Computer-Aided Drug Design and Drug Discovery

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 15271

Special Issue Editors


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Guest Editor
Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania
Interests: pharmacology; drug discovery; drug repurposing; virtual screening; small molecule drugs; neurodegenerative diseases; TRP channels

E-Mail Website
Guest Editor
Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, 6 Traian Vuia Street, 020956 Bucharest, Romania
Interests: the design and synthesis of new anticancer agents; the design and synthesis of new antimicrobial compounds; studies and structural analysis; the isolation and analysis of natural compounds with anticancer effects; computer-assisted drug design studies
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Special Issue Information

Dear Colleagues,

Computer-aided methods play a crucial role in every stage of drug discovery and development, enabling a more efficient and cost-effective approach. The integration of computational methods with experimental approaches has become indispensable in modern drug discovery. At a time when advancements in technology are revolutionizing the pharmaceutical industry, this Special Issue aims to highlight the latest developments in computer-aided drug design and drug discovery. The iterative process of designing, synthesizing, and testing new compounds can lead to the identification of promising drug leads. This Special Issue will provide a platform to showcase cutting-edge research, innovative methodologies, and breakthroughs that leverage computational approaches in pharmaceutical research. It will explore various aspects, including but not limited to: 

  • Novel computational techniques and algorithms in drug design;
  • Artificial intelligence and machine learning in drug discovery;
  • Molecular modeling and simulation for drug development;
  • Virtual screening and high-throughput screening methods;
  • Structure-based drug design and ligand–receptor interactions;
  • CADD in pharmacokinetics (ADME prediction);
  • Target and off-target identification;
  • Repurposing candidate prioritization;
  • Optimization strategies for lead compounds;
  • Predictive modeling and toxicology assessments;
  • Case studies and successful applications of computer-aided drug design.

We invite you to contribute to this Special Issue by submitting your original research, review articles, or perspectives that encapsulate your expertise and insights. Your contribution would provide readers with valuable knowledge and inspire further advancements in the field.

Dr. Dragos Paul Mihai
Prof. Dr. George Mihai Nitulescu
Guest Editors

Manuscript Submission Information

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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. Pharmaceuticals is an international peer-reviewed open access monthly 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 2900 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

  • chemoinformatics
  • bioinformatics
  • molecular modeling
  • virtual screening
  • molecular docking
  • quantitative structure–activity relationship (QSAR)
  • pharmacophore modeling
  • target identification
  • repurposing
  • ADME-T prediction

Published Papers (11 papers)

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Research

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25 pages, 13165 KiB  
Article
Investigating Potential Cancer Therapeutics: Insight into Histone Deacetylases (HDACs) Inhibitions
by Basharat Ahmad, Aamir Saeed, Ahmed Al-Amery, Ismail Celik, Iraj Ahmed, Muhammad Yaseen, Imran Ahmad Khan, Dhurgham Al-Fahad and Mashooq Ahmad Bhat
Pharmaceuticals 2024, 17(4), 444; https://doi.org/10.3390/ph17040444 - 29 Mar 2024
Viewed by 491
Abstract
Histone deacetylases (HDACs) are enzymes that remove acetyl groups from ɛ-amino of histone, and their involvement in the development and progression of cancer disorders makes them an interesting therapeutic target. This study seeks to discover new inhibitors that selectively inhibit HDAC enzymes which [...] Read more.
Histone deacetylases (HDACs) are enzymes that remove acetyl groups from ɛ-amino of histone, and their involvement in the development and progression of cancer disorders makes them an interesting therapeutic target. This study seeks to discover new inhibitors that selectively inhibit HDAC enzymes which are linked to deadly disorders like T-cell lymphoma, childhood neuroblastoma, and colon cancer. MOE was used to dock libraries of ZINC database molecules within the catalytic active pocket of target HDACs. The top three hits were submitted to MD simulations ranked on binding affinities and well-occupied interaction mechanisms determined from molecular docking studies. Inside the catalytic active site of HDACs, the two stable inhibitors LIG1 and LIG2 affect the protein flexibility, as evidenced by RMSD, RMSF, Rg, and PCA. MD simulations of HDACs complexes revealed an alteration from extended to bent motional changes within loop regions. The structural deviation following superimposition shows flexibility via a visual inspection of movable loops at different timeframes. According to PCA, the activity of HDACs inhibitors induces structural dynamics that might potentially be utilized to define the nature of protein inhibition. The findings suggest that this study offers solid proof to investigate LIG1 and LIG2 as potential HDAC inhibitors. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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19 pages, 4512 KiB  
Article
AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations
by Enrico Bautista, Young Hyun Jung, Manuela Jaramillo, Harrish Ganesh, Aryaan Varma, Kush Savsani and Sivanesan Dakshanamurthy
Pharmaceuticals 2024, 17(4), 419; https://doi.org/10.3390/ph17040419 - 26 Mar 2024
Viewed by 712
Abstract
The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico [...] Read more.
The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope–HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope–MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax’s comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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20 pages, 12507 KiB  
Article
Identification of mIDH1 R132C/S280F Inhibitors from Natural Products by Integrated Molecular Docking, Pharmacophore Modeling and Molecular Dynamics Simulations
by Weitong Zhang, Hailong Bai, Yifan Wang, Xiaorui Wang, Ruyi Jin, Hui Guo, Huanling Lai, Yuping Tang and Yuwei Wang
Pharmaceuticals 2024, 17(3), 336; https://doi.org/10.3390/ph17030336 - 05 Mar 2024
Viewed by 684
Abstract
Mutant isocitrate dehydrogenase 1 (mIDH1) is a common driving factor in acute myeloid leukemia (AML), with the R132 mutation accounting for a high proportion. The U.S. Food and Drug Administration (FDA) approved Ivosidenib, a molecular entity that targets IDH1 with R132 mutations, as [...] Read more.
Mutant isocitrate dehydrogenase 1 (mIDH1) is a common driving factor in acute myeloid leukemia (AML), with the R132 mutation accounting for a high proportion. The U.S. Food and Drug Administration (FDA) approved Ivosidenib, a molecular entity that targets IDH1 with R132 mutations, as a promising therapeutic option for AML with mIDH1 in 2018. It was of concern that the occurrence of disease resistance or recurrence, attributed to the IDH1 R132C/S280F second site mutation, was observed in certain patients treated with Ivosidenib within the same year. Furthermore, it should be noted that most mIDH1 inhibitors demonstrated limited efficacy against mutations at this specific site. Therefore, there is an urgent need to investigate novel inhibitors targeting mIDH1 for combating resistance caused by IDH1 R132C/S280F mutations in AML. This study aimed to identify novel mIDH1 R132C/S280F inhibitors through an integrated strategy of combining virtual screening and dynamics simulations. First, 2000 hits were obtained through structure-based virtual screening of the COCONUT database, and hits with better scores than −10.67 kcal/mol were obtained through molecular docking. A total of 12 potential small molecule inhibitors were identified through pharmacophore modeling screening and Prime MM-GBSA. Dynamics simulations were used to study the binding modes between the positive drug and the first three hits and IDH1 carrying the R132C/S280F mutation. RMSD showed that the four dynamics simulation systems remained stable, and RMSF and Rg showed that the screened molecules have similar local flexibility and tightness to the positive drug. Finally, the lowest energy conformation, hydrogen bond analysis, and free energy decomposition results indicate that in the entire system the key residues LEU120, TRP124, TRP267, and VAL281 mainly contribute van der Waals forces to the interaction, while the key residues VAL276 and CYS379 mainly contribute electrostatic forces. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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22 pages, 10149 KiB  
Article
Heterocycles 52: The Drug-Likeness Analysis of Anti-Inflammatory Thiazolo[3,2-b][1,2,4]triazole and Imidazo[2,1-b][1,3,4]thiadiazole Derivatives
by Anamaria Apan, Dorina Casoni, Denisa Leonte, Cristina Pop, Irina Iaru, Cristina Mogoșan and Valentin Zaharia
Pharmaceuticals 2024, 17(3), 295; https://doi.org/10.3390/ph17030295 - 25 Feb 2024
Viewed by 843
Abstract
Lipophilicity, a significant physicochemical parameter of bioactive molecules, along with absorption, distribution, metabolism, excretion parameters and toxicity risk, was investigated for 32 thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives with anti-inflammatory potential. The experimental lipophilicity study was carried out by reversed-phase thin-layer chromatography in a binary [...] Read more.
Lipophilicity, a significant physicochemical parameter of bioactive molecules, along with absorption, distribution, metabolism, excretion parameters and toxicity risk, was investigated for 32 thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives with anti-inflammatory potential. The experimental lipophilicity study was carried out by reversed-phase thin-layer chromatography in a binary isopropanol-water mobile phase, and the obtained results were compared with the theoretical lipophilicity parameters estimated by various computational methods. Strong correlations were found between the experimental retention factors and calculated partition coefficients. A modified Petra/Osiris/Molinspiration analysis was performed on the previously synthesized compounds, using SwissADME, Osiris and Molinspiration web tools. The predicted in silico parameters highlighted the most promising compounds as potential drug candidates. The compounds showed good gastrointestinal absorption, moderate activity according to the bioactivity score (values situated between −1.25 and −0.06), and a safe toxicity profile. The results obtained in this study will contribute to lipophilicity studies and other future studies focused on modulating new drug candidates starting from thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives, which are important heterocycles in medicinal chemistry. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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20 pages, 4539 KiB  
Article
Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction
by Ismail Mondal, Amit Kumar Halder, Nirupam Pattanayak, Sudip Kumar Mandal and Maria Natalia D. S. Cordeiro
Pharmaceuticals 2024, 17(2), 263; https://doi.org/10.3390/ph17020263 - 19 Feb 2024
Viewed by 864
Abstract
Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques [...] Read more.
Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure–Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds’ high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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17 pages, 4925 KiB  
Article
Antimicrobial Evaluation of Sulfonamides after Coupling with Thienopyrimidine Coplanar Structure
by Elshaymaa I. Elmongy, Wejdan S. Alanazi, Alhanouf I. Aldawsari, Asma A. Alfaouri and Reem Binsuwaidan
Pharmaceuticals 2024, 17(2), 188; https://doi.org/10.3390/ph17020188 - 31 Jan 2024
Viewed by 710
Abstract
This work describes the design and synthesis of three series of hybrids of thienopyrimidines and sulfonamides. Dihydrofolate reductase enzyme was selected as a target for the in-silico screening of the synthesized thienopyrimidine–sulfonamide hybrid as an antibacterial, while squalene epoxidase was selected as an [...] Read more.
This work describes the design and synthesis of three series of hybrids of thienopyrimidines and sulfonamides. Dihydrofolate reductase enzyme was selected as a target for the in-silico screening of the synthesized thienopyrimidine–sulfonamide hybrid as an antibacterial, while squalene epoxidase was selected as an antifungal target protein. All screened compounds showed promising binding affinity ranges, with perfect fitting not exceeding 1.9 Å. The synthesized compounds were tested for their antimicrobial activity using agar well diffusion and minimum inhibitory concentration tests against six bacterial strains in addition to two Candida strains. Compounds 8iii and 12ii showed varying degrees of inhibition against Staphylococcus aureus and Escherichia coli bacterial strains, whereas the best antifungal activity against Candida was displayed by compound 8iii. Compound 12ii, the cyclohexathienopyrimidine coupled with sulfadiazine at position 3, has the best antibacterial activity, which is consistent with molecular docking results at the active site of the oxidoreductase protein. Interestingly, compound 12ii also has the highest docking binding energy at the antifungal squalene epoxidase active site. Investigating the physicochemical properties of the synthesized hybrids revealed their high tolerability with cell membranes, and moderate to poor oral bioavailability, and that all are drug-like candidates, among which 4i, the cyclohexathieno[2,3-d] pyrimidine core with sulphaguanidine incorporated at position 4, recorded the best score (1.58). Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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20 pages, 6972 KiB  
Article
Exploring Potentilla nepalensis Phytoconstituents: Integrated Strategies of Network Pharmacology, Molecular Docking, Dynamic Simulations, and MMGBSA Analysis for Cancer Therapeutic Targets Discovery
by Mallari Praveen, Ihsan Ullah, Ricardo Buendia, Imran Ahmad Khan, Mian Gul Sayed, Rahmul Kabir, Mashooq Ahmad Bhat and Muhammad Yaseen
Pharmaceuticals 2024, 17(1), 134; https://doi.org/10.3390/ph17010134 - 19 Jan 2024
Cited by 2 | Viewed by 1208
Abstract
Potentilla nepalensis belongs to the Rosaceae family and has numerous therapeutic applications as potent plant-based medicine. Forty phytoconstituents (PCs) from the root and stem through n-hexane (NR and NS) and methanolic (MR and MS) extracts were identified in earlier studies. However, the PCs [...] Read more.
Potentilla nepalensis belongs to the Rosaceae family and has numerous therapeutic applications as potent plant-based medicine. Forty phytoconstituents (PCs) from the root and stem through n-hexane (NR and NS) and methanolic (MR and MS) extracts were identified in earlier studies. However, the PCs affecting human genes and their roles in the body have not previously been disclosed. In this study, we employed network pharmacology, molecular docking, molecular dynamics simulations (MDSs), and MMGBSA methodologies. The SMILES format of PCs from the PubChem was used as input to DIGEP-Pred, with 764 identified as the inducing genes. Their enrichment studies have shown inducing genes’ gene ontology descriptions, involved pathways, associated diseases, and drugs. PPI networks constructed in String DB and network topological analyzing parameters performed in Cytoscape v3.10 revealed three therapeutic targets: TP53 from MS-, NR-, and NS-induced genes; HSPCB and Nf-kB1 from MR-induced genes. From 40 PCs, two PCs, 1b (MR) and 2a (MS), showed better binding scores (kcal/mol) with p53 protein of −8.6 and −8.0, and three PCs, 3a, (NR) 4a, and 4c (NS), with HSP protein of −9.6, −8.7, and −8.2. MDS and MMGBSA revealed these complexes are stable without higher deviations with better free energy values. Therapeutic targets identified in this study have a prominent role in numerous cancers. Thus, further investigations such as in vivo and in vitro studies should be carried out to find the molecular functions and interlaying mechanism of the identified therapeutic targets on numerous cancer cell lines in considering the PCs of P. nepalensis. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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12 pages, 2240 KiB  
Article
In Silico Screening of Multi-Domain Targeted Inhibitors for PTK6: A Strategy Integrating Drug Repurposing and Consensus Docking
by Yujing Zhou and Ming Wah Wong
Pharmaceuticals 2024, 17(1), 60; https://doi.org/10.3390/ph17010060 - 29 Dec 2023
Viewed by 777
Abstract
Protein tyrosine kinase 6 (PTK6), also known as breast tumor kinase (BRK), serves as a non-receptor intracellular tyrosine kinase within the Src kinases family. Structurally resembling other Src kinases, PTK6 possesses an Src homology 3 (SH3) domain, an Src homology 2 (SH2) domain, [...] Read more.
Protein tyrosine kinase 6 (PTK6), also known as breast tumor kinase (BRK), serves as a non-receptor intracellular tyrosine kinase within the Src kinases family. Structurally resembling other Src kinases, PTK6 possesses an Src homology 3 (SH3) domain, an Src homology 2 (SH2) domain, and a tyrosine kinase domain (SH1). While considerable efforts have been dedicated to designing PTK6 inhibitors targeting the SH1 domain, which is responsible for kinase activity in various pathways, it has been observed that solely inhibiting the SH1 domain does not effectively suppress PTK6 activity. Subsequent investigations have revealed the involvement of SH2 and SH3 domains in intramolecular and substrate binding interactions, which are crucial for PTK6 function. Consequently, the identification of PTK6 inhibitors targeting not only the SH1 domain but also the SH2 and SH3 domains becomes imperative. Through an in silico structural-based virtual screening approach, incorporating drug repurposing and a consensus docking approach, we have successfully identified four potential ligands capable of concurrently inhibiting the tyrosine kinase domain and SH2/SH3 domains of PT6K simultaneously. This finding suggests potential pathways for therapeutic interventions in PTK6 inhibition. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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22 pages, 2485 KiB  
Article
Quality Assessment of Selected Protein Structures Derived from Homology Modeling and AlphaFold
by Furkan Ayberk Binbay, Dhruv Chetanbhai Rathod, Ajay Abisheck Paul George and Diana Imhof
Pharmaceuticals 2023, 16(12), 1662; https://doi.org/10.3390/ph16121662 - 29 Nov 2023
Cited by 1 | Viewed by 1121
Abstract
With technology advancing, many prediction algorithms have been developed to facilitate the modeling of inherently dynamic and flexible macromolecules such as proteins. Improvements in the prediction of protein structures have attracted a great deal of attention due to the advantages they offer, e.g., [...] Read more.
With technology advancing, many prediction algorithms have been developed to facilitate the modeling of inherently dynamic and flexible macromolecules such as proteins. Improvements in the prediction of protein structures have attracted a great deal of attention due to the advantages they offer, e.g., in drug design. While trusted experimental methods, such as X-ray crystallography, NMR spectroscopy, and electron microscopy, are preferred structure analysis techniques, in silico approaches are also being widely used. Two computational methods, which are on opposite ends of the spectrum with respect to their modus operandi, i.e., homology modeling and AlphaFold, have been established to provide high-quality structures. Here, a comparative study of the quality of structures either predicted by homology modeling or by AlphaFold is presented based on the characteristics determined by experimental studies using structure validation servers to fulfill the purpose. Although AlphaFold is able to predict high-quality structures, high-confidence parts are sometimes observed to be in disagreement with experimental data. On the other hand, while the structures obtained from homology modeling are successful in incorporating all aspects of the experimental structure used as a template, this method may struggle to accurately model a structure in the absence of a suitable template. In general, although both methods produce high-quality models, the criteria by which they are superior to each other are different and thus discussed in detail. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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26 pages, 15164 KiB  
Article
Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies
by Chunye Zhang, Yuxiang Sui, Shuai Liu and Ming Yang
Pharmaceuticals 2023, 16(10), 1479; https://doi.org/10.3390/ph16101479 - 17 Oct 2023
Cited by 1 | Viewed by 1665
Abstract
The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. [...] Read more.
The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand–protein binding properties and analyze the protein–protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug–protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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Review

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22 pages, 1609 KiB  
Review
Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis
by Sarfaraz K. Niazi and Zamara Mariam
Pharmaceuticals 2024, 17(1), 22; https://doi.org/10.3390/ph17010022 - 22 Dec 2023
Cited by 6 | Viewed by 4263
Abstract
In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting [...] Read more.
In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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