{C}\mathrm{N}$ | $\cdots \mathrm{C}\mathrm{l}$ | 530 |

TCL10 | $\cdots {\mathrm{C}\mathrm{H}}_{2}-\mathrm{P}\mathrm{h}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 71 |

TCL11 | $\cdots {\left({\mathrm{C}\mathrm{H}}_{2}\right)}_{2}-\mathrm{P}\mathrm{h}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 76 |

TCL12 | $\cdots \mathrm{P}\mathrm{h}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 140 |

TCL13 | $\cdots \mathrm{P}\mathrm{h}-o{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 440 |

TCL14 | $\cdots \mathrm{P}\mathrm{h}-o{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{N}$ | $\cdots \mathrm{C}\mathrm{l}$ | 410 |

TCL15 | $\cdots \mathrm{P}\mathrm{h}-p\mathrm{F}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 38 |

TCL16 | $\cdots \mathrm{C}\mathrm{N}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 49 |

TCL17 | $\cdots \mathrm{P}\mathrm{h}-p{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 190 |

TCL18 | … $\mathrm{C}\mathrm{l}$ | $\cdots \mathrm{H}$ | …NH_{2} | 7000 |

TCL19 | …2H-tetrazol-5-yl | … Cl | … Cl | 100,000 |

TCL20 | … 3-pyridyl | …Cl | …Cl | 33,000 |

Validation Set (VS) | ${\mathrm{R}}_{1}$ | ${\mathrm{R}}_{2}$ | ${\mathrm{R}}_{3}$ | ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ [nM] |

TCL21 | $\cdots {\left({\mathrm{C}\mathrm{H}}_{2}\right)}_{2}{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 210 |

TCL22 | $\cdots {\mathrm{C}\mathrm{H}}_{2}{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 110 |

TCL23 | $\cdots {\mathrm{C}\mathrm{H}}_{2}-\left(3-\mathrm{P}\mathrm{y}\mathrm{r}\right)$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 840 |

TCL24 | $\cdots \mathrm{P}\mathrm{h}-m{\mathrm{C}\mathrm{H}}_{3}$ | $\cdots \mathrm{C}\mathrm{l}$ | $\cdots \mathrm{C}\mathrm{l}$ | 230 |

**Table 2.**Relative complexation Gibbs free energy (binding affinity, rGFE) and its components for the training set of the PfENR inhibitors

**TCL1–20**and the validation set inhibitors

**TCL21–24**.

Training Set ^{a} | ${\u2206\u2206\mathbf{H}}_{\mathbf{M}\mathbf{M}}$^{b}${[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\u2206\u2206\mathbf{G}}_{\mathbf{s}\mathbf{o}\mathbf{l}}$^{c}${[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\u2206\u2206\mathbf{T}\mathbf{S}}_{\mathbf{v}\mathbf{i}\mathbf{b}}$^{d}${[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\u2206\u2206\mathbf{G}}_{\mathbf{c}\mathbf{o}\mathbf{m}}$^{e}${[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\mathbf{I}\mathbf{C}}_{50}^{\mathbf{e}\mathbf{x}\mathbf{p}}$^{f, g}$[\mathbf{n}\mathbf{M}]$ |
---|---|---|---|---|---|

TCL1 | 0 | 0 | 0 | 0 | 73 |

TCL2 | 8.88 | −2.30 | 2.06 | 4.52 | 200 |

TCL3 | 15.58 | −0.16 | 5.38 | 10.04 | 530 |

TCL4 | 14.87 | −2.65 | 3.90 | 8.31 | 480 |

TCL5 | 11.16 | −2.60 | 4.16 | 4.40 | 180 |

TCL6 | 6.35 | −0.94 | 4.90 | 0.51 | 120 |

TCL7 | 13.45 | −2.34 | 4.53 | 6.58 | 290 |

TCL8 | 8.68 | 0.12 | 0.62 | 8.17 | 640 |

TCL9 | 8.21 | −0.20 | 2.86 | 5.15 | 530 |

TCL10 | 0.11 | 0.62 | 2.38 | −1.65 | 71 |

TCL11 | 2.71 | 0.19 | 2.25 | 0.66 | 76 |

TCL12 | 4.01 | 0.03 | 2.99 | 1.04 | 140 |

TCL13 | 11.64 | −1.93 | 1.69 | 8.02 | 440 |

TCL14 | 12.08 | −1.21 | 4.18 | 6.69 | 410 |

TCL15 | −0.78 | −0.73 | 1.81 | −3.33 | 38 |

TCL16 | −1.38 | −1.61 | 0.46 | −3.44 | 49 |

TCL17 | 2.97 | −0.45 | −0.04 | 2.56 | 190 |

TCL18 | 31.80 | 6.56 | 2.24 | 36.13 | 7 000 |

TCL19 | 55.03 | −0.34 | 1.58 | 53.11 | 100,000 |

TCL20 | 37.93 | 12.97 | 3.3 | 47.61 | 33,000 |

Validation Set ^{a} | ${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}$^{b}${[\mathrm{k}\mathrm{c}\mathrm{a}\mathrm{l}\xb7\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}]$ | ${\u2206\u2206\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}$^{c}${[\mathrm{k}\mathrm{c}\mathrm{a}\mathrm{l}\xb7\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}]$ | ${\u2206\u2206\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}$^{d}$[{\mathrm{k}\mathrm{c}\mathrm{a}\mathrm{l}\xb7\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}]$ | ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$^{e}${[\mathrm{k}\mathrm{c}\mathrm{a}\mathrm{l}\xb7\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}]$ | ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}/{p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$^{f} |

TCL21 | 10.47 | −1.95 | 3.94 | 4.75 | 1.00 |

TCL22 | 7.53 | −1.41 | 3.80 | 2.32 | 1.02 |

TCL23 | 7.51 | 0.89 | 1.14 | 7.26 | 0.93 |

TCL24 | 7.17 | −1.19 | 2.29 | 3.70 | 0.99 |

^{a}for chemical structures of the triclosan analogues of TS and VS, see Table 1;

^{b}${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}$ is the relative enthalpic contribution to the change in rGFE in the formation of the enzyme-inhibitor E:I complex formation derived from molecular mechanics (MM): ${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}\cong \left[{\mathrm{E}}_{\mathrm{M}\mathrm{M}}\left\{\mathrm{E}:{\mathrm{I}}_{\mathrm{x}}\right\}-{\mathrm{E}}_{\mathrm{M}\mathrm{M}}\left\{{\mathrm{I}}_{\mathrm{x}}\right\}\right]-\left[{\mathrm{E}}_{\mathrm{M}\mathrm{M}}\left\{\mathrm{E}:{\mathrm{I}}_{\mathrm{r}\mathrm{e}\mathrm{f}}\right\}-{\mathrm{E}}_{\mathrm{M}\mathrm{M}}\left\{{\mathrm{I}}_{\mathrm{r}\mathrm{e}\mathrm{f}}\right\}\right]$, ${\mathrm{I}}_{\mathrm{r}\mathrm{e}\mathrm{f}}$ is the reference inhibitor

**TCL1**;

^{c}${\u2206\u2206\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}$ is the solvent effect contribution to the rGFE change of the E:I complex formation: ${\u2206\u2206\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}=\left[{\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}\left\{\mathrm{E}:{\mathrm{I}}_{\mathrm{x}}\right\}-{\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}\left\{{\mathrm{I}}_{\mathrm{x}}\right\}\right]-\left[{\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}\left\{\mathrm{E}:{\mathrm{I}}_{\mathrm{r}\mathrm{e}\mathrm{f}}\right\}-{\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}\left\{{\mathrm{I}}_{\mathrm{r}\mathrm{e}\mathrm{f}}\right\}\right]$;

^{d}${-\u2206\u2206\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}$ is the relative entropic contribution to the rGFE of E:I complex formation: ${\u2206\u2206\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}=\left[{\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}{\left\{{\mathrm{I}}_{\mathrm{x}}\right\}}_{\mathrm{E}}-{\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}\left\{{\mathrm{I}}_{\mathrm{x}}\right\}\right]-\left[{\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}{\left\{{\mathrm{I}}_{\mathrm{r}\mathrm{e}\mathrm{f}}\right\}}_{\mathrm{E}}-{\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}\left\{{\mathrm{I}}_{\mathrm{r}\mathrm{e}\mathrm{f}}\right\}\right]$;

^{e}${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$ is the rGFE change of E:I complex formation: ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}\cong {\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}+{\u2206\u2206\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}-{\u2206\u2206\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}$.

^{f}${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ is the experimental half-maximal inhibitory concentration of TCLs against PfENR obtained from the literature [22,24,26,28].

^{g}The ratio of predicted and experimental half-maximal inhibitory concentrations ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}/{p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$. ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}=-{\mathit{log}}_{10}{\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ was predicted from the computed ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$ using the regression Equation (2) for PfENR shown in Table 3.

**Table 3.**Regression analysis of computed binding affinities ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$, its enthalpic component ${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}$, and experimental half-maximal inhibitory concentrations ${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=-{\mathit{log}}_{10}{\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ of TCLs towards the PfENR [21,23,25,27].

Statistical Data of Regression | $\left(1\right)$ | $\left(2\right)$ |
---|---|---|

${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=-0.0614\times {\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}+7.1496\hspace{1em}\hspace{1em}\left(1\right)$ | ||

${p\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}=-0.0544\times {\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}+6.9336\hspace{1em}\hspace{1em}\left(2\right)$ | ||

Number of compounds in TS | 20 | 20 |

Squared correlation coefficient of regression ${\mathrm{R}}^{2}$ | 0.94 | 0.95 |

Cross-validated squared correlation coefficient ${\mathrm{R}}_{\mathrm{x}\mathrm{v}}^{2}$ | 0.92 | 0.93 |

Standard error of regression σ | 0.21 | 0.18 |

Statistical significance of regression, Fisher F-test | 327.3 | 426.16 |

Level of statistical significance α | >95% | >95% |

Range of experimental activities ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ [nM] | 38–100,000 |

**Table 4.**Parameters of 10 generated PH4 pharmacophoric hypotheses for the PfENR inhibitor after the CatScramble validation procedure (49 scrambled runs for each hypothesis at the selected confidence level of 98%).

Hypothesis | RMSD ^{a} | ${\mathbf{R}}^{2}$^{b} | Total Cost ^{c} | Costs Difference ^{d} | Closest Random ^{e} | Features ^{f} |
---|---|---|---|---|---|---|

Hypo1 | 6.565 | 0.96 | 409.3 | 5013.9 | 1021.7 | HBA, HYD-AL, HYD-AL, HYD-Ar, HYD |

Hypo2 | 9.310 | 0.93 | 803.5 | 4619.7 | 1035.5 | HBA, HYD-AL, HYD-AL, HYD, HYD |

Hypo3 | 9.428 | 0.92 | 822.7 | 4600.5 | 1058.9 | HBA, HYD-AL, HYD-Ar, HYD, HYD |

Hypo4 | 9.587 | 0.92 | 850.3 | 4572.9 | 1068.1 | HBA, HYD-AL, HYD, HYD, HYD |

Hypo5 | 10.776 | 0.90 | 1069.8 | 4353.4 | 1076.6 | HYD-AL, HYD-AL, HYD-Ar, HYD-Ar, HYD |

Hypo6 | 10.780 | 0.90 | 1071.3 | 4352.0 | 1079.7 | HYD-AL, HYD-AL, HYD-Ar, HY, HY |

Hypo7 | 18.776 | 0.64 | 3195.9 | 2227.3 | 1088.2 | HYD-AL, HYD-Ar, HYD-Ar, HY, HY |

Hypo8 | 11.420 | 0.89 | 1195.3 | 4227.9 | 1090.9 | HBA, HYD-AL, HY, HYD, HYD |

Hypo9 | 18.835 | 0.64 | 3216.9 | 2206.4 | 1099.1 | HYD-AL, HYD-Ar, HYD, HYD, HYD |

Hypo10 | 11.583 | 0.88 | 1231.8 | 4191.4 | 1102.3 | HBA, HYD-AL, HYD-Ar, HYD, HYD |

^{a}Root Mean Square Deviation;

^{b}squared correlation coefficient;

^{c}overall cost parameter of the PH4 pharmacophore;

^{d}cost difference between Null cost and total cost of this hypothesis;

^{e}lowest cost of 49 scrambled runs at a selected level of confidence of 98%. Fixed Cost = 15.2 with RMSD = 0, Null Cost = 5423.2 with RMSD = 24.53 and Configuration cost = 9.55.

^{f}HBA (hydrogen-bond Acceptor); HYD (Hydrophobic); HYD-AL (Hydrophobic Aliphatic); HYD-Ar (Hydrophobic Aromatic).

**Table 5.**${\mathrm{R}}_{1}$, ${\mathrm{R}}_{2}$ and ${\mathrm{R}}_{3}$ groups used in the design of the initial diversity library of TCL analogues.

R-Groups | |||||
---|---|---|---|---|---|

1 | 2 | 3 | |||

4 | 5 | 6 | |||

7 | 8 | 9 | |||

10 | 11 | 12 | |||

13 | 14 | 15 | |||

16 | 17 | 18 | |||

19 | 20 | 21 | |||

22 | 23 | 24 | |||

25 | 26 | 27 | |||

28 | 29 | 30 | |||

31 | 32 | 33 | |||

34 | 35 | 36 | |||

37 | 38 | 39 | |||

40 | 41 | 42 | |||

43 | 44 | 45 | |||

46 | 47 | 48 | |||

49 | 50 | 51 | |||

52 | 53 | 54 | |||

55 | 56 | 57 | |||

58 | 59 | 60 |

**1**–

**60**; ${\mathrm{R}}_{2}$-groups: fragments

**1**–

**23**;

**41**–

**48**. ${\mathrm{R}}_{3}$-groups: fragments

**1**–

**11**;

**41**–

**47**. Dashed bonds indicate the attachment point of the fragment.

**Table 6.**Relative GFE calculated and its components for 20 best analogues of TCLs identified by in silico screening of the diversity combinatorial library. The numbering of the analogues concatenates the index of each substituent ${\mathrm{R}}_{1}-{\mathrm{R}}_{2}-{\mathrm{R}}_{3}$ with the substituent numbers taken from Table 5.

No. | TCL Analogue | ${\u2206\u2206\mathbf{H}}_{\mathbf{M}\mathbf{M}}$^{a}${[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\u2206\u2206\mathbf{G}}_{\mathbf{s}\mathbf{o}\mathbf{l}}$^{b}${[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\u2206\u2206\mathbf{T}\mathbf{S}}_{\mathbf{v}\mathbf{i}\mathbf{b}}$^{c}${[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\u2206\u2206\mathbf{G}}_{\mathbf{c}\mathbf{o}\mathbf{m}}$^{d}$[{\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\mathbf{p}\mathbf{I}\mathbf{C}}_{50}^{\mathbf{p}\mathbf{r}\mathbf{e}}$^{e} | ${\mathbf{I}\mathbf{C}}_{50}^{\mathbf{p}\mathbf{r}\mathbf{e}}$^{f}[nM] |
---|---|---|---|---|---|---|---|

Ref. | TCL1TCL-03-03-03 | 0 | 0 | 0 | 0 | 7.14 * | 73 * |

1 | TCL-58-01-01 | −17.8 | 8.1 | 7.6 | −17.3 | 7.87 | 13.4 |

2 | TCL-59-01-01 | −20.6 | 8.1 | 6.5 | −19.0 | 7.97 | 10.8 |

3 | TCL-60-01-01 | −21.8 | 8.9 | 7.5 | −20.4 | 8.04 | 9.1 |

8 | TCL-26-16-01 | −20.5 | 9.4 | 6.5 | −17.6 | 7.89 | 12.8 |

47 | TCL-33-08-05 | −18.9 | 0.4 | 8.3 | −26.7 | 8.39 | 4.1 |

51 | TCL-59-08-05 | −13.8 | 3.0 | 6.5 | −17.3 | 7.87 | 13.4 |

52 | TCL-60-08-05 | −15.2 | 2.6 | 6.2 | −18.8 | 7.96 | 11.1 |

85 | TCL-60-08-08 | −49.9 | 25.8 | 9.0 | −33.0 | 8.73 | 1.9 |

91 | TCL-33-16-08 | −15.45 | 2.28 | 12.14 | −25.3 | 8.31 | 4.9 |

92 | TCL-58-16-08 | −11.3 | 3.2 | 12.2 | −20.3 | 8.04 | 9.2 |

94 | TCL-60-16-08 | −14.8 | 7.3 | 12.1 | −19.6 | 8.00 | 10.1 |

112 | TCL-55-19-09 | 0.8 | −2.7 | 14.2 | −16.1 | 7.81 | 15.5 |

118 | TCL-58-23-09 | −29.2 | 11.2 | 11.2 | −29.2 | 8.52 | 3.1 |

119 | TCL-59-23-09 | −11.6 | 6.1 | 12.0 | −17.4 | 7.88 | 13.2 |

135 | TCL-36-23-10 | −17.3 | 5.1 | 6.2 | −18.4 | 7.93 | 11.6 |

154 | TCL-56-17-11 | −12.5 | 4.5 | 11.7 | −19.6 | 8.00 | 10.1 |

155 | TCL-35-20-11 | −4.3 | −1.7 | 11.6 | −17.7 | 7.90 | 12.7 |

191 | TCL-32-08-45 | −17.1 | 3.5 | 4.3 | −18.0 | 7.91 | 12.2 |

200 | TCL-56-16-46 | −14.0 | 4.2 | 7.6 | −17.4 | 7.88 | 13.2 |

205 | TCL-56-23-46 | −21.7 | 6.1 | 7.1 | −22.7 | 8.17 | 6.8 |

^{a}${\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}$ is the relative enthalpic contribution to the GFE change ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$ of the PfENR-TCLx complex formation (for details, see the footnote in Table 2);

^{b}${\u2206\u2206\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}$ is the relative solvation contribution to ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$;

^{c}${\u2206\u2206\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}$ is the relative entropic (vibrational) contribution to ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$;

^{d}${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$ is the relative GFE change of the PfENR-TCLx complex formation ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}={\u2206\u2206\mathrm{H}}_{\mathrm{M}\mathrm{M}}+{\u2206\u2206\mathrm{G}}_{\mathrm{s}\mathrm{o}\mathrm{l}}-{\u2206\u2206\mathrm{T}\mathrm{S}}_{\mathrm{v}\mathrm{i}\mathrm{b}}$;

^{e}$p{\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ is the logarithm of predicted inhibition potency towards PfENR calculated from ${\u2206\u2206\mathrm{G}}_{\mathrm{c}\mathrm{o}\mathrm{m}}$ using correlation Equation (2), Table 3;

^{f}${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ is the predicted inhibition potency toward PfENR. * Experimental values of $\mathrm{p}{\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ and ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ are given for the reference inhibitor

**TCL1**instead of the predicted values.

**Table 7.**ADME-related properties of virtual TCL hits and other reference antimalarial agents computed by QikProp [39].

Analogues ^{a} | # Star ^{b} | ${\mathbf{M}}_{\mathbf{W}}$ | S_{mol} ^{d} | S_{mol, hfo} ^{e} | V_{m} ^{f} | RotB ^{g} | HBD ^{h} | HBA ^{i} | logP_{o/w} ^{j} | logS_{wat} ^{k} | logKHSA ^{l} | logB/B ^{m} | BIP_{caco} ^{n} | # Meta ^{o} | ${\mathbf{I}\mathbf{C}}_{50}^{\mathbf{p}\mathbf{r}\mathbf{e}}$PfENR [nM] | HOA ^{q} | %HOA ^{r} |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

58-01-01 | 1 | 270 | 592.4 | 259.8 | 1009.0 | 8 | 1.0 | 1.3 | 5.0 | −5.2 | 0.8 | −0.5 | 2933.7 | 2 | 13.3 | 3 | 100 |

59-01-01 | 1 | 284 | 622.3 | 291.2 | 1070.5 | 9 | 1.0 | 1.3 | 5.4 | −5.7 | 0.9 | −0.5 | 2936.4 | 2 | 10.8 | 3 | 100 |

60-01-01 | 1 | 298 | 654.7 | 322.1 | 1126.8 | 10 | 1.0 | 1.3 | 5.8 | −6.1 | 1.0 | −0.6 | 2933.7 | 2 | 9.1 | 3 | 100 |

26-16-01 | 0 | 337 | 652.3 | 181.2 | 1130.1 | 6 | 1.0 | 3.3 | 4.7 | −5.0 | 0.8 | 0.2 | 759.3 | 3 | 12.9 | 3 | 100 |

29-16-01 | 0 | 320 | 635.4 | 181.3 | 1099.0 | 6 | 1.0 | 4.8 | 3.5 | −3.9 | 0.4 | −0.2 | 416.7 | 3 | 22.0 | 3 | 94 |

02-21-01 | 1 | 274 | 557.6 | 218.1 | 953.5 | 6 | 1.0 | 1.3 | 4.8 | −5.2 | 0.7 | −0.2 | 3200.1 | 2 | 20.2 | 3 | 100 |

05-21-01 | 0 | 272 | 562.4 | 218.0 | 961.2 | 7 | 2.0 | 2.0 | 4.7 | −4.3 | 0.4 | −0.9 | 965.7 | 3 | 18.5 | 3 | 100 |

55-23-01 | 1 | 411 | 787.1 | 107.3 | 1375.0 | 10 | 3.0 | 3.5 | 5.3 | −6.0 | 1.0 | −1.1 | 168.6 | 5 | 38.2 | 2 | 85 |

56-04-02 | 0 | 353 | 674.0 | 194.7 | 1166.8 | 8 | 2.0 | 5.8 | 3.2 | −4.1 | 0.3 | −0.7 | 181.1 | 6 | 73.3 | 3 | 86 |

40-23-02 | 3 | 448 | 809.8 | 106.3 | 1416.7 | 9 | 2.0 | 2.8 | 6.9 | −7.6 | 1.5 | −0.1 | 562.2 | 4 | 32.1 | 1 | 100 |

55-45-02 | 0 | 383 | 704.6 | 199.7 | 1228.9 | 10 | 3.0 | 4.3 | 4.0 | −4.5 | 0.5 | −0.8 | 183.5 | 6 | 35.5 | 3 | 91 |

34-14-03 | 0 | 341 | 629.0 | 88.1 | 1078.7 | 7 | 3.0 | 3.8 | 3.2 | −3.9 | 0.3 | −0.7 | 115.8 | 6 | 67.2 | 3 | 83 |

38-10-04 | 0 | 390 | 735.5 | 353.2 | 1292.5 | 8 | 3.0 | 4.5 | 4.3 | −5.1 | 0.7 | −0.5 | 333.5 | 8 | 67.2 | 3 | 100 |

57-20-04 | 0 | 392 | 776.1 | 391.3 | 1373.8 | 10 | 2.0 | 5.3 | 4.6 | −5.5 | 0.8 | −0.8 | 274.6 | 8 | 30.5 | 3 | 100 |

33-08-05 | 0 | 313 | 607.7 | 243.6 | 1060.9 | 7 | 3.5 | 3.0 | 3.2 | −4.5 | 0.4 | −1.5 | 265.6 | 4 | 4.1 | 3 | 89 |

58-08-05 | 0 | 301 | 624.3 | 259.8 | 1066.5 | 10 | 3.5 | 3.0 | 3.2 | −4.3 | 0.2 | −1.8 | 236.0 | 4 | 19.4 | 3 | 88 |

59-08-05 | 0 | 315 | 656.9 | 291.5 | 1126.4 | 11 | 3.5 | 3.0 | 3.6 | −4.8 | 0.3 | −1.9 | 244.3 | 4 | 13.3 | 3 | 90 |

60-08-05 | 0 | 329 | 686.6 | 322.1 | 1184.3 | 12 | 3.5 | 3.0 | 3.9 | −5.1 | 0.5 | −2.0 | 236.0 | 4 | 11.1 | 3 | 92 |

37-20-06 | 0 | 394 | 758.8 | 256.5 | 1334.3 | 10 | 2.8 | 3.3 | 5.6 | −6.0 | 1.0 | −0.2 | 560.6 | 6 | 51.6 | 3 | 96 |

49-16-07 | 0 | 423 | 708.2 | 180.3 | 1231.8 | 7 | 1.0 | 4.8 | 4.4 | −6.2 | 0.7 | −0.6 | 184.3 | 3 | 26.2 | 3 | 93 |

32-19-07 | 1 | 357 | 711.7 | 227.3 | 1248.1 | 8 | 1.0 | 2.8 | 5.6 | −8.0 | 1.2 | −1.2 | 729.9 | 3 | 41.7 | 1 | 100 |

58-08-08 | 0 | 302 | 628.7 | 259.8 | 1074.4 | 10 | 4 | 3.5 | 3.0 | −4.2 | 0.2 | −1.9 | 202.2 | 5 | 89.6 | 3 | 85 |

59-08-08 | 0 | 316 | 692.1 | 310.1 | 1164.7 | 11 | 4 | 3.5 | 3.6 | −5.2 | 0.3 | −2.0 | 223.4 | 5 | 40.2 | 3 | 89 |

60-08-08 | 0 | 330 | 690.1 | 322.1 | 1190.5 | 12 | 4 | 3.5 | 3.7 | −5.0 | 0.4 | −2.1 | 206.6 | 5 | 1.9 | 3 | 90 |

58-16-08 | 0 | 343 | 702.5 | 426.6 | 1230.8 | 11 | 2.5 | 4.3 | 3.7 | −4.2 | 0.5 | −1.0 | 169.6 | 6 | 9.2 | 3 | 88 |

59-16-08 | 0 | 357 | 756.6 | 479.9 | 1321.3 | 12 | 2.5 | 4.3 | 4.4 | −5.0 | 0.7 | −1.0 | 219.6 | 6 | 22.6 | 3 | 94 |

60-16-08 | 0 | 371 | 763.8 | 488.9 | 1347.0 | 13 | 2.5 | 4.3 | 4.5 | −5.0 | 0.8 | −1.2 | 173.2 | 6 | 10.0 | 3 | 93 |

40-13-09 | 0 | 428 | 777.9 | 268.0 | 1377.1 | 11 | 2.8 | 3.3 | 6.1 | −6.5 | 1.1 | −0.1 | 616.6 | 7 | 38.2 | 1 | 100 |

54-20-09 | 3 | 422 | 818.2 | 402.0 | 1465.9 | 11 | 2.0 | 2.8 | 6.7 | −7.2 | 1.5 | −0.3 | 606.3 | 6 | 16.9 | 1 | 100 |

32-17-10 | 0 | 339 | 669.4 | 453.1 | 1201.7 | 6 | 2.0 | 2.3 | 5.3 | −6.4 | 1.1 | −0.6 | 1787.7 | 6 | 57.1 | 1 | 100 |

07-22-10 | 0 | 310 | 650.9 | 307.0 | 1111.8 | 8 | 2.0 | 3.8 | 3.7 | −6.3 | 0.5 | −1.5 | 410.8 | 5 | 22.3 | 1 | 95 |

58-23-09 | 4 | 374 | 739.2 | 355.2 | 1326.4 | 10 | 1 | 1.3 | 7.2 | −7.7 | 1.6 | −0.5 | 3666.3 | 3 | 3.0 | 1 | 100 |

59-23-09 | 4 | 388 | 754.3 | 380.4 | 1382.8 | 11 | 1 | 1.3 | 7.6 | −7.8 | 1.7 | −0.6 | 3622.7 | 3 | 13.2 | 1 | 100 |

60-23-09 | 4 | 402 | 825.5 | 442.5 | 1470.6 | 12 | 1 | 1.3 | 8.1 | −9.0 | 1.9 | −0.8 | 3267.4 | 3 | 21.0 | 1 | 100 |

46-23-10 | 0 | 333 | 647.9 | 150.2 | 1118.0 | 7 | 2.0 | 4.3 | 3.7 | −5.3 | 0.5 | −1.4 | 407.7 | 4 | 50.3 | 3 | 96 |

58-23-10 | 1 | 375 | 754.9 | 338.5 | 1332.6 | 10 | 2 | 2.3 | 6.3 | −7.3 | 1.3 | −0.9 | 1715.4 | 4 | 45.0 | 1 | 100 |

59-23-10 | 2 | 389 | 781.0 | 368.7 | 1386.8 | 11 | 2 | 2.3 | 6.8 | −7.7 | 1.4 | −0.8 | 2514.6 | 4 | 32.9 | 1 | 100 |

60-23-10 | 3 | 403 | 824.9 | 407.6 | 1459.3 | 12 | 2 | 2.3 | 7.2 | −8.3 | 1.5 | −1.1 | 1825.5 | 4 | 22.9 | 1 | 100 |

40-09-11 | 0 | 391 | 782.8 | 365.6 | 1382.3 | 10 | 2.0 | 3.8 | 5.6 | −6.1 | 1.1 | −0.5 | 520.7 | 7 | 45.5 | 3 | 95 |

37-15-41 | 0 | 427 | 735.0 | 193.1 | 1275.9 | 9 | 3.0 | 4.3 | 4.1 | −4.1 | 0.7 | 0.0 | 86.9 | 6 | 52.3 | 3 | 86 |

27-16-41 | 0 | 433 | 703.5 | 183.0 | 1218.1 | 6 | 1.0 | 3.3 | 5.6 | −6.4 | 1.1 | 0.4 | 740.0 | 3 | 66.3 | 1 | 100 |

32-08-45 | 0 | 321 | 605.6 | 114.4 | 1055.1 | 7 | 2.5 | 3.0 | 4.0 | −4.8 | 0.5 | −0.9 | 879.5 | 4 | 12.2 | 3 | 100 |

25-07-46 | 1 | 343 | 668.1 | 143.9 | 1151.1 | 7 | 1.0 | 4.8 | 3.5 | −6.6 | 0.5 | −1.9 | 136.3 | 3 | 16.5 | 1 | 86 |

28-13-47 | 0 | 373 | 656.3 | 93.6 | 1135.4 | 7 | 2.8 | 2.5 | 5.1 | −6.3 | 0.7 | −0.6 | 994.2 | 5 | 13.0 | 1 | 100 |

Chloroquine | 1 | 294 | 594.1 | 188.9 | 982.9 | 6 | 0.0 | 3.0 | 4.6 | −5.3 | 0.4 | −0.1 | 3718.1 | 0 | - | 3 | 100 |

Amodiaquine | 1 | 334 | 603.2 | 131.7 | 1018.7 | 6 | 0.0 | 5.0 | 3.6 | −4.4 | 0.0 | −0.4 | 1689.1 | 0 | - | 3 | 100 |

Dapsone | 1 | 236 | 431.6 | 0.0 | 687.9 | 2 | 0.0 | 7.0 | −0.4 | −0.5 | −1.3 | −0.9 | 289.1 | 0 | - | 3 | 69 |

Trimethoprim | 0 | 272 | 500.2 | 223.9 | 835.9 | 5 | 0.0 | 6.5 | 0.6 | −1.5 | −0.9 | −1.2 | 282.8 | 3 | - | 3 | 74 |

Mefloquine | 2 | 362 | 533.1 | 0.0 | 925.1 | 2 | 0.0 | 4.0 | 4.1 | −4.9 | 0.2 | 0.5 | 2903.1 | 0 | - | 3 | 100 |

Pamaquine | 0 | 315 | 654.8 | 443.4 | 1148.1 | 9 | 1.0 | 4.8 | 4.0 | −3.8 | 0.4 | 0.2 | 1475.2 | 5 | - | 3 | 100 |

Sulfametopyrazine | 1 | 268 | 473.4 | 77.8 | 773.3 | 4 | 0.0 | 9.0 | −1.0 | 0.2 | −1.7 | −1.3 | 195.8 | 1 | - | 2 | 62 |

Quinacrine | 0 | 370 | 680.5 | 268.8 | 1163.6 | 7 | 0.0 | 3.5 | 5.6 | −6.5 | 0.8 | −0.1 | 4435.7 | 1 | - | 1 | 100 |

Tetracycline | 5 | 422 | 604.5 | 173.1 | 1111.8 | 2 | 0.0 | 7.0 | −3.4 | 1.1 | −2.5 | −2.6 | 6.8 | 5 | - | 1 | 22 |

Lumefantrine | 5 | 497 | 819.1 | 160.7 | 1437.5 | 7 | 0.0 | 3.0 | 8.3 | −10.0 | 1.7 | 0.2 | 4337.2 | 0 | - | 1 | 100 |

Bulaquine | 0 | 369 | 560.2 | 360.2 | 1097.8 | 9 | 1.0 | 5.8 | 3.6 | −3.0 | 0.1 | −0.4 | 3099.7 | 7 | - | 3 | 100 |

Hydroxychloroquine | 1 | 310 | 609.5 | 119.5 | 1006.5 | 6 | 0.0 | 5.0 | 3.4 | −4.5 | −0.1 | −0.7 | 1023.7 | 0 | - | 3 | 100 |

Sulfadoxine | 1 | 296 | 510.6 | 152.3 | 849.5 | 5 | 0.0 | 9.5 | −0.8 | −0.1 | −1.7 | −1.4 | 213.4 | 2 | - | 2 | 64 |

Halofantrine | 5 | 470 | 785.4 | 160.2 | 1351.8 | 5 | 0.0 | 3.0 | 7.6 | −9.9 | 1.5 | 0.2 | 2844.1 | 0 | - | 1 | 100 |

Proguanil | 1 | 238 | 478.2 | 125.3 | 768.6 | 6 | 0.0 | 6.0 | 1.1 | −1.5 | −1.1 | −0.7 | 834.6 | 0 | - | 3 | 86 |

Doxycycline | 4 | 422 | 602.2 | 174.1 | 1104.2 | 2 | 0.0 | 17.2 | −4.0 | 1.7 | −2.9 | −2.5 | 9.2 | 4 | - | 1 | 21 |

Artemether | 1 | 312 | 531.1 | 506.1 | 970.2 | 2 | 0.0 | 5.7 | 2.7 | −2.99 | −0.20 | 0.20 | 5731.8 | 0 | - | 3 | 100 |

Dihydroartemisinine | 1 | 284 | 477.4 | 395.7 | 864.6 | 1 | 1.0 | 5.7 | 1.8 | −2.92 | −0.10 | −0.10 | 1664.9 | 0 | - | 3 | 95 |

Artemisinin | 0 | 282 | 456.6 | 380.6 | 848.4 | 0 | 0.0 | 5.3 | 1.7 | −2.10 | −0.30 | 0.00 | 1886.0 | 1 | - | 3 | 96 |

^{a}designed TCL analogues (Table 6) and known antimalarials;

^{b}drug-likeness, number of property descriptors (24 of the full list of 49 QikProp descriptors, ver. 6.5, release 139) that fall outside the range of values for 95% of known drugs;

^{c}molar mass in [g·mol

^{−1}] (range for 95% of drugs: 130–725 g·mol

^{−1}) [39];

^{d}total solvent-accessible molecular surface, in (Å

^{2}) (probe radius 1.4 Å) (range for 95% of drugs: 300–1000 Å

^{2});

^{e}hydrophobic portion of the solvent-accessible molecular surface, in (Å

^{2}) (range for 95% of drugs: 0–750 Å

^{2});

^{f}total volume of the molecule enclosed by solvent-accessible molecular surface, in (Å

^{3}) (range for 95% of drugs: 500–2000 Å

^{3});

^{g}number of rotatable non-trivial (not CX3), non-hindered (not alkene, amide, small ring) bonds (range for 95% of drugs: 0–15);

^{h}estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution. Values are averages taken over a number of configurations, so they can assume non-integer values (range for 95% of drugs: 0.0–6.0);

^{i}estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution (range for 95% of drugs: 2.0–20.0);

^{j}logarithm of the partition coefficient between the n-octanol and water phases (range for 95% of drugs: −2 to 6.5);

^{k}logarithm of predicted aqueous solubility, logS. S in (mol·dm

^{–3}) is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid (range for 95% of drugs: −6.0 to 0.5);

^{l}logarithm of the predicted binding constant to human serum albumin (range for 95% of drugs: −1.5 to 1.5);

^{m}logarithm of the predicted brain/blood partition coefficient (range for 95% of drugs: −3.0 to 1.2);

^{n}predicted apparent Caco-2 cell membrane permeability on the Boehringer–Ingelheim scale in [nm·s

^{−1}] (range for 95% of drugs: <25 poor, >500 nm·s

^{−1}great);

^{o}number of likely metabolic reactions (range for 95% of drugs: 1–8);

^{p}predicted inhibition constants ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ of designed TCLs vs. PfENR.;

^{q}human oral absorption (1 = low, 2 = medium, 3 = high);

^{r}percentage of human oral absorption in the gastrointestinal tract (<25% = poor, >80% = high); * star in any column indicates that the property descriptor value of the compound falls outside the range of values for 95% of known drugs.

**Table 8.**Ensemble averages of the total and potential energies of complexes PfENR-TCLx for

**TCL11**and selected virtual hits.

Inhibitor | Structure | $\u2329{\mathbf{E}}_{\mathbf{t}\mathbf{o}\mathbf{t}}\u232a$^{a}$[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7{\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | $\u2329{\mathbf{E}}_{\mathbf{p}\mathbf{o}\mathbf{t}}\u232a$^{b}$[\mathbf{k}\mathbf{c}\mathbf{a}\mathbf{l}\xb7{\mathbf{m}\mathbf{o}\mathbf{l}}^{-1}]$ | ${\mathbf{I}\mathbf{C}}_{50}^{\mathbf{p}\mathbf{r}\mathbf{e}}$^{c}[nM] |
---|---|---|---|---|

TCL11 * | −113,962.1 | −142,462.7 | 76 | |

TCL-33-08-05 | −114,523.3 | −143,133.8 | 4.1 | |

TCL-58-01-01 | −116,055.6 | −145,058.2 | 13.3 | |

TCL-58-16-08 | −114,528.9 | −143,152.9 | 9.2 | |

TCL-59-01-01 | −114,445.5 | −143,063.9 | 10.8 | |

TCL-60-01-01 | −114,415.0 | −143,027.9 | 9.1 |

^{a}Ensemble average of the total energy ${\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}}$ of the system (sum of potential energy ${\mathrm{E}}_{\mathrm{p}\mathrm{o}\mathrm{t}}$ and kinetic energy ${\mathrm{E}}_{\mathrm{k}\mathrm{i}\mathrm{n}}$);

^{b}Ensemble average of the potential energy ${\mathrm{E}}_{\mathrm{p}\mathrm{o}\mathrm{t}}$;

^{c}${\mathrm{I}\mathrm{C}}_{50}$ was predicted by QSAR model (Table 3, Equation (2)) for the designed analogues. Experimental ${\mathrm{I}\mathrm{C}}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$ for the

**TCL11**. *

**TCL11**is the co-crystallized ligand in the (PDB entry 1OOS) [21].

Inhibitors of PfENR | Number of Water Molecules | Number of Counterions | Number of Atoms of the System |
---|---|---|---|

TCL11 | 13,747 | 5 Cl^{−} | 45,561 |

TCL-33-08-05 | 13,806 | 6 Cl^{−} | 46,746 |

TCL-58-01-01 | 14,028 | 5 Cl^{−} | 47,406 |

TCL-58-16-08 | 13,810 | 6 Cl^{−} | 46,767 |

TCL-59-01-01 | 13,812 | 5 Cl^{−} | 46,761 |

TCL-60-01-01 | 13,807 | 5 Cl^{−} | 46,749 |

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**MDPI and ACS Style**

Bieri, C.; Esmel, A.; Keita, M.; Owono, L.C.O.; Dali, B.; Megnassan, E.; Miertus, S.; Frecer, V.
Structure-Based Design and Pharmacophore-Based Virtual Screening of Combinatorial Library of Triclosan Analogues Active against Enoyl-Acyl Carrier Protein Reductase of *Plasmodium falciparum* with Favourable ADME Profiles. *Int. J. Mol. Sci.* **2023**, *24*, 6916.
https://doi.org/10.3390/ijms24086916

**AMA Style**

Bieri C, Esmel A, Keita M, Owono LCO, Dali B, Megnassan E, Miertus S, Frecer V.
Structure-Based Design and Pharmacophore-Based Virtual Screening of Combinatorial Library of Triclosan Analogues Active against Enoyl-Acyl Carrier Protein Reductase of *Plasmodium falciparum* with Favourable ADME Profiles. *International Journal of Molecular Sciences*. 2023; 24(8):6916.
https://doi.org/10.3390/ijms24086916

**Chicago/Turabian Style**

Bieri, Cecile, Akori Esmel, Melalie Keita, Luc Calvin Owono Owono, Brice Dali, Eugene Megnassan, Stanislav Miertus, and Vladimir Frecer.
2023. "Structure-Based Design and Pharmacophore-Based Virtual Screening of Combinatorial Library of Triclosan Analogues Active against Enoyl-Acyl Carrier Protein Reductase of *Plasmodium falciparum* with Favourable ADME Profiles" *International Journal of Molecular Sciences* 24, no. 8: 6916.
https://doi.org/10.3390/ijms24086916