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16 pages, 3876 KiB

Open AccessArticle

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches

by Amar Ajmal, Muhammad Danial, Maryam Zulfat, Muhammad Numan, Sidra Zakir, Chandni Hayat, Khulood Fahad Alabbosh, Magdi E. A. Zaki, Arif Ali and Dongqing Wei

Pharmaceuticals 2024, 17(5), 551; https://doi.org/10.3390/ph17050551 - 25 Apr 2024

Viewed by 366

Abstract

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is [...] Read more.

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)

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25 pages, 13165 KiB

Open AccessArticle

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 645

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

Open AccessArticle

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 956

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

Open AccessArticle

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 732

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

Open AccessArticle

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 899

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

Open AccessArticle

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 1027

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

Open AccessArticle

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 780

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

Open AccessArticle

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 1339

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

Open AccessArticle

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 811

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

Open AccessArticle

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 1209

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

Open AccessArticle

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 1758

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

Open AccessReview

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 4812

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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