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

Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 26,294

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

De novo Molecular Design with Generative Long Short-term Memory

Grisoni, F., & Schneider, G. (2019) · CHIMIA International Journal for Chemistry · doi:10.2533/chimia.2019.1006 · Cited by 18

Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases

Llorach-Parés, L., Nonell‐Canals, A., Àvila, C., & Sánchez-Martínez, M. (2022) · Marine Drugs · doi:10.3390/md20010053 · Cited by 18

Structure-Based Design and Molecular Dynamics Simulations of Pentapeptide AEYTR as a Potential Acetylcholinesterase Inhibitor

Prasasty, V. D., & Istyastono, E. P. (2020) · Indonesian Journal of Chemistry · doi:10.22146/ijc.46329 · Cited by 18

Neuropharmacological, cytotoxic, and anthelmintic potentials of Lasia spinosa (L.) Thwaites rhizome: In vivo, in vitro, and computational approach

Mohammad, M., Rasel, M., Richi, F. T., Mamun, M. J. I., Ekram, M. E. H., Rabbi, S. A. H., Hossain, S., Hasan, M. A., Sarker, M. F., & Alam, S. (2025) · Pharmacological Research - Natural Products · doi:10.1016/j.prenap.2025.100254 · Cited by 17

Potential Inhibitors of Fascin From A Database of Marine Natural Products: A Virtual Screening and Molecular Dynamics Study

Lin, L., Lin, K. F., Wu, X., Liu, J., Cheng, Y., Xu, L., Li, E., & Dong, G. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.719949 · Cited by 17

A review of approaches in computer-aided drug design in drug discovery

Padole, S. S., Asnani, A. J., Chaple, D., & Katre, S. G. (2022) · GSC Biological and Pharmaceutical Sciences · doi:10.30574/gscbps.2022.19.2.0161 · Cited by 17

Target Identification Approaches in Drug Discovery

Rasul, A., Riaz, A., Sarfraz, I., Khan, S. G., Hussain, G., Zara, R., Sadiqa, A., Bushra, G., Riaz, S., Iqbal, M. J., et al. (2022) · Computer-aided drug discovery and design · doi:10.1007/978-3-030-95895-4_3 · Cited by 19

Achillea fragrantissima (Forssk.) Sch.Bip Flower Dichloromethane Extract Exerts Anti-Proliferative and Pro-Apoptotic Properties in Human Triple-Negative Breast Cancer (MDA-MB-231) Cells: In Vitro and In Silico Studies

Alshuail, N., Alehaideb, Z., Alghamdi, S. S., Suliman, R. S., Al‐Eidi, H., Ali, R., Barhoumi, T., Almutairi, M. S., Alwhibi, M. S., Alghanem, B., et al. (2022) · Pharmaceuticals · doi:10.3390/ph15091060 · Cited by 17

Rational design, synthesis, antifungal evaluation and docking studies of antifungal peptide CGA-N12 analogues based on the target CtKRE9

Li, R., Wu, J., He, F., Xu, Q., Yin, K., Li, S., Li, W., Wei, A., Zhang, L., Zhang, X., et al. (2023) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2023.106355 · Cited by 17

Deep learning in computer-aided drug design: a case study

Ahmad, S., Aslam, D., Ansari, A., Bhat, A. M., & Raza, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22299-3.00012-8 · Cited by 17

Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions

Milon, T. I., Wang, Y., Fontenot, R. L., Khajouie, P., Villinger, F. J., Raghavan, V. V., & Xu, W. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108117 · Cited by 18

Virtual screening in drug discovery: a precious tool for a still-demanding challenge

Rizzuti, B., & Grande, F. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819132-3.00014-2 · Cited by 18

Web-Based Quantitative Structure–Activity Relationship Resources Facilitate Effective Drug Discovery

Wang, Y., Li, J., Shi, X.-X., Wang, Z., Hao, G., & Yang, G. (2021) · Topics in Current Chemistry · doi:10.1007/s41061-021-00349-3 · Cited by 20

In Silico Identification of Possible Inhibitors for Protein Kinase B (PknB) of Mycobacterium tuberculosis

Vieira, T. F., Martins, F., moreira, J., Barbosa, T., & Sousa, S. (2021) · Molecules · doi:10.3390/molecules26206162 · Cited by 13

Computational analysis of Ayurvedic metabolites for potential treatment of drug-resistant Candida auris

Shah, M., Zia, M., Ahmad, I., Khan, M. U., Ejaz, H., Alam, M., Aziz, S., Nishan, U., Dib, H., Ullah, R., et al. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1537872 · Cited by 17

Inhibition of SARS-CoV-2 main protease: a repurposing study that targets the dimer interface of the protein

Pekel, H., Ilter, M., & Şensoy, Ö. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1910571 · Cited by 16

In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions

Shulga, D. A., Ivanov, N. N., & Palyulin, V. A. (2022) · Molecules · doi:10.3390/molecules27061985 · Cited by 16

Computational Drug Repurposing Approach to Identify Novel Inhibitors of ILK Protein for Treatment of Esophageal Squamous Cell Carcinoma

Liu, J., Ma, X., Cao, L., Wei, Y., Gao, Y., Qu, C., Maimaitiming, N., & Zhang, L. (2022) · Journal of Oncology · doi:10.1155/2022/3658334 · Cited by 16

Pharmacophore modeling, 3D-QSAR, and MD simulation-based overture for the discovery of new potential HDAC1 inhibitors

Lanka, G., Banerjee, S., Regula, S., Adhikari, N., & Ghosh, B. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2429020 · Cited by 18

Structure‐based virtual screening aids the identification of glycosyltransferases in the biosynthesis of salidroside

Yao, Y., Chen, F., Wu, C., Chang, X., Cheng, W., Wang, Q., Deng, Z., Liu, T., & Lü, L. (2025) · Plant Biotechnology Journal · doi:10.1111/pbi.70002 · Cited by 16

Computer-Aided Drug Discovery

Lu, P., Bevan, D. R., Leber, A. J., Hontecillas, R., Tubau‐Juni, N., & Bassaganya-Riera, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1_2 · Cited by 16

Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening

Vázquez, J., Deplano, A., Herrero, A., Gibert, E., Herrero, E., & Luque, F. J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b01191 · Cited by 16

Exploring the interaction between Salvia miltiorrhiza and xanthine oxidase: insights from computational analysis and experimental studies combined with enzyme channel blocking

Tang, H., Yang, L., Li, W., Li, J., & Chen, J. (2016) · RSC Advances · doi:10.1039/c6ra24396g · Cited by 16

Conceptual density functional theory in drug discovery: an overview

Rohit, H., & Tandon, H. (2025) · Journal of Molecular Modeling · doi:10.1007/s00894-025-06487-5 · Cited by 16

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein–ligand docking

Kwon, S., & Seok, C. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.047 · Cited by 16

“Dual Anta-Inhibitors” of the A2A Adenosine Receptor and Casein Kinase CK1delta: Synthesis, Biological Evaluation, and Molecular Modeling Studies

Spinaci, A., Buccioni, M., Catarzi, D., Cui, C., Colotta, V., Ben, D. D., Cescon, E., Francucci, B., Grieco, I., Lambertucci, C., et al. (2023) · Pharmaceuticals · doi:10.3390/ph16020167 · Cited by 15

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Hudson, M. L., & Samudrala, R. (2021) · Molecules · doi:10.3390/molecules26092581 · Cited by 16

Using computers to ESKAPE the antibiotic resistance crisis

da Silva, T. H., Hachigian, T., Lee, J., & King, M. D. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.10.005 · Cited by 15

Applications of Molecular Simulation in the Discovery of Antituberculosis Drugs: A Review

Hu, J., Wu, Z., Xie, T., Liu, X., Yan, X. H., Sun, X., Liu, W., Liang, L., He, G., Gan, Y., et al. (2019) · Protein and Peptide Letters · doi:10.2174/0929866526666190620145919 · Cited by 15

The role of human in the loop: lessons from D3R challenge 4

Stroganov, O. V., Novikov, F. N., Medvedev, M. G., Дмитриенко, А. О., Gerasimov, I. S., Svitanko, I. V., & Chilov, G. G. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00291-4 · Cited by 15

Scaffold Hopping in Tuberculosis Drug Discovery: Principles, Applications, and Case Studies

Kovar, O., Kufa, M., Finger, V., Soukup, O., Krátký, M., Torruellas, C., Roh, J., & Korábečný, J. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c01100 · Cited by 14

Viral informatics: bioinformatics-based solution for managing viral infections

Kumar, S., Kumar, G. S., Maitra, S. S., Malý, P., Bharadwaj, S., Sharma, P., & Dwivedi, V. D. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac326 · Cited by 15

Insect protease inhibitors; promising inhibitory compounds against SARS-CoV-2 main protease

Hemmati, S. A., & Tabein, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105228 · Cited by 14

No dance, no partner! A tale of receptor flexibility in docking and virtual screening

Basciu, A., Callea, L., Motta, S., Bonvin, A. M. J. J., Bonati, L., & Vargiu, A. V. (2022) · Annual reports in medicinal chemistry · doi:10.1016/bs.armc.2022.08.006 · Cited by 14

Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches

Li, B., Li, H., Xue, Y. W., Yang, M., Huang, L., Zhang, Z., Liu, J., & Deng, G. (2018) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2018.1492460 · Cited by 14

Structure and Function Studies of Asian Corn Borer Ostrinia furnacalis Pheromone Binding Protein2

Mazumder, S., Dahal, S. R., Chaudhary, B. P., & Mohanty, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-35509-x · Cited by 14

Structure-Based Drug Design Workflow

Hardianto, A., Yusuf, M., Liu, F., & Ranganathan, S. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20104-0 · Cited by 14

Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells

Mesa, A., Orrego, A., Branch, J. W., Mera, C., & Ordúz, S. (2025) · Current Microbiology · doi:10.1007/s00284-025-04346-3 · Cited by 13

Advances in computer-aided drug design: From molecular docking to artificial intelligence-driven therapeutic discovery

Fahim, A. M. (2026) · ASPET Discovery · doi:10.1016/j.aspetd.2026.100024 · Cited by 16

Structure-based design and application of a nucleotide coenzyme mimetic ligand: Application to the affinity purification of nucleotide dependent enzymes

Marinou, M., Platis, D., Ataya, F. S., Chronopoulou, E. G., Vlachakis, D., & Labrou, N. E. (2018) · Journal of Chromatography A · doi:10.1016/j.chroma.2018.01.009 · Cited by 13

Discovery of potent DNMT1 inhibitors against sickle cell disease using structural-based virtual screening, MM-GBSA and molecular dynamics simulation-based approaches

Chandu, A., Joshi, R. P., Gupta, P., Ramalingam, S., & Murugesan, S. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2199081 · Cited by 13

Broadening the scope of WEE1 inhibitors: identifying novel drug candidates via computational approaches and drug repurposing

Chandrasekaran, J., Sivakumaresan, Y., Shankar, K., Dickson, M., kumar, S. L. S., Ramanathan, L., Ahmad, I., & Patel, H. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2251070 · Cited by 13

In silicobinding mechanism prediction of benzimidazole based corticotropin releasing factor-1 receptor antagonists by quantitative structure activity relationship, molecular docking and pharmacokinetic parameters calculation

Kumar, N., Mishra, S. S., Sharma, C. S., Singh, H. P., & Kalra, S. (2017) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2017.1332688 · Cited by 13

Computationally exploring novel xanthine oxidase inhibitors using docking-based 3D-QSAR, molecular dynamics, and virtual screening

Chen, Y., Gao, Y., Wu, F., Luo, X., Ju, X.-L., & Liu, G. (2020) · New Journal of Chemistry · doi:10.1039/d0nj03221b · Cited by 13

2D QSAR, design, docking study and ADMET of some N-aryl derivatives concerning inhibitory activity against Alzheimer disease

Ajala, A., Uzairu, A., Shallangwa, G. A., & Abechi, S. E. (2022) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-022-00420-w · Cited by 13

In silico screening of phenylethanoid glycosides, a class of pharmacologically active compounds as natural inhibitors of SARS-CoV-2 proteases

Cheohen, C. F. D. A. R., Esteves, M. E. A., da Fonseca, T. S., Leal, C. M., Assis, F. D. L. F., Campos, M. F., Rebelo, R. S., Allonso, D., Leitão, G. G., da Silva, M. L., et al. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.02.020 · Cited by 13

Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease

Tomašević, S., Milošević, M., Miličević, B., Simić, V., Prodanović, M., Mijailovich, S. M., & Filipović, N. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15030793 · Cited by 13

Tailored Modeling of Rivastigmine Derivatives as Dual Acetylcholinesterase and Butyrylcholinesterase Inhibitors for Alzheimer's Disease Treatment

Adeowo, F. Y., Oyetunji, T. P., Ejalonibu, M. A., Ndagi, U., Kumalo, H. M., & Lawal, M. M. (2021) · Chemistry & Biodiversity · doi:10.1002/cbdv.202100361 · Cited by 12

Structure-based virtual screening of anti-breast cancer compounds from Artemisia absinthium —insights through molecular docking, pharmacokinetics, and molecular dynamic simulations

Gnanaselvan, S., Yadav, S. A., & Manoharan, S. P. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2212805 · Cited by 12

Combined Structure- and Ligand-Based Approach for the Identification of Inhibitors of AcrAB-TolC in Escherichia coli

Pisoni, L. A., Semple, S. J., Liu, S., Sykes, M. J., & Venter, H. (2023) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.3c00350 · Cited by 12

Discovery of Novel Pyrazole Acyl Thiourea Skeleton Analogue as Potential Herbicide Candidates

Ma, C., Tian, L., Wang, Y., Huo, J., An, Z., Sun, S., Kou, S., Wang, W., Li, Y., Zhang, J., et al. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.3c08863 · Cited by 12

Universal neural network potentials as descriptors: towards scalable chemical property prediction using quantum and classical computers

Shiota, T., Ishihara, K., & Mizukami, W. (2024) · Digital Discovery · doi:10.1039/d4dd00098f · Cited by 14

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

Rasul, H. O., Ghafoor, D. D., Aziz, B. K., Hassan, B. A., Rashid, T. A., & Kıvrak, A. (2024) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-024-05110-2 · Cited by 16

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study

Masand, V. H., Akasapu, S., Gandhi, A., Rastija, V., & Patil, M. K. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.104172 · Cited by 12

Directionally Modified Fluorophores for Super-Resolution Imaging of Target Enzymes: A Case Study with Carboxylesterases

Jia, Y., Wang, J., Li, P., Ma, X., & Han, K. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01469 · Cited by 12

New lead compounds identification against KRas mediated cancers through pharmacophore-based virtual screening and in vitro assays

Ghufran, M., Rehman, A. U., Ayaz, M., Ul‐Haq, Z., Uddin, R., Azam, S. S., & Wadood, A. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2022.2128878 · Cited by 12

Pyrazoline Scaffold: Hit Identification to Lead Synthesis and Biological Evaluation As Antidiabetic Agents

Sharma, S., Ansari, M. H. R., Sharma, K., Singh, R. K., Ali, S., Alam, M. M., Zaman, M. S., Alam, P., & Akhter, M. (2023) · Future Medicinal Chemistry · doi:10.4155/fmc-2022-0141 · Cited by 12

Molecular docking-based virtual screening, drug-likeness, and pharmacokinetic profiling of some anti-Salmonella typhimurium cephalosporin derivatives

Ameji, P. J., Uzairu, A., Shallangwa, G. A., & Uba, S. (2023) · Journal of Taibah University Medical Sciences · doi:10.1016/j.jtumed.2023.05.021 · Cited by 12

In Silico and In Vitro Identification of P-Glycoprotein Inhibitors from a Library of 375 Phytochemicals

Schäfer, J., Klösgen, V. J., Omer, E. A., Kadioglu, O., Mbaveng, A. T., Kuete, V., Hildebrandt, A., & Efferth, T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241210240 · Cited by 12

Identification of Potential Tryptase Inhibitors from FDA-Approved Drugs Using Machine Learning, Molecular Docking, and Experimental Validation

Yasir, M., Park, J., Han, E., Park, W. S., Han, J., & Chun, W. (2024) · ACS Omega · doi:10.1021/acsomega.4c04886 · Cited by 12

Immune Network Technology on the Basis of Random Forest Algorithm for Computer-Aided Drug Design

Samigulina, G. A., & Zarina, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56148-6_4 · Cited by 12

Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt

Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie · doi:10.1002/ange.201909987 · Cited by 12

Balanced QSAR analysis to identify the structural requirements of ABBV-075 (Mivebresib) analogues as bromodomain and extraterminal domain (BET) family bromodomain inhibitor

Masand, V. H., Patil, M. K., El-Sayed, N. N. E., Zaki, M. E. A., Almarhoon, Z. M., & Al‐Hussain, S. A. (2020) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2020.129597 · Cited by 12

Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands

Pérez‐Castillo, Y., Helguera, A. M., Cordeiro, M. N. D. S., Tejera, E., Paz‐y‐Miño, C., Sánchez‐Rodríguez, A., Borges, F., & Cruz‐Monteagudo, M. (2017) · Current Neuropharmacology · doi:10.2174/1570159x15666170109143757 · Cited by 12

From Computers to Bedside: Computational Chemistry Contributing to FDA Approval

Athanasiou, C., & Cournia, Z. (2018) · Methods and principles in medicinal chemistry · doi:10.1002/9783527806836.ch7 · Cited by 12

The role of nuclear receptors in metabolic homeostasis and disease: From molecular mechanisms to drug discovery

He, F., Wu, H., Zhao, F., Liu, X., Yang, Q., Zhou, S., Xu, T., Xu, J., Wang, S., & Zou, D. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.107856 · Cited by 12

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Hellemann, E., & Durrant, J. D. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00478 · Cited by 12

Novel dual-targeting inhibitors of NSD2 and HDAC2 for the treatment of liver cancer: structure-based virtual screening, molecular dynamics simulation, and in vitro and in vivo biological activity evaluations

Jin, X. Y., Wang, Y., Chen, J., Niu, M., Yang, Y., Zhang, Q., & Bao, G. (2023) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.1080/14756366.2023.2289355 · Cited by 11

Computer-aided drug discovery of natural antiviral metabolites as potential SARS-CoV-2 helicase inhibitors

Elkaeed, E. B., Eissa, I. H., Saleh, A. M., Alsfouk, B. A. A., & Metwaly, A. M. (2024) · Journal of Chemical Research · doi:10.1177/17475198231221253 · Cited by 11

Cheminformatics-based analysis identified (Z)-2-(2,5-dimethoxy benzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one as an inhibitor of Marburg replication by interacting with NP

Siddiquee, N. H., Talukder, M. E. K., Ahmed, E., Zeba, L. T., Aivy, F. S., Rahman, M. H., Barua, D., Rumman, R., Hossain, M. I., Shimul, M. E. K., et al. (2024) · Microbial Pathogenesis · doi:10.1016/j.micpath.2024.106892 · Cited by 11

Tripeptides inhibit dual targets AChE and BACE-1: a computational study

Tuan, A., Nguyen, T. H., Quân, P. M., Nguyen, H. T., Long, N. P., Vu, V. V., Phùng, H. T. T., & Ngô, S. T. (2025) · RSC Advances · doi:10.1039/d5ra00709g · Cited by 11

A formally exact method for high-throughput absolute binding-free-energy calculations

Bian, H., Shao, X., Chipot, C. J., Cai, W., & Fu, H. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00821-w · Cited by 12

Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A 2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors

Helguera, A. M., Pérez‐Castillo, Y., Cordeiro, M. N. D. S., Tejera, E., Paz‐y‐Miño, C., Sánchez‐Rodríguez, A., Teijeira, M., Ancede‐Gallardo, E., Cagide, F., Borges, F., et al. (2016) · Current Pharmaceutical Design · doi:10.2174/1381612822666160302103542 · Cited by 11

Investigation of novel chemical scaffolds targeting prolyl oligopeptidase for neurological therapeutics

Kumar, R., Saravanan, P., Bavi, R., Baek, A., Son, M., Rampogu, S. D., Park, C., Lee, G., Zeb, A., Parate, S., et al. (2018) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2018.12.006 · Cited by 11

Old Drugs for New Purpose—Fast Pace Therapeutic Identification for SARS‐CoV ‐2 Infections by Pharmacophore Guided Drug Repositioning Approach

Rampogu, S. D., & Lee, K. W. (2021) · Bulletin of the Korean Chemical Society · doi:10.1002/bkcs.12171 · Cited by 11

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

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