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Computational Methods in Drug Design and Food Chemistry
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Computational Methods in Drug Design and Food Chemistry

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (30 December 2020) | Viewed by 86789

Special Issue Editor

Dr. Giosuè Costa

E-Mail Website
Guest Editor
Department of Health Sciences- Magna Græcia University of Catanzaro, Catanzaro, Italy
Interests: drug discovery; computational chemistry; computer-aided drug design; virtual screening; food chemistry; nutraceuticals; natural products
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, the contribution of computational methodologies to drug discovery is no longer in doubt, and all major world pharmaceutical, academic, and biotechnology companies use computational design tools. Computer-aided drug design includes computational methods and resources that are used to facilitate the design and discovery of new bioactive chemical entities, including natural compounds with potentially nutraceutical activity.

The confirmation of the usefulness of these methodologies came in 2013, when the Nobel prize for chemistry was awarded to Martin Karplus, Michael Levitt, and Arieh Warshel “for the development of multiscale models for complex chemical systems”; thus, from this point of view, chemistry is an experimental science, but theoretical chemists are providing answers to questions about how to design drugs to fit with their target molecules.

In this Special Issue, we encourage authors to submit manuscripts in the form of a research paper, review, or communication that contributes positively in each aspect of medicinal chemistry and drug discovery, from the design of high-throughput screening libraries to providing estimations of the molecular properties required for drug molecules, improving our understanding of how they interact with biological targets of pharmaceutical interest.

This Special Issue will accept original research papers, high-quality reviews, and communications in the field of computational methods in drug design and food chemistry.

Dr. Giosuè Costa
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Molecular docking and structure-based virtual screening
  • Fragment-based drug design
  • Advances in molecular dynamics simulations and free-energy calculations applicable in drug design
  • QM applications in drug discovery
  • Pharmacophore modeling
  • In silico absorption, distribution, metabolism, and excretion (ADME)
  • Computational methods for drug target profiling and polypharmacology
  • Integrating structure- and ligand-based approaches for computer-aided drug design
  • Multi-target rational drug design
  • Computer-aided drug repurposing
  • In silico toxicology
  • Database of natural compounds: implementation and searching

Published Papers (18 papers)

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Research

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12 pages, 6772 KiB  
Article
PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors
by Enade P. Istyastono, Nunung Yuniarti, Vivitri D. Prasasty and Sudi Mungkasi
Molecules 2021, 26(9), 2452; https://doi.org/10.3390/molecules26092452 - 22 Apr 2021
Cited by 2 | Viewed by 2415
Abstract
Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction [...] Read more.
Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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24 pages, 8060 KiB  
Article
Binding of Androgen- and Estrogen-Like Flavonoids to Their Cognate (Non)Nuclear Receptors: A Comparison by Computational Prediction
by Giulia D’Arrigo, Eleonora Gianquinto, Giulia Rossetti, Gabriele Cruciani, Stefano Lorenzetti and Francesca Spyrakis
Molecules 2021, 26(6), 1613; https://doi.org/10.3390/molecules26061613 - 14 Mar 2021
Cited by 23 | Viewed by 4160
Abstract
Flavonoids are plant bioactives that are recognized as hormone-like polyphenols because of their similarity to the endogenous sex steroids 17β-estradiol and testosterone, and to their estrogen- and androgen-like activity. Most efforts to verify flavonoid binding to nuclear receptors (NRs) and explain their action [...] Read more.
Flavonoids are plant bioactives that are recognized as hormone-like polyphenols because of their similarity to the endogenous sex steroids 17β-estradiol and testosterone, and to their estrogen- and androgen-like activity. Most efforts to verify flavonoid binding to nuclear receptors (NRs) and explain their action have been focused on ERα, while less attention has been paid to other nuclear and non-nuclear membrane androgen and estrogen receptors. Here, we investigate six flavonoids (apigenin, genistein, luteolin, naringenin, quercetin, and resveratrol) that are widely present in fruits and vegetables, and often used as replacement therapy in menopause. We performed comparative computational docking simulations to predict their capability of binding nuclear receptors ERα, ERβ, ERRβ, ERRγ, androgen receptor (AR), and its variant ART877A and membrane receptors for androgens, i.e., ZIP9, GPRC6A, OXER1, TRPM8, and estrogens, i.e., G Protein-Coupled Estrogen Receptor (GPER). In agreement with data reported in literature, our results suggest that these flavonoids show a relevant degree of complementarity with both estrogen and androgen NR binding sites, likely triggering genomic-mediated effects. It is noteworthy that reliable protein–ligand complexes and estimated interaction energies were also obtained for some suggested estrogen and androgen membrane receptors, indicating that flavonoids could also exert non-genomic actions. Further investigations are needed to clarify flavonoid multiple genomic and non-genomic effects. Caution in their administration could be necessary, until the safe assumption of these natural molecules that are largely present in food is assured. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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13 pages, 2908 KiB  
Article
In Silico Strategy for Targeting the mTOR Kinase at Rapamycin Binding Site by Small Molecules
by Serena Vittorio, Rosaria Gitto, Ilenia Adornato, Emilio Russo and Laura De Luca
Molecules 2021, 26(4), 1103; https://doi.org/10.3390/molecules26041103 - 19 Feb 2021
Cited by 9 | Viewed by 3345
Abstract
Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified [...] Read more.
Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified structure-based pharmacophore hypothesis was built starting from the FKBP12-rapamycin-FRB ternary complex retrieved from RCSB Protein Data Bank (PDB code 1FAP). Then, the obtained model was used as filter to screen the ZINC biogenic compounds library, containing molecules derived from natural sources or natural-inspired compounds. The resulting hits were clustered according to their similarity; moreover, compounds showing the highest pharmacophore fit-score were chosen from each cluster. The selected molecules were subjected to docking studies to clarify their putative binding mode. The binding free energy of the obtained complexes was calculated by MM/GBSA method and the hits characterized by the lowest ΔGbind values were identified as potential mTOR inhibitors. Furthermore, the stability of the resulting complexes was studied by means of MD simulation which revealed that the selected compounds were able to form a stable ternary complex with FKBP12 and FRB domain, thus underlining their potential ability to inhibit mTOR with a rapamycin-like mechanism. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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28 pages, 8920 KiB  
Article
Heterocyclic Substitutions Greatly Improve Affinity and Stability of Folic Acid towards FRα. an In Silico Insight
by Mohammad G. Al-Thiabat, Fadi G. Saqallah, Amirah Mohd Gazzali, Noratiqah Mohtar, Beow Keat Yap, Yee Siew Choong and Habibah A Wahab
Molecules 2021, 26(4), 1079; https://doi.org/10.3390/molecules26041079 - 18 Feb 2021
Cited by 13 | Viewed by 3897
Abstract
Folate receptor alpha (FRα) is known as a biological marker for many cancers due to its overexpression in cancerous epithelial tissue. The folic acid (FA) binding affinity to the FRα active site provides a basis for designing more specific targets for FRα. Heterocyclic [...] Read more.
Folate receptor alpha (FRα) is known as a biological marker for many cancers due to its overexpression in cancerous epithelial tissue. The folic acid (FA) binding affinity to the FRα active site provides a basis for designing more specific targets for FRα. Heterocyclic rings have been shown to interact with many receptors and are important to the metabolism and biological processes within the body. Nineteen FA analogs with substitution with various heterocyclic rings were designed to have higher affinity toward FRα. Molecular docking was used to study the binding affinity of designed analogs compared to FA, methotrexate (MTX), and pemetrexed (PTX). Out of 19 FA analogs, analogs with a tetrazole ring (FOL03) and benzothiophene ring (FOL08) showed the most negative binding energy and were able to interact with ASP81 and SER174 through hydrogen bonds and hydrophobic interactions with amino acids of the active site. Hence, 100 ns molecular dynamics (MD) simulations were carried out for FOL03, FOL08 compared to FA, MTX, and PTX. The root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of FOL03 and FOL08 showed an apparent convergence similar to that of FA, and both of them entered the binding pocket (active site) from the pteridine part, while the glutamic part was stuck at the FRα pocket entrance during the MD simulations. Molecular mechanics Poisson-Boltzmann surface accessible (MM-PBSA) and H-bond analysis revealed that FOL03 and FOL08 created more negative free binding and electrostatic energy compared to FA and PTX, and both formed stronger H-bond interactions with ASP81 than FA with excellent H-bond profiles that led them to become bound tightly in the pocket. In addition, pocket volume calculations showed that the volumes of active site for FOL03 and FOL08 inside the FRα pocket were smaller than the FA–FRα system, indicating strong interactions between the protein active site residues with these new FA analogs compared to FA during the MD simulations. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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13 pages, 3103 KiB  
Article
In Silico Study of Polyunsaturated Fatty Acids as Potential SARS-CoV-2 Spike Protein Closed Conformation Stabilizers: Epidemiological and Computational Approaches
by Alonso Vivar-Sierra, María José Araiza-Macías, José Patricio Hernández-Contreras, Arely Vergara-Castañeda, Gabriela Ramírez-Vélez, Rodolfo Pinto-Almazán, Juan Rodrigo Salazar and Marco A. Loza-Mejía
Molecules 2021, 26(3), 711; https://doi.org/10.3390/molecules26030711 - 29 Jan 2021
Cited by 34 | Viewed by 7661
Abstract
SARS-CoV-2 infects host cells by interacting its spike protein with surface angiotensin-converting enzyme 2 (ACE2) receptors, expressed in lung and other cell types. Although several risk factors could explain why some countries have lower incidence and fatality rates than others, environmental factors such [...] Read more.
SARS-CoV-2 infects host cells by interacting its spike protein with surface angiotensin-converting enzyme 2 (ACE2) receptors, expressed in lung and other cell types. Although several risk factors could explain why some countries have lower incidence and fatality rates than others, environmental factors such as diet should be considered. It has been described that countries with high polyunsaturated fatty acid (PUFA) intake have a lower number of COVID-19 victims and a higher rate of recovery from the disease. Moreover, it was found that linoleic acid, an omega-6 PUFA, could stabilize the spike protein in a closed conformation, blocking its interaction with ACE2. These facts prompted us to perform in silico simulations to determine if other PUFA could also stabilize the closed conformation of spike protein and potentially lead to a reduction in SARS-CoV-2 infection. We found that: (a) countries whose source of omega-3 is from marine origin have lower fatality rates; and (b) like linoleic acid, omega-3 PUFA could also bind to the closed conformation of spike protein and therefore, could help reduce COVID-19 complications by reducing viral entrance to cells, in addition to their known anti-inflammatory effects. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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14 pages, 3109 KiB  
Article
Molecular Docking Suggests the Targets of Anti-Mycobacterial Natural Products
by Rafael Baptista, Sumana Bhowmick, Jianying Shen and Luis A. J. Mur
Molecules 2021, 26(2), 475; https://doi.org/10.3390/molecules26020475 - 18 Jan 2021
Cited by 20 | Viewed by 3608
Abstract
Tuberculosis (TB) is a major global threat, mostly due to the development of antibiotic-resistant forms of Mycobacterium tuberculosis, the causal agent of the disease. Driven by the pressing need for new anti-mycobacterial agents several natural products (NPs) have been shown to have in [...] Read more.
Tuberculosis (TB) is a major global threat, mostly due to the development of antibiotic-resistant forms of Mycobacterium tuberculosis, the causal agent of the disease. Driven by the pressing need for new anti-mycobacterial agents several natural products (NPs) have been shown to have in vitro activities against M. tuberculosis. The utility of any NP as a drug lead is augmented when the anti-mycobacterial target(s) is unknown. To suggest these, we used a molecular reverse docking approach to predict the interactions of 53 selected anti-mycobacterial NPs against known “druggable” mycobacterial targets ClpP1P2, DprE1, InhA, KasA, PanK, PknB and Pks13. The docking scores/binding free energies were predicted and calculated using AutoDock Vina along with physicochemical and structural properties of the NPs, using PaDEL descriptors. These were compared to the established inhibitor (control) drugs for each mycobacterial target. The specific interactions of the bisbenzylisoquinoline alkaloids 2-nortiliacorinine, tiliacorine and 13′-bromotiliacorinine against the targets PknB and DprE1 (−11.4, −10.9 and −9.8 kcal·mol−1; −12.7, −10.9 and −10.3 kcal·mol−1, respectively) and the lignan α-cubebin and Pks13 (−11.0 kcal·mol−1) had significantly superior docking scores compared to controls. Our approach can be used to suggest predicted targets for the NP to be validated experimentally, but these in silico steps are likely to facilitate drug optimization. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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21 pages, 4655 KiB  
Article
Five Novel Non-Sialic Acid-Like Scaffolds Inhibit In Vitro H1N1 and H5N2 Neuraminidase Activity of Influenza a Virus
by Luis Márquez-Domínguez, Julio Reyes-Leyva, Irma Herrera-Camacho, Gerardo Santos-López and Thomas Scior
Molecules 2020, 25(18), 4248; https://doi.org/10.3390/molecules25184248 - 16 Sep 2020
Cited by 10 | Viewed by 2866
Abstract
Neuraminidase (NA) of influenza viruses enables the virus to access the cell membrane. It degrades the sialic acid contained in extracellular mucin. Later, it is responsible for releasing newly formed virions from the membrane of infected cells. Both processes become key functions within [...] Read more.
Neuraminidase (NA) of influenza viruses enables the virus to access the cell membrane. It degrades the sialic acid contained in extracellular mucin. Later, it is responsible for releasing newly formed virions from the membrane of infected cells. Both processes become key functions within the viral cycle. Therefore, it is a therapeutic target for research of the new antiviral agents. Structure–activity relationships studies have revealed which are the important functional groups for the receptor–ligand interaction. Influenza virus type A NA activity was inhibited by five scaffolds without structural resemblance to sialic acid. Intending small organic compound repositioning along with drug repurposing, this study combined in silico simulations of ligand docking into the known binding site of NA, along with in vitro bioassays. The five proposed scaffolds are N-acetylphenylalanylmethionine, propanoic 3-[(2,5-dimethylphenyl) carbamoyl]-2-(piperazin-1-yl) acid, 3-(propylaminosulfonyl)-4-chlorobenzoic acid, ascorbic acid (vitamin C), and 4-(dipropylsulfamoyl) benzoic acid (probenecid). Their half maximal inhibitory concentration (IC50) was determined through fluorometry. An acidic reagent 2′-O-(4-methylumbelliferyl)-α-dN-acetylneuraminic acid (MUNANA) was used as substrate for viruses of human influenza H1N1 or avian influenza H5N2. Inhibition was observed in millimolar ranges in a concentration-dependent manner. The IC50 values of the five proposed scaffolds ranged from 6.4 to 73 mM. The values reflect a significant affinity difference with respect to the reference drug zanamivir (p < 0.001). Two compounds (N-acetyl dipeptide and 4-substituted benzoic acid) clearly showed competitive mechanisms, whereas ascorbic acid reflected non-competitive kinetics. The five small organic molecules constitute five different scaffolds with moderate NA affinities. They are proposed as lead compounds for developing new NA inhibitors which are not analogous to sialic acid. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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23 pages, 6503 KiB  
Article
Probing the Highly Disparate Dual Inhibitory Mechanisms of Novel Quinazoline Derivatives against Mycobacterium tuberculosis Protein Kinases A and B
by Fisayo A. Olotu and Mahmoud E. Soliman
Molecules 2020, 25(18), 4247; https://doi.org/10.3390/molecules25184247 - 16 Sep 2020
Cited by 1 | Viewed by 2022
Abstract
Mycobacterium tuberculosis (Mtb) serine/threonine (Ser/Thr) Protein kinases A (PknA) and B (PknB) have been identified as highly attractive targets for overcoming drug resistant tuberculosis. A recent lead series optimization study yielded compound 33 which exhibited potencies ~1000 times higher than compound [...] Read more.
Mycobacterium tuberculosis (Mtb) serine/threonine (Ser/Thr) Protein kinases A (PknA) and B (PknB) have been identified as highly attractive targets for overcoming drug resistant tuberculosis. A recent lead series optimization study yielded compound 33 which exhibited potencies ~1000 times higher than compound 57. This huge discrepancy left us curious to investigate the mechanistic ‘dual’ (in)activities of the compound using computational methods, as carried out in this study. Findings revealed that 33 stabilized the PknA and B conformations and reduced their structural activities relative to 57. Optimal stability of 33 in the hydrophobic pockets further induced systemic alterations at the P-loops, catalytic loops, helix Cs and DFG motifs of PknA and B. Comparatively, 57 was more surface-bound with highly unstable motions. Furthermore, 33 demonstrated similar binding patterns in PknA and B, involving conserved residues of their binding pockets. Both π and hydrogen interactions played crucial roles in the binding of 33, which altogether culminated in high ΔGs for both proteins. On the contrary, the binding of 57 was characterized by unfavorable interactions with possible repulsive effects on its optimal dual binding to both proteins, as evidenced by the relatively lowered ΔGs. These findings would significantly contribute to the rational structure-based design of novel and highly selective dual inhibitors of Mtb PknA and B. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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12 pages, 2943 KiB  
Article
Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
by Javed Iqbal, Martin Vogt and Jürgen Bajorath
Molecules 2020, 25(17), 3952; https://doi.org/10.3390/molecules25173952 - 29 Aug 2020
Cited by 1 | Viewed by 2350
Abstract
Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For [...] Read more.
Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For SAR analysis, 3D AL models are particularly intuitive. In these models, an interpolated potency surface is added as a third dimension to a 2D projection of chemical space. Accordingly, AL topology can be associated with characteristic SAR features. Going beyond visualization and a qualitative assessment of SARs, it would be very helpful to compare 3D ALs of different datasets in more quantitative terms. However, quantitative AL analysis is still in its infancy. Recently, it has been shown that 3D AL models with pre-defined topologies can be correctly classified using machine learning. Classification was facilitated on the basis of AL image feature representations learned with convolutional neural networks. Therefore, we have further investigated image analysis for quantitative comparison of 3D ALs and devised an approach to determine (dis)similarity relationships for ALs representing different compound datasets. Herein, we report this approach and demonstrate proof-of-principle. The methodology makes it possible to computationally compare 3D ALs and quantify topological differences reflecting varying SAR information content. For SAR exploration in drug design, this adds a quantitative measure of AL (dis)similarity to graphical analysis. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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16 pages, 2262 KiB  
Article
Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer
by Xiaojia Ji, Chunming Jin, Xialan Dong, Maria S. Dixon, Kevin P. Williams and Weifan Zheng
Molecules 2020, 25(17), 3933; https://doi.org/10.3390/molecules25173933 - 28 Aug 2020
Cited by 6 | Viewed by 2906
Abstract
Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We [...] Read more.
Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec. 3.80 million “cancer”-related PubMed abstracts were processed as the corpus for Word2Vec to derive vector representation of biological concepts. These vectors for drugs and diseases served as the foundation for creating similarity maps of drugs and diseases, respectively, which were then employed to find potential therapy for IBC. Three hundred and thirty-six (336) known drugs and three hundred and seventy (370) diseases were expressed as vectors in this study. Nine hundred and seventy (970) previously known drug-disease association pairs among these drugs and diseases were used as the reference set. Based on the hypothesis that similar drugs can be used against similar diseases, we have identified 18 diseases similar to IBC, with 24 corresponding known drugs proposed to be the repurposing therapy for IBC. The literature search confirmed most known drugs tested for IBC, with four of them being novel candidates. We conclude that LWAS based on the Word2Vec technology is a novel approach to drug repurposing especially useful for rare diseases. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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9 pages, 1231 KiB  
Article
Alkylated Sesamol Derivatives as Potent Antioxidants
by Ivanete C. Palheta, Lanalice R. Ferreira, Joyce K. L. Vale, Osmarina P. P. Silva, Anderson M. Herculano, Karen R. H. M. Oliveira, Antonio M. J. Chaves Neto, Joaquín M. Campos, Cleydson B. R. Santos and Rosivaldo S. Borges
Molecules 2020, 25(14), 3300; https://doi.org/10.3390/molecules25143300 - 21 Jul 2020
Cited by 7 | Viewed by 2511
Abstract
Sesamol is a phenolic derivative. Its antioxidant activity is low than that of Trolox and depends on benzodioxole moiety. Thus, a molecular modification strategy through alkylation, inspired by natural and synthetic antioxidants, was studied by molecular modeling at the DFT/B3LYP level of theory [...] Read more.
Sesamol is a phenolic derivative. Its antioxidant activity is low than that of Trolox and depends on benzodioxole moiety. Thus, a molecular modification strategy through alkylation, inspired by natural and synthetic antioxidants, was studied by molecular modeling at the DFT/B3LYP level of theory by comparing the 6-31+G(d,p) and 6-311++G(2d,2p) basis sets. All proposed derivatives were compared to classical related antioxidants such as Trolox, t-butylated hydroxytoluene (BHT) and t-butylated hydroxyanisole (BHA). According to our results, molecular orbitals, single electron or hydrogen-atom transfers, spin density distributions, and alkyl substitutions at the ortho positions related to phenol moiety were found to be more effective than any other positions. The trimethylated derivative was more potent than Trolox. t-Butylated derivatives were stronger than all other alkylated derivatives and may be new alternative forms of modified antioxidants from natural products with applications in the chemical, pharmaceutical, and food industries. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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17 pages, 2165 KiB  
Article
Computational Methods for the Identification of Molecular Targets of Toxic Food Additives. Butylated Hydroxytoluene as a Case Study
by Valentina Tortosa, Valentina Pietropaolo, Valentina Brandi, Gabriele Macari, Andrea Pasquadibisceglie and Fabio Polticelli
Molecules 2020, 25(9), 2229; https://doi.org/10.3390/molecules25092229 - 09 May 2020
Cited by 9 | Viewed by 3838
Abstract
Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use [...] Read more.
Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein–ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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16 pages, 2959 KiB  
Article
Molecular Modeling of Epithiospecifier and Nitrile-Specifier Proteins of Broccoli and Their Interaction with Aglycones
by Juan Román, Dorian González, Mario Inostroza-Ponta and Andrea Mahn
Molecules 2020, 25(4), 772; https://doi.org/10.3390/molecules25040772 - 11 Feb 2020
Cited by 9 | Viewed by 3589
Abstract
Glucosinolates are secondary plant metabolites of Brassicaceae. They exert their effect after enzymatic hydrolysis to yield aglycones, which become nitriles and epithionitriles through the action of epithiospecifier (ESP) and nitrile-specifier proteins (NSP). The mechanism of action of broccoli ESP and NSP is [...] Read more.
Glucosinolates are secondary plant metabolites of Brassicaceae. They exert their effect after enzymatic hydrolysis to yield aglycones, which become nitriles and epithionitriles through the action of epithiospecifier (ESP) and nitrile-specifier proteins (NSP). The mechanism of action of broccoli ESP and NSP is poorly understood mainly because ESP and NSP structures have not been completely characterized and because aglycones are unstable, thus hindering experimental measurements. The aim of this work was to investigate the interaction of broccoli ESP and NSP with the aglycones derived from broccoli glucosinolates using molecular simulations. The three-dimensional structure of broccoli ESP was built based on its amino-acid sequence, and the NSP structure was constructed based on a consensus amino-acid sequence. The models obtained using Iterative Threading ASSEmbly Refinement (I-TASSER) were refined with the OPLS-AA/L all atom force field of GROMACS 5.0.7 and were validated by Veryfy3D and ERRAT. The structures were selected based on molecular dynamics simulations. Interactions between the proteins and aglycones were simulated with Autodock Vina at different pH. It was concluded that pH determines the stability of the complexes and that the aglycone derived from glucoraphanin has the highest affinity to both ESP and NSP. This agrees with the fact that glucoraphanin is the most abundant glucosinolate in broccoli florets. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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14 pages, 5470 KiB  
Article
Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors
by Sheila C. Araujo, Vinicius G. Maltarollo, Michell O. Almeida, Leonardo L. G. Ferreira, Adriano D. Andricopulo and Kathia M. Honorio
Molecules 2020, 25(2), 264; https://doi.org/10.3390/molecules25020264 - 09 Jan 2020
Cited by 10 | Viewed by 3802
Abstract
Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are [...] Read more.
Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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13 pages, 1101 KiB  
Article
Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks
by Floriane Montanari, Lara Kuhnke, Antonius Ter Laak and Djork-Arné Clevert
Molecules 2020, 25(1), 44; https://doi.org/10.3390/molecules25010044 - 21 Dec 2019
Cited by 70 | Viewed by 6356
Abstract
Simple physico-chemical properties, like logD, solubility, or melting point, can reveal a great deal about how a compound under development might later behave. These data are typically measured for most compounds in drug discovery projects in a medium throughput fashion. Collecting and assembling [...] Read more.
Simple physico-chemical properties, like logD, solubility, or melting point, can reveal a great deal about how a compound under development might later behave. These data are typically measured for most compounds in drug discovery projects in a medium throughput fashion. Collecting and assembling all the Bayer in-house data related to these properties allowed us to apply powerful machine learning techniques to predict the outcome of those assays for new compounds. In this paper, we report our finding that, especially for predicting physicochemical ADMET endpoints, a multitask graph convolutional approach appears a highly competitive choice. For seven endpoints of interest, we compared the performance of that approach to fully connected neural networks and different single task models. The new model shows increased predictive performance compared to previous modeling methods and will allow early prioritization of compounds even before they are synthesized. In addition, our model follows the generalized solubility equation without being explicitly trained under this constraint. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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19 pages, 5670 KiB  
Article
Modeling the Antileukemia Activity of Ellipticine-Related Compounds: QSAR and Molecular Docking Study
by Edgar Márquez, José R. Mora, Virginia Flores-Morales, Daniel Insuasty and Luis Calle
Molecules 2020, 25(1), 24; https://doi.org/10.3390/molecules25010024 - 19 Dec 2019
Cited by 11 | Viewed by 3364
Abstract
The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski’s rules and have a structure analogous [...] Read more.
The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski’s rules and have a structure analogous to ellipticine, was performed using the quantitative structure activity relationship (QSAR) technique, followed by a detailed docking study on three different proteins significantly involved in this disease (PDB IDs: SYK, PI3K and BTK). As a result, a model with only four descriptors (HOMO, softness, AC1RABAMBID, and TS1KFABMID) was found to be robust enough for prediction of the antileukemia activity of the compounds studied in this work, with an R2 of 0.899 and Q2 of 0.730. A favorable interaction between the compounds and their target proteins was found in all cases; in particular, compounds 9 and 22 showed high activity and binding free energy values of around −10 kcal/mol. Theses compounds were evaluated in detail based on their molecular structure, and some modifications are suggested herein to enhance their biological activity. In particular, compounds 22_1, 22_2, 9_1, and 9_2 are indicated as possible new, potent ellipticine derivatives to be synthesized and biologically tested. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Review

Jump to: Research

17 pages, 1385 KiB  
Review
Machine Learning Methods in Drug Discovery
by Lauv Patel, Tripti Shukla, Xiuzhen Huang, David W. Ussery and Shanzhi Wang
Molecules 2020, 25(22), 5277; https://doi.org/10.3390/molecules25225277 - 12 Nov 2020
Cited by 158 | Viewed by 18766
Abstract
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. [...] Read more.
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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19 pages, 1956 KiB  
Review
Application of MM-PBSA Methods in Virtual Screening
by Giulio Poli, Carlotta Granchi, Flavio Rizzolio and Tiziano Tuccinardi
Molecules 2020, 25(8), 1971; https://doi.org/10.3390/molecules25081971 - 23 Apr 2020
Cited by 100 | Viewed by 7607
Abstract
Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target [...] Read more.
Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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