Role of computer-aided drug design in modern drug discovery

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Scalable molecular dynamics with NAMD

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Molecular properties that influence the oral bioavailability of drug candidates

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

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The price of innovation: new estimates of drug development costs

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

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QSAR Modeling: Where have you been? Where are you going to?

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LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters

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A Critical Assessment of Docking Programs and Scoring Functions

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Identifying and Characterizing Binding Sites and Assessing Druggability

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CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues

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Fpocket: An open source platform for ligand pocket detection

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A ‘Rule of Three’ for fragment-based lead discovery?

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Impact of high-throughput screening in biomedical research

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Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

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PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results

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Target identification and mechanism of action in chemical biology and drug discovery

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Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites

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The Process of Structure-Based Drug Design

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Integration of virtual and high-throughput screening

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Comparative evaluation of 11 scoring functions for molecular docking.

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The Organic Chemistry of Drug Design and Drug Action

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Virtual Screening Strategies in Drug Discovery: A Critical Review

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Role of the Active-Site Solvent in the Thermodynamics of Factor Xa Ligand Binding

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PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching

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Diverse, High-Quality Test Set for the Validation of Protein−Ligand Docking Performance

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Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

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Fast prediction and visualization of protein binding pockets with PASS

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Comparative Assessment of Scoring Functions on a Diverse Test Set

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Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.

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MetaSite: Understanding Metabolism in Human Cytochromes from the Perspective of the Chemist

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Flexible ligand docking to multiple receptor conformations: a practical alternative

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LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation.

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Molecular Docking and High-Throughput Screening for Novel Inhibitors of Protein Tyrosine Phosphatase-1B

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Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure

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Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy

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Recognizing Pitfalls in Virtual Screening: A Critical Review

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RosettaLigand Docking with Full Ligand and Receptor Flexibility

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A detergent-based assay for the detection of promiscuous inhibitors

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“Soft docking”: Matching of molecular surface cubes

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MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories

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Recent advances in computer-aided drug design

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Preclinical safety evaluation of biotechnology-derived pharmaceuticals

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Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction

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SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism

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An improved relaxed complex scheme for receptor flexibility in computer-aided drug design

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Classification of Current Scoring Functions

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Structure-based drug design to augment hit discovery

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The Organic Chemistry of Drug Design and Drug Action

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Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context

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Challenges, Applications, and Recent Advances of Protein-Ligand Docking in Structure-Based Drug Design

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Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries

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Effect of Detergent on “Promiscuous” Inhibitors

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Allosite: a method for predicting allosteric sites

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Topomer CoMFA: A Design Methodology for Rapid Lead Optimization

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Target Identification in Chemical Genetics

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Ensemble-Based Virtual Screening Reveals Potential Novel Antiviral Compounds for Avian Influenza Neuraminidase

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Do Structurally Similar Ligands Bind in a Similar Fashion?

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The holistic integration of virtual screening in drug discovery

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Potential and Limitations of Ensemble Docking

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Structure-Based Drug Design Strategies in Medicinal Chemistry

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Ligand−Protein Docking with Water Molecules

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How far can virtual screening take us in drug discovery?

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DrugScore Meets CoMFA: Adaptation of Fields for Molecular Comparison (AFMoC) or How to Tailor Knowledge-Based Pair-Potentials to a Particular Protein

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FLIPDock: Docking flexible ligands into flexible receptors

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Emerging Methods for Ensemble-Based Virtual Screening

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Recent Advances in Multidimensional QSAR (4D-6D): A Critical Review

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Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening

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Identifying targets for drug discovery using bioinformatics

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The Promises and Challenges of Toxico-Epigenomics: Environmental Chemicals and Their Impacts on the Epigenome

Chung, F. F., & Herceg, Z. (2020) · Environmental Health Perspectives · doi:10.1289/ehp6104 · Cited by 86 · see citing works

Structure Based Design and Molecular Docking Studies for Phosphorylated Tau Inhibitors in Alzheimer’s Disease

Pradeepkiran, J. A., & Reddy, P. H. (2019) · Cells · doi:10.3390/cells8030260 · Cited by 82 · see citing works

A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

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Strategies to apply 3Rs in preclinical testing

Gorzalczany, S. B., & Basso, A. (2021) · Pharmacology Research & Perspectives · doi:10.1002/prp2.863 · Cited by 80 · see citing works

In Silico Strategies in Tuberculosis Drug Discovery

Macalino, S. J. Y., Billones, J. B., Organo, V. G., & Carrillo, M. C. O. (2020) · Molecules · doi:10.3390/molecules25030665 · Cited by 81 · see citing works

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer

Aziz, M., Ejaz, S. A., Zargar, S., Akhtar, N., Aborode, A. T., Wani, T. A., Batiha, G. E., Siddique, F., Alqarni, M., & Akintola, A. A. (2022) · Molecules · doi:10.3390/molecules27134098 · Cited by 78 · see citing works

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Lee, J. W., Maria‐Solano, M. A., Vu, T. N. L., Yoon, S.-H., & Choi, S. (2022) · Biochemical Society Transactions · doi:10.1042/bst20211240 · Cited by 77 · see citing works

Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation

Samad, A., Ajmal, A., Mahmood, A., Khurshid, B., Li, P., Jan, S. M., Rehman, A. U., He, P., Abdalla, A. N., Umair, M., et al. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1060076 · Cited by 76 · see citing works

Multidisciplinary approaches for enzyme biocatalysis in pharmaceuticals: protein engineering, computational biology, and nanoarchitectonics

Kim, S., Ga, S., Bae, H., Sluyter, R., Konstantinov, K., Shrestha, L. K., Kim, J. H., Kim, J. H., & Ariga, K. (2023) · EES Catalysis · doi:10.1039/d3ey00239j · Cited by 73 · see citing works

New tricks for human farnesyltransferase inhibitor: cancer and beyond

Wang, J., Yao, X., & Huang, J. Y. (2017) · MedChemComm · doi:10.1039/c7md00030h · Cited by 73 · see citing works

Evaluation of potential anti-RNA-dependent RNA polymerase (RdRP) drugs against the newly emerged model of COVID-19 RdRP using computational methods

Poustforoosh, A., Hashemipour, H., Tüzün, B., Pardakhty, A., Mehrabani, M., & Nematollahi, M. H. (2021) · Biophysical Chemistry · doi:10.1016/j.bpc.2021.106564 · Cited by 72 · see citing works

Study of combining virtual screening and antiviral treatments of the Sars-CoV-2 (Covid-19)

Khodadadi, E., Maroufi, P., Khodadadi, E., Esposito, I., Ganbarov, K., Espsoito, S., Yousefi, M., Zeinalzadeh, E., & Kafil, H. S. (2020) · Microbial Pathogenesis · doi:10.1016/j.micpath.2020.104241 · Cited by 65 · see citing works

Structure based virtual screening: Fast and slow

Varela‐Rial, A., Majewski, M., & Fabritiis, G. D. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1544 · Cited by 65 · see citing works

Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology

Cho, H., & Choi, I. S. (2019) · ChemMedChem · doi:10.1002/cmdc.201900458 · Cited by 66 · see citing works

PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms

Yan, X., Lu, Y., Li, Z., Wei, Q., Gao, X., Wang, S., Wu, S., & Cui, S. R. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01512 · Cited by 62 · see citing works

Therapeutic strategies of dual-target small molecules to overcome drug resistance in cancer therapy

Ye, J., Wu, J., & Liu, B. (2023) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2023.188866 · Cited by 63 · see citing works

Demonstration of AutoDock as an Educational Tool for Drug Discovery

Helgren, T. R., & Hagen, T. J. (2017) · Journal of Chemical Education · doi:10.1021/acs.jchemed.6b00555 · Cited by 59 · see citing works

Rapid and accurate estimation of protein–ligand relative binding affinities using site-identification by ligand competitive saturation

Goel, H., Hazel, A. J., Ustach, V. D., Jo, S., Yu, W., & MacKerell, A. D. (2021) · Chemical Science · doi:10.1039/d1sc01781k · Cited by 57 · see citing works

Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction

Wee, J., & Xia, K. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01415 · Cited by 59 · see citing works

Sample efficient reinforcement learning with active learning for molecular design

Dodds, M., Guo, J., Löhr, T., Tibo, A., Engkvist, O., & Janet, J. P. (2024) · Chemical Science · doi:10.1039/d3sc04653b · Cited by 55 · see citing works

Sulfide regulation and catabolism in health and disease

Hou, Y., Lv, B., Du, J., Ye, M., Jin, H., Yi, Y., & Huang, Y. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02231-w · Cited by 56 · see citing works

Recent Advances in Quantum Computing for Drug Discovery and Development

Wang, P.-H., Chen, J.-H., Yang, Y.-Y., Lee, C., & Tseng, Y. J. (2023) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2023.3249499 · Cited by 58 · see citing works

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

Paliwal, A., Jain, S., Kumar, S., Wal, P., Khandai, M., Khandige, P. S., Sadananda, V., Anwer, M. K., Gulati, M., Behl, T., et al. (2024) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2024.2330666 · Cited by 58 · see citing works

The rcdk and cluster R packages applied to drug candidate selection

Voicu, A., Duţeanu, N., Voicu, M., Vlad, M., & Dumitrascu, V. C. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0405-0 · Cited by 55 · see citing works

Molecular designing, crystal structure determination and in silico screening of copper(II) complexes bearing 8-hydroxyquinoline derivatives as anti-COVID-19

Ali, A., Sepay, N., Afzal, M. F., Sepay, N., AlArifi, A. S., Shahid, M., & Ahmad, M. (2021) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2021.104772 · Cited by 54 · see citing works

In Silico Approaches: A Way to Unveil Novel Therapeutic Drugs for Cervical Cancer Management

Gomes, D., Silvestre, S., Duarte, A. P., Venuti, A., Soares, C. P., Passarinha, L. A., & Sousa, Â. (2021) · Pharmaceuticals · doi:10.3390/ph14080741 · Cited by 53 · see citing works

GC-MS analysis of phytoconstituents from Ruellia prostrata and Senna tora and identification of potential anti-viral activity against SARS-CoV-2

Alam, R., Imon, R. R., Talukder, M. E. K., Akhter, S., Hossain, M. A., Ahammad, F., & Rahman, M. M. (2021) · RSC Advances · doi:10.1039/d1ra06842c · Cited by 53 · see citing works

Chemical Chaperones as Novel Drugs for Parkinson’s Disease

Pujols, J., Peña‐Díaz, S., Pallarès, I., & Ventura, S. (2020) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2020.01.005 · Cited by 53 · see citing works

Identification of Zika Virus NS2B-NS3 Protease Inhibitors by Structure-Based Virtual Screening and Drug Repurposing Approaches

Santos, F. R. D. S., Nunes, D. A. F., Lima, W. G., Davyt, D., dos Santos, L. L., Taranto, A. G., & Ferreira, J. M. S. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00933 · Cited by 52 · see citing works

Algorithm selection for protein–ligand docking: strategies and analysis on ACE

Chen, T., Shu, X., Zhou, H., Beckford, F. A., & Mısır, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-35132-5 · Cited by 52 · see citing works

ChatGPT in Drug Discovery

Sharma, G., & Thakur, A. (2023) · doi:10.26434/chemrxiv-2023-qgs3k · Cited by 48 · see citing works

Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation

Duhan, M., Sindhu, J., Kumar, P. P., Devi, M., Singh, R. P., Kumar, R., Lal, S., Kumar, A., Kumar, S., & Hussain, K. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1863861 · Cited by 51 · see citing works

Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery

Hampel, H. J., Vergallo, A., Caraci, F., Cuello, A. C., Lemercier, P., Vellas, B. J., Giudici, K. V., Baldacci, F., Hänisch, B., Haberkamp, M., et al. (2020) · Neuropharmacology · doi:10.1016/j.neuropharm.2020.108081 · Cited by 50 · see citing works

Allosteric small molecule modulators of nuclear receptors

Meijer, F. A., de Gevel, I. A. L.-V., de Vries, R. M. J. M., & Brunsveld, L. (2019) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2019.01.022 · Cited by 50 · see citing works

Thiazolidine‐4‐one clubbed pyrazoles hybrids: Potent α‐amylase and α‐glucosidase inhibitors with NLO properties

Kumar, P. P., Duhan, M., Sindhu, J., Kadyan, K., Saini, S., & Panihar, N. (2019) · Journal of Heterocyclic Chemistry · doi:10.1002/jhet.3882 · Cited by 50 · see citing works

A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease

Elkaeed, E. B., Eissa, I. H., Elkady, H., Abdelalim, A., Alqaisi, A. M., Alsfouk, A. A., Elwan, A., & Metwaly, A. M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23158407 · Cited by 49 · see citing works

c-Jun N-Terminal Kinase Inhibitors as Potential Leads for New Therapeutics for Alzheimer’s Diseases

Rehfeldt, S. C. H., Majolo, F., Goettert, M. I., & Laufer, S. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21249677 · Cited by 46 · see citing works

Identification of potent BRD4‐BD1 inhibitors using classical and steered molecular dynamics based free energy analysis

Gupta, A., & Purohit, R. (2024) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30532 · Cited by 48 · see citing works

Pharmacoinformatics-based identification of potential bioactive compounds against Ebola virus protein VP24

Kwofie, S. K., Broni, E., Teye, J., Quansah, E., Issah, I., Wilson, M. D., Miller, W. A., Tiburu, E. K., & Bonney, J. H. K. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103414 · Cited by 48 · see citing works

Identification of Novel SARS-CoV-2 Inhibitors: A Structure-Based Virtual Screening Approach

Aissouq, A. E., Chedadi, O., Bouachrıne, M., & Ouammou, A. (2021) · Journal of Chemistry · doi:10.1155/2021/1901484 · Cited by 47 · see citing works

Small Molecule Wnt Pathway Modulators from Natural Sources: History, State of the Art and Perspectives

Blagodatski, A. S., Klimenko, A., Jia, L., & Katanaev, V. L. (2020) · Cells · doi:10.3390/cells9030589 · Cited by 47 · see citing works

Computational Identification of Druggable Bioactive Compounds from Catharanthus roseus and Avicennia marina against Colorectal Cancer by Targeting Thymidylate Synthase

Islam, M. R., Awal, M. A., Khames, A., Abourehab, M. A. S., Samad, A., Hassan, W. M. I., Alam, R., Osman, O. I., Nur, S. M., Molla, M. H. R., et al. (2022) · Molecules · doi:10.3390/molecules27072089 · Cited by 47 · see citing works

In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective

Rahman, M. M., Islam, M. R., Akash, S., Mim, S. A., Rahaman, M. S., Emran, T. B., Akkol, E. K., Sharma, R., Alhumaydhi, F. A., Sweilam, S. H., et al. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.929430 · Cited by 49 · see citing works

Dynamical mean field theory algorithm and experiment on quantum computersPreprint

Rungger, I., Fitzpatrick, N., Chen, H., Alderete, C. H., Apel, H., Cowtan, A., Patterson, A., Ramo, D. M., Zhu, Y., Nguyen, N. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04735 · Cited by 52 · see citing works

High-throughput virtual screening of drug databanks for potential inhibitors of SARS-CoV-2 spike glycoprotein

Awad, I. E., Abu‐Saleh, A. A. A., Sharma, S., Yadav, A., & Poirier, R. A. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1835721 · Cited by 47 · see citing works

Discovery of New Catalytic Topoisomerase II Inhibitors for Anticancer Therapeutics

Matias-Barrios, V. M., Radaeva, M., Song, Y., Alperstein, Z., Lee, A. R., Schmitt, V., Lee, J., Ban, F., Xie, N., Qi, J., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.633142 · Cited by 45 · see citing works

Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism

Zhai, J., Man, V. H., Ji, B., Cai, L., & Wang, J. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103728 · Cited by 44 · see citing works

Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

Zaki, M. E. A., Al‐Hussain, S. A., Masand, V. H., Akasapu, S., Bajaj, S. O., El-Sayed, N. N. E., Ghosh, A., & Lewaa, I. (2021) · Pharmaceuticals · doi:10.3390/ph14040357 · Cited by 44 · see citing works

Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation

Bepari, A. K., & Reza, H. M. (2021) · PeerJ · doi:10.7717/peerj.11261 · Cited by 44 · see citing works

Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies

Duhan, M., Kumar, P. P., Sindhu, J., Singh, R. P., Devi, M., Kumar, A., Kumar, R., & Lal, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104876 · Cited by 43 · see citing works

In silico prediction of Antifungal compounds from Natural sources towards Lanosterol 14-alpha demethylase (CYP51) using Molecular docking and Molecular dynamic simulation

Sama‐ae, I., Pattaranggoon, N. C., & Tedasen, A. (2023) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2023.108435 · Cited by 43 · see citing works

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

Garduño‐Juárez, R., Tovar-Anaya, D. O., Perez‐Aguilar, J. M., Lozano-Aguirre, L. F., Zubillaga, R. A., Álvarez-Pérez, M. A., & Villarreal-Ramírez, E. (2024) · Polymers · doi:10.3390/polym16131864 · Cited by 44 · see citing works

In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors

Mirza, M. U., Vanmeert, M., Froeyen, M., Ali, A., Rafique, S., & Idrees, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43129-2 · Cited by 43 · see citing works

Tailoring diesel bioblendstock from integrated catalytic upgrading of carboxylic acids: a “fuel property first” approach

Huo, X., Huq, N. A., Stunkel, J., Cleveland, N. S., Starace, A. K., Settle, A. E., York, A. M., Nelson, R. S., Brandner, D. G., Fouts, L., et al. (2019) · Green Chemistry · doi:10.1039/c9gc01820d · Cited by 43 · see citing works

Quinone scaffolds as potential therapeutic anticancer agents: Chemistry, mechanism of Actions, Structure-Activity relationships and future perspectives

Faizan, S., Mohsen, M. M. A., Amarakanth, C., Justin, A., Rahangdale, R. R., Hariharapura, R. C., & Kumar, B. R. P. (2024) · Results in Chemistry · doi:10.1016/j.rechem.2024.101432 · Cited by 42 · see citing works

A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures

Kurosaki, K., Wu, R., & Uesawa, Y. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21217853 · Cited by 44 · see citing works

A new paradigm for applying deep learning to protein–ligand interaction prediction

Wang, Z., Wang, S., Li, Y., Guo, J., Wei, Y., Mu, Y., Zheng, L., & Li, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae145 · Cited by 41 · see citing works

Harnessing systematic protein–ligand interaction fingerprints for drug discovery

Zhao, Z., & Bourne, P. E. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.07.004 · Cited by 41 · see citing works

MODERN APPROACH TO COMPUTER AIDED DRUG DESIGNING

Dr. Somesh Kumar Saxena*, N. P. J. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15179994

COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

Surabhi, S., & Singh, B. (2018) · Journal of Drug Delivery and Therapeutics · doi:10.22270/jddt.v8i5.1894 · Cited by 169 · see citing works

A Review On Computer-Aided Drug Design And Discovery

Ahire, S. M., Jadhav, S. P., Shewale, V. V., Pawar, P. S., Kokande, A. M., Sonawane, D. P., & Patil, D. M. (2023) · Journal for ReAttach Therapy and Developmental Diversities

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 · see citing works

COMPUTER AIDED DRUG DESIGN: A MINI-REVIEW

Yahya, B. A. (2020) · Journal of medical pharmaceutical and allied sciences · doi:10.22270/jmpas.v9i5.971 · Cited by 1 · see citing works