Role of computer-aided drug design in modern drug discovery

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

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AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139

Scalable molecular dynamics with NAMD

Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914

Molecular properties that influence the oral bioavailability of drug candidates

Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420

GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit

Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M., Smith, J., Kasson, P., van der Spoel, D., et al. (2013) · Bioinform. · doi:10.1093/bioinformatics/btt055 · Cited by 7,255

The price of innovation: new estimates of drug development costs

DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003) · Journal of Health Economics · doi:10.1016/s0167-6296(02)00126-1 · Cited by 4,265

New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays

Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 4,196

Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins

Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931

How to improve R&D productivity: the pharmaceutical industry's grand challenge

Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566

Novel poly (2-oxazoline) block copolymer with aromatic heterocyclic side chains as a drug delivery platform

Hwang, D., Ramsey, J. D., Makita, N., Sachse, C., Jordan, R., Sokolsky-Papkov, M., & Kabanov, A. V. (2019) · Journal of Controlled Release · doi:10.1016/j.jconrel.2019.06.037 · Cited by 40

New strategies to enhance the efficiency and precision of drug discovery

An, Q., Huang, L., Wang, C., Wang, D., & Tu, Y. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1550158 · Cited by 45

Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening

Sirous, H., Campiani, G., Calderone, V., & Brogi, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104808 · Cited by 38

Phytoconstituents of traditional Himalayan Herbs as potential inhibitors of Human Papillomavirus (HPV-18) for cervical cancer treatment: An In silico Approach

Salaria, D., Rolta, R., Mehta, J., Awofisayo, O., Fadare, O. A., Kaur, B., Kumar, B., da Costa, R. A., Chandel, S. R., Kaushik, N., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265420 · Cited by 39

Protein structure and computational drug discovery

Nero, T. L., Parker, M. W., & Morton, C. J. (2018) · Biochemical Society Transactions · doi:10.1042/bst20180202 · Cited by 39

The emerging potential of interactive virtual reality in drug discovery

Walters, R. K., Gale, E., Barnoud, J., Glowacki, D. R., & Mulholland, A. J. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2079632 · Cited by 44

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Li, L., Liu, S., Bi, W., Liu, F., Xu, S., Li, P., & Chen, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241813953 · Cited by 37

Assessing multiple score functions in Rosetta for drug discovery

Smith, S. T., & Meiler, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240450 · Cited by 40

Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

Martins, F. G., Melo, A., & Sousa, S. F. (2021) · Molecules · doi:10.3390/molecules26092600 · Cited by 37

A large-scale transcriptional analysis reveals herb-derived ginsenoside F2 suppressing hepatocellular carcinoma via inhibiting STAT3

Tan, X., Ma, X., Dai, Y., An, J., Yu, X., Li, S., Liao, Y., Pei, T., Tang, Y., Gui, Y., et al. (2023) · Phytomedicine · doi:10.1016/j.phymed.2023.155031 · Cited by 35

Evaluation of potency of the selected bioactive molecules from Indian medicinal plants with MPro of SARS-CoV-2 through in silico analysis

Halder, P., Pal, U., Paladhi, P., Dutta, S., Paul, P., Pal, S., Das, D., Ganguly, A., Dutta, I., Mandal, S., et al. (2021) · Journal of Ayurveda and Integrative Medicine · doi:10.1016/j.jaim.2021.05.003 · Cited by 35

Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors

Masand, V. H., El-Sayed, N. N. E., Bambole, M. U., Patil, V. R., & Thakur, S. D. (2018) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2018.07.080 · Cited by 33

Discovery of potent inhibitors for SARS-CoV-2's main protease by ligand-based/structure-based virtual screening, MD simulations, and binding energy calculations

Abu‐Saleh, A. A. A., Awad, I. E., Yadav, A., & Poirier, R. A. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp04326e · Cited by 32

Advances in machine intelligence‐driven virtual screening approaches for big‐data

Kumar, N., & Acharya, V. (2023) · Medicinal Research Reviews · doi:10.1002/med.21995 · Cited by 36

Screening of Potent Phytochemical Inhibitors Against SARS-CoV-2 Main Protease: An Integrative Computational Approach

Mahmud, S., Hasan, M. R., Biswas, S., Paul, G. K., Afrose, S., Mita, M. A., Shimu, M. S. S., Promi, M. M., Hani, U., Rahamathulla, M., et al. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.717141 · Cited by 33

Pharmacophore modeling and its applications

Tyagi, R., Singh, A., Chaudhary, K. K., & Yadav, M. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89775-4.00009-2 · Cited by 37

Fragmented blind docking: a novel protein–ligand binding prediction protocol

Grasso, G., Gregorio, A. D., Mavkov, B., Piga, D., Labate, G. F. D., Danani, A., & Deriu, M. A. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1988709 · Cited by 34

Exploration of plant-derived natural polyphenols toward COVID-19 main protease inhibitors: DFT, molecular docking approach, and molecular dynamics simulations

Ma, Y., Tao, Y., Qu, H., Wang, C., Yan, F., Gao, X., & Zhang, M. (2022) · RSC Advances · doi:10.1039/d1ra07364h · Cited by 32

Virtual screening of potential inhibitor against breast cancer-causing estrogen receptor alpha (ERα): molecular docking and dynamic simulations

Muhammad, S., Saba, A., Khera, R. A., Al-Sehemi, A. G., Algarni, H., Javed Iqbal, M., Alshahrani, M. Y., & Chaudhry, A. R. (2022) · Molecular Simulation · doi:10.1080/08927022.2022.2072840 · Cited by 32

Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development

Ataeinia, B., & Heidari, P. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.008 · Cited by 31

DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model

Xie, J., Chen, S., Lei, J., & Yang, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01466 · Cited by 31

Modern Tools and Techniques in Computer-Aided Drug Design

Anwar, T., Kumar, P., & Khan, A. U. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00011-4 · Cited by 34

Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs

Duay, S. S., Yap, R. C. Y., Gaitano, A. L., Santos, J. A. A., & Macalino, S. J. Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24119289 · Cited by 33

COVID-19: CADD to the rescue

Onawole, A. T., Sulaiman, K. O., Kolapo, T. U., Akinde, F. O., & Adegoke, R. O. (2020) · Virus Research · doi:10.1016/j.virusres.2020.198022 · Cited by 30

Design of Inhibitors of the Intrinsically Disordered Protein NUPR1: Balance between Drug Affinity and Target Function

Rizzuti, B., Lan, W., Santofimia‐Castaño, P., Zhou, Z., Velázquez‐Campoy, A., Abián, O., Peng, L., Neira, J. L., Xia, Y., & Iovanna, J. (2021) · Biomolecules · doi:10.3390/biom11101453 · Cited by 30

Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

Brown, B. P., Vu, O., Geanes, A. R., Kothiwale, S., Butkiewicz, M., Lowe, E. W., Mueller, R., Pape, R., Mendenhall, J., & Meiler, J. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.833099 · Cited by 30

Targeting Glutaminase by Natural Compounds: Structure-Based Virtual Screening and Molecular Dynamics Simulation Approach to Suppress Cancer Progression

Tabrez, S. S. M., Zughaibi, T. A., Hoque, M., Suhail, M., Khan, M. I., & Khan, A. U. (2022) · Molecules · doi:10.3390/molecules27155042 · Cited by 30

Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation

Wang, Z., Sun, L., Xu, Y., Liang, P., Xu, K., & Huang, J. L. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04443-6 · Cited by 29

Marine natural compounds as potential CBP bromodomain inhibitors for treating cancer: an in-silico approach using molecular docking, ADMET, molecular dynamics simulations and MM-PBSA binding free energy calculations

Ali, M. L., Noushin, F., Azme, E., Hasan, M. M., Hoque, N., & Metu, A. F. (2024) · In Silico Pharmacology · doi:10.1007/s40203-024-00258-5 · Cited by 29

Computational investigation of imidazopyridine analogs as protein kinase B (Akt1) allosteric inhibitors by using 3D-QSAR, molecular docking and molecular dynamics simulations

Gu, X., Wang, Y., Wang, M., Wang, J., & Li, N. (2019) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2019.1705185 · Cited by 29

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules

Kognole, A. A., Hazel, A. J., & MacKerell, A. D. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00381 · Cited by 28

ChemTSv2 : Functional molecular design using de novo molecule generator

Ishida, S., Aasawat, T. K., Sumita, M., Katouda, M., Yoshizawa, T., Yoshizoe, K., Tsuda, K., & Terayama, K. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1680 · Cited by 32

Histamine H3 receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects

Ghamari, N., Zarei, O., Reiner‐Link, D., Dastmalchi, S., Stark, H., & Hamzeh‐Mivehroud, M. (2018) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13471 · Cited by 29

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

Masand, V. H., Rastija, V., Patil, M. K., Gandhi, A., & Chapolikar, A. (2020) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2020.1784271 · Cited by 28

Cytotoxic Potential of Bioactive Compounds from Aspergillus flavus, an Endophytic Fungus Isolated from Cynodon dactylon, against Breast Cancer: Experimental and Computational Approach

Kalimuthu, A. K., Pavadai, P., Theivendren, P., Babkiewicz, E., Pijanowska, J., Mrówka, P., Rajagopal, G., Deepak, V., Sundar, K., Maszczyk, P., et al. (2022) · Molecules · doi:10.3390/molecules27248814 · Cited by 31

A rhodanine derivative as a potential antibacterial and anticancer agent: Crystal structure, spectral characterization, DFT calculations, Hirshfeld surface analysis, in silico molecular docking and ADMET studies

Guerraoui, A., Goudjil, M., Direm, A., Guerraoui, A., Şengün, İ. Y., Parlak, C., Djedouani, A., Chelazzi, L., Monti, F., Lunedei, E., et al. (2023) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2023.135025 · Cited by 28

Anesthetic drug discovery with computer-aided drug design and machine learning

Liu, X., Xue, Z., Luo, M., Ke, B., & Lv, J. (2024) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-023-00047-x · Cited by 30

Breakthroughs in AI and multi-omics for cancer drug discovery: A review

Fatima, I., Rehman, A., Ding, Y., Wang, P., Meng, Y., Rehman, H. U., Warraich, D. A., Wang, Z., Feng, L., & Liao, M. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.116925 · Cited by 29

Nature’s defense against emerging neurodegenerative threats: Dynamic simulation, PCA, DCCM identified potential plant-based antiviral lead targeting borna disease virus nucleoprotein

Siddiquee, N. H., Hossain, M. I., Priya, F. M., Azam, S. B., Talukder, M. E. K., Barua, D., Malek, S., Saha, N., Muntaha, S., Paul, R., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310802 · Cited by 27

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

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Molecular docking and molecular dynamic simulation approaches for drug development and repurposing of drugs for severe acute respiratory syndrome-Coronavirus-2

Subhaswaraj, P., & Busi, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91172-6.00007-8 · Cited by 29

Chemoinformatics Strategies for Leishmaniasis Drug Discovery

Ferreira, L. L. G., & Andricopulo, A. D. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01278 · Cited by 26

Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design

Mangione, W., & Samudrala, R. (2019) · Molecules · doi:10.3390/molecules24010167 · Cited by 25

Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods

Vita, S. D., Lauro, G., Ruggiero, D., Terracciano, S., Riccio, R., & Bifulco, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00428 · Cited by 27

SAR and QSAR research on tyrosinase inhibitors using machine learning methods

Wu, Y., Huo, D., Chen, G., & Yan, A. (2021) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2020.1862297 · Cited by 28

Polyphenolic Natural Products Active In Silico Against SARS-CoV-2 Spike Receptor Binding Domains and Non-structural Proteins - A Review

Macabeo, A. P. G., Quimque, M. T. J., Notarte, K. I., Adviento, X. A., Cabunoc, M. H., de Leon, V. N., Reyes, F. S. L. G. D., Lugtu, E. J., Manzano, J. A., Monton, S. N., et al. (2021) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207325666210917113207 · Cited by 26

The prediction of protein–ligand unbinding for modern drug discovery

Zhang, Q., Zhao, N., Meng, X., Yu, F., Yao, X., & Liu, H. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2002298 · Cited by 27

Efficient and enhanced sampling of drug‐like chemical space for virtual screening and molecular design using modern machine learning methods

Goel, M., Aggarwal, R., Sridharan, B., Pal, P. K., & Priyakumar, U. D. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1637 · Cited by 27

Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening

Yu, L., He, X. D., Fang, X., Liu, L., & Liu, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01371 · Cited by 26

Marine-Derived Furanones Targeting Quorum-Sensing Receptors in Pseudomonas aeruginosa : Molecular Insights and Potential Mechanisms of Inhibition

Boakye, A., Seidu, M. P., Adomako, A., Laryea, M. K., & Borquaye, L. S. (2024) · Bioinformatics and Biology Insights · doi:10.1177/11779322241275843 · Cited by 25

Discovery of natural MCL1 inhibitors using pharmacophore modelling, QSAR, docking, ADMET, molecular dynamics, and DFT analysis

Das, U., Chanda, T., Kumar, J., & Peter, A. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108427 · Cited by 25

Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular dynamic simulations

Wang, Y., Feng, S., Gao, H., & Wang, J. (2019) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2019.1608305 · Cited by 25

In Silico Approach in Drug Design and Drug Discovery: An Update

Jabalia, N., Kumar, A., Kumar, V., & Rani, R. (2021) · Springer eBooks · doi:10.1007/978-981-15-8936-2_10 · Cited by 25

Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy

Alshehri, F. F. (2023) · Saudi Pharmaceutical Journal · doi:10.1016/j.jsps.2023.101835 · Cited by 24

Exploring the interaction between Salvia miltiorrhiza and α-glucosidase: insights from computational analysis and experimental studies

Tang, H., Zhao, D., & Xue, Z. (2018) · RSC Advances · doi:10.1039/c8ra04772c · Cited by 24

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Pavan, M., & Moro, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24054401 · Cited by 25

Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction

Sabando, M. V., Ponzoni, I., & Soto, A. J. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105777 · Cited by 23

Molecular Modeling Study of c-KIT/PDGFRα Dual Inhibitors for the Treatment of Gastrointestinal Stromal Tumors

Keretsu, S., Ghosh, S., & Cho, S. J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218232 · Cited by 23

Identification of Novel and Potent Indole-Based Benzenesulfonamides as Selective Human Carbonic Anhydrase II Inhibitors: Design, Synthesis, In Vitro, and In Silico Studies

Elkamhawy, A., Woo, J., Nada, H., Angeli, A., Bedair, T. M. I., Supuran, C. T., & Lee, K. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052540 · Cited by 23

Taxifolin and Lucidin as Potential E6 Protein Inhibitors: p53 Function Re-Establishment and Apoptosis Induction in Cervical Cancer Cells

Gomes, D., Yaduvanshi, S., Silvestre, S., Duarte, A. P., Santos, A. O., Soares, C. P., Kumar, V., Passarinha, L. A., & Sousa, Â. (2022) · Cancers · doi:10.3390/cancers14122834 · Cited by 23

Discovery of Novel HIV Protease Inhibitors Using Modern Computational Techniques

Okafor, S. N., Angsantikul, P., & Ahmed, H. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232012149 · Cited by 23

A flexible data-free framework for structure-based de novo drug design with reinforcement learning

Du, H., Jiang, D., Zhang, O., Wu, Z., Gao, J., Zhang, X., Wang, X., Deng, Y., Kang, Y., Li, D., et al. (2023) · Chemical Science · doi:10.1039/d3sc04091g · Cited by 24

Anti-Melanogenic Activity of Ethanolic Extract from Garcinia atroviridis Fruits Using In Vitro Experiments, Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation

Tedasen, A., Chiabchalard, A., Tencomnao, T., Yamasaki, K., Majima, H. J., Phongphithakchai, A., & Chatatikun, M. (2024) · Antioxidants · doi:10.3390/antiox13060713 · Cited by 23

Multi-Functional Diarylurea Small Molecule Inhibitors of TRPV1 with Therapeutic Potential for Neuroinflammation

Feng, Z., Pearce, L. V., Zhang, Y., Xing, C., Herold, B. K. A., Ma, S., Hu, Z., Turcios, N. A., Yang, P., Tong, Q., et al. (2016) · The AAPS Journal · doi:10.1208/s12248-016-9888-z · Cited by 23

Molecular modelling studies of 3,5-dipyridyl-1,2,4-triazole derivatives as xanthine oxidoreductase inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamic simulations

Tang, H., Yang, L., Li, J., & Chen, J. (2016) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2016.09.018 · Cited by 23

Lead identification and optimization

Arya, H., & Coumar, M. S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821471-8.00004-0 · Cited by 22

Discovering potent inhibitors against the Mpro of the SARS-CoV-2. A medicinal chemistry approach

Mehmood, A., Nawab, S., Wang, Y., Xue, J., Kaushik, A. C., & Wei, D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105235 · Cited by 22

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Alzain, A. A., Elbadwi, F. A., Shoaib, T. H., Sherif, A. E., Osman, W., Ashour, A. A., Mohamed, G. A., Ibrahim, S. R. M., Roh, E. J., & Hassan, A. H. E. (2024) · Frontiers in Chemistry · doi:10.3389/fchem.2024.1339891 · Cited by 22

The role of physicochemical and topological parameters in drug design

Darlami, J., & Sharma, S. (2024) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2024.1424402 · Cited by 21

Novel potential inhibitor discovery against tyrosyl-tRNA synthetase fromStaphylococcus aureusby virtual screening, molecular dynamics, MMPBSA and QMMM simulations

Farshadfar, C., Mollica, A., Rafii, F., Noorbakhsh, A., Nikzad, M., Seyedi, S. H., Abdi, F., Verki, S. A., & Mirzaie, S. (2020) · Molecular Simulation · doi:10.1080/08927022.2020.1726911 · Cited by 21

Computer-Aided Drug Designing

Rajkishan, T., Rachana, A., Shruti, S., Patel, B., & Patel, D. (2021) · Springer eBooks · doi:10.1007/978-981-33-6191-1_9 · Cited by 21

Synthesis of hydrazine containing piperazine or benzimidazole derivatives and their potential as α-amylase inhibitors by molecular docking, inhibition kinetics and in vitro cytotoxicity activity studies

Çakmak, Ü., Tuncay, F. Ö., Özdemir, S. B., Demir, E. A., Demir, İ., Çolak, A., Uzuner, S. Ç., Erdem, S. S., & Yıldırım, N. (2021) · Medicinal Chemistry Research · doi:10.1007/s00044-021-02785-8 · Cited by 21

Identification of novel Plasmodium falciparum dihydroorotate dehydrogenase inhibitors for malaria using in silico studies

Alzain, A. A., Ahmed, Z. A. M., Mahadi, M. A., Khairy, E. A., & Elbadwi, F. A. (2022) · Scientific African · doi:10.1016/j.sciaf.2022.e01214 · Cited by 23

Advances in schistosomiasis drug discovery based on natural products.

Mtemeli, F. L., Ndlovu, J., Mugumbate, G., Makwikwi, T., & Shoko, R. (2022) · All Life · doi:10.1080/26895293.2022.2080281 · Cited by 21

Natural polymers as potential P-glycoprotein inhibitors: Pre-ADMET profile and computational analysis as a proof of concept to fight multidrug resistance in cancer

Gandla, K., Islam, F., Zehravi, M., Karunakaran, A., Sharma, I., Haque, M. A., Kumar, S., Pratyush, K., Dhawale, S. A., Nainu, F., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19454 · Cited by 26

Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents

Askari, S., Ghofrani, A., & Taherdoost, H. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3040070 · Cited by 22

Field-Template, QSAR, Ensemble Molecular Docking, and 3D-RISM Solvation Studies Expose Potential of FDA-Approved Marine Drugs as SARS-CoVID-2 Main Protease Inhibitors

Kalhotra, P., Chittepu, V. C. S. R., Osorio-Revilla, G. I., & Gallardo‐Velázquez, T. (2021) · Molecules · doi:10.3390/molecules26040936 · Cited by 21

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