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

FRET-based in vitro assay for rapid detecting of SARS-CoV-2 entry inhibitors

Yan, C., Qiao, Q., Zhou, W., Zhou, X., Chen, Y., Miao, L., & Xu, Z. (2024) · Chinese Chemical Letters · doi:10.1016/j.cclet.2024.110258 · Cited by 3

Vaccine Adjuvants: From Empirical to a More Rational Drug Design

Marciani, D. J. (2024) · Exploratory Research and Hypothesis in Medicine · doi:10.14218/erhm.2024.00002 · Cited by 4

Integrated transcriptomics- and structure-based drug repositioning identifies drugs with proteasome inhibitor properties

Larsson, P., Rosa, M. C. D., Righino, B., Olsson, M., Florea, B. I., Forssell-Aronsson, E. B., Kovács, A., Karlsson, P., Helou, K., & Parris, T. Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-69465-6 · Cited by 3

ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity

Chatterjee, D., Kaur, G., Muradia, S., Singh, B., & Agrewala, J. N. (2019) · Scientific Reports · doi:10.1038/s41598-018-36179-5 · Cited by 3

Pemodelan Homologi Komparatif FABP Belalang Kembara (Locusta migratoria) Dengan PHYRE2 dan Skrining Virtual Inhibitor Potensial

Saudale, F. Z., & Suatu, I. R. S. (2020) · Indo J Chem Res · doi:10.30598//ijcr.2020.7-fre · Cited by 3

A computational approach yields selective inhibitors of human excitatory amino acid transporter 2 (EAAT2)

Damm‐Ganamet, K. L., Rives, M., Wickenden, A. D., McAllister, H., & Mirzadegan, T. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ac119.011190 · Cited by 3

Computational investigation on the effects of pharmaceutical polymers on the structure and dynamics of interleukin2 in heat stress

Moradi, S., Mirzaei, S., Khosravi, R., Farhadian, N., Koushki, E. H., & Shahlaei, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1784283 · Cited by 3

The Integration of Pharmacophore-Based 3D QSAR Modeling and Virtual Screening in Identification of Natural Product Inhibitors against SARS-CoV-2

Norouzi, S., Farahani, M., & Ebrahimi, S. N. (2020) · Pharmaceutical Sciences · doi:10.34172/ps.2020.98 · Cited by 3

Computer-Aid Molecular Docking Technology in Cereal Mycotoxin Analysis

Chen, J., Ye, J., Yan, Z., Shuai, C., & Yuan, Q. (2019) · Journal of Food Science and Engineering · doi:10.17265/2159-5828/2019.06.007 · Cited by 3

Inhibition of SARS-CoV-2 Main Protease: A Repurposing Study That Targets the Dimer Interface of the ProteinPreprint

Pekel, H., Ilter, M., & Şensoy, Ö. (2020) · ChemRxiv · doi:10.26434/chemrxiv.13353434 · Cited by 3

Identification of A Potential Inhibitor for Anticancer Target MTHFD2 by Consensus Docking and Molecular DynamicsPreprint

Zhou, H., Hong, Y., & Beckford, F. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.09.566353 · Cited by 3

Asenapine as a Potential Lead Inhibitor against Central Ca2+/Calmodulin-Dependent Protein Kinase II: Investigation by Docking Simulation and Experimental Validation

Daoud, S., Abutayeh, R. F., Alabed, S. J., & Taha, M. O. (2023) · The Open Medicinal Chemistry Journal · doi:10.2174/18741045-v17-e230217-2022-14 · Cited by 2

In silico Assessment of Phytochemicals from Selected Plants as Prospective TGF‐β1 Inhibitors for Prostate Cancer Therapy

Ishabiyi, F. O., Omotosho‐Sanni, R. Y., Baammi, S., Bourhia, M., Shazly, G. A., Ibenmoussa, S., & Umar, H. I. (2024) · ChemistrySelect · doi:10.1002/slct.202401413 · Cited by 2

Structure-based discovery and conformational analysis of non-phosphomimetic bidentate inhibitor for protein tyrosine phosphatase 1B

Yoon, S., Cho, J., Kim, J., Kim, K. D., & Kang, Y. J. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.144848 · Cited by 2

Phytochemical inhibitors against MPOXV: Insights from molecular dynamics simulation and pharmacokinetics profile

Masum, M. H. U., Khatun, M. M., Akter, F., Parvin, R., & Mahdeen, A. A. (2025) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2025.100056 · Cited by 2

Protein–Ligand Structure Prediction by Template‐Guided Ensemble Docking Strategy

Zhang, K., Wu, Q., & Huang, S. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70063 · Cited by 2

From Quantum Chemistry to Dynamics: Exploring Potential Inhibitors for Alongshan Virus RdRp Through Advanced Computational Techniques

Khan, K., Ahmad, S., Aljasir, M. A., Nadeem, S., Siddique, F., Saira, & Rehman, B. (2025) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416525501133 · Cited by 2

Tiliacorinine as a Promising Candidate for Cholangiocarcinoma Therapy via Oxidative Stress Molecule Modulation: A Study Integrating Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation

Boonsit, T., Chatatikun, M., Sirirattanakul, S., Pattaranggoon, N. C., Sama‐ae, I., Kawakami, F., Imai, M., Raungrut, P., Phongphithakchai, A., Tedasen, A., et al. (2025) · Antioxidants · doi:10.3390/antiox14111273 · Cited by 2

Molecular docking: a computational approach for the discovery of novel targets against visceral leishmaniasis

Gouri, V., Kanojia, A., Kumar, A., & Kumar, A. (2026) · Journal of Molecular Modeling · doi:10.1007/s00894-026-06647-1 · Cited by 2

Fungal L-Asparaginase from environmental isolates with antimicrobial and antibiofilm activities

Khalifa, B. A., Hashem, Z. S., Hisham, M., Khedr, E. H., & Abdelhameed, S. M. (2026) · AMB Express · doi:10.1186/s13568-026-02025-5 · Cited by 2

Bio‐active designed peptide for hair keratin strengthening

Rha, C., In, B. G., Myoung, K., Song, C., Lee, E.-S., Baek, H. S., & Park, W. S. (2026) · International Journal of Cosmetic Science · doi:10.1111/ics.70092 · Cited by 2

Integrated computational and experimental characterization of a new thiazolidinone EGFR inhibitor with promising anticancer activity

Elkady, H., Elgammal, W. E., Mohamed, Y., Taghour, M. S., Husein, D. Z., Amin, F. G., Alsfouk, B. A. A., Elkaeed, E. B., Eissa, I. H., & Metwaly, A. M. (2026) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2026.146138 · Cited by 2

Structure-based identification of novel scaffolds as potential HIV-1 entry inhibitors involving CCR5

Appiah‐Kubi, P., Iwuchukwu, E. A., & Soliman, M. E. S. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1982006 · Cited by 2

Natural Compound Isoliensinine Inhibits Stress‐Induced Hair Greying by Blocking β2‐Adrenoceptor

Yan, L., Li, M., Zhu, M., Sun, R., Gao, Y., Zhao, X., Ling, Y., Xu, X., Chu, W., & Wang, X. (2023) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1155/2023/7238029 · Cited by 2

In Silico Discovery of Class IIb HDAC Inhibitors: The State of Art

Khatun, S., Amin, S. A., Gayen, S., & Jha, T. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-33871-7_2 · Cited by 2

Identification and dynamics of novel scaffolds against Enterococcus faecium serine hydroxymethyltransferase enzyme: a potential target for antibiotics development

Alhassan, H. H., Alruwaili, Y. S., Alzarea, S. I., Alruwaili, M., Alsaidan, O. A., Alzarea, A. I., Manni, E., & Qamar, M. T. U. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2257313 · Cited by 2

Collective and Coordinated Conformational Changes Determine Agonism or Antagonism at the Human Trace Amine-Associated Receptor 1

Robles, A. I., Dinamarca‐Villarroel, L., Torres, G. E., & Fierro, A. (2023) · ACS Omega · doi:10.1021/acsomega.3c06315 · Cited by 2

Up-to-Date Developments in Homology Modeling

Muhammed, M. T., & Aki, E. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815179934123010006 · Cited by 2

Computational Study of Antimicrobial Peptides for Promising Therapeutic Applications against Methicillin-resistant Staphylococcus aureus

Sinoliya, P., Solanki, P. S., Niraj, R. R. K., & Sharma, V. (2024) · Current Computer - Aided Drug Design · doi:10.2174/0115734099285473240101111303 · Cited by 2

Drug and formulation development processes

Charoo, N. A., Mohamed, E. M., Eltigani, M., Khan, M. A., Nutan, M. T. H., & Rahman, Z. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91111-5.00007-x · Cited by 2

Rational drug repurposing for alzheimer’s treatment using in-silico ligand and structure-based approaches

Rathi, K. M., Wavhale, R. D., Bhole, R. P., Undale, V., & Chaudhari, S. Y. (2024) · Brazilian Journal of Pharmaceutical Sciences · doi:10.1590/s2175-97902024e23618 · Cited by 2

Molecular Docking and Computational In Silico Investigations of Metal-Based Drug Agents

Arjmand, F., Tabassum, S., & Khan, H. Y. (2024) · Springer eBooks · doi:10.1007/978-981-97-0146-9_6 · Cited by 4

ACE inhibitors from Suaeda salsa: 3D-QSAR modeling, metabolomics, molecular docking and molecular dynamics simulations

Yue, G., Gu, H., Zhang, K., Song, Y., & Hao, Y. (2024) · In Silico Pharmacology · doi:10.1007/s40203-024-00233-0 · Cited by 2

Proteasome and PARP1 dual-target inhibitor for multiple myeloma: Fluzoparib

Deng, K., Li, Q., Lu, L., Wang, L., Cheng, Z., & Wang, S. Z. (2024) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2024.101781 · Cited by 2

Leveraging the Thermodynamics of Protein Conformations in Drug Discovery

Zhang, B. W., Fajer, M., Chen, W., Moraca, F., & Wang, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01612 · Cited by 3

Targeting GPR52 for potential agonists for schizophrenia therapy: A computational drug discovery study

Demir, S., & Alparslan, G. T. (2025) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2025.108994 · Cited by 2

In Silico Study Revealing the Inhibitory Potential of Flavonoids From Azadirachta Indica against Pf DHFR and Pf DHODH as Antimalarial Agents

Tjitda, P. J. P., Nitbani, F. O., Wahyuningsih, T. D., Lerrick, R. I., & Abanit, Y. M. (2025) · ChemistrySelect · doi:10.1002/slct.202404851 · Cited by 2

Advancing QSAR models in drug discovery for best practices, theoretical foundations, and applications in targeting nuclear factor-κB inhibitors- A bright future in pharmaceutical chemistry

Hammoudi, N.-E.-H., Lalaoui, O., Sobhi, W., Erto, A., Micoli, L., Jeon, B., Benguerba, Y., Elfalleh, W., Ali, M. A. M., Ibrahim, N. A., et al. (2025) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2025.105544 · Cited by 2

Deciphering the dynamics of cathepsin D as a potential drug target to enhance anticancer drug-induced apoptosis

Abideen, S. A., Khan, M., Irfan, M., & Ahmad, S. (2022) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2022.119677 · Cited by 2

Classifying Calpain Inhibitors for the Treatment of Cataracts: A Self Organising Map (SOM) ANN/KM Approach in Drug Discovery

Hudson, I. L., Leemaqz, S., Neffe, A. T., & Abell, A. D. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-28495-8_9 · Cited by 2

Analyzing the protein-protein interaction network and the topological properties of prostate cancer and allied diseases: A computational bioinformatics approach

Puspo, N. A., Akter, L., Siddique, S., Paul, B. K., Ahmed, K., Bhuiyan, T., & Islam, M. K. (2020) · Gene Reports · doi:10.1016/j.genrep.2020.100842 · Cited by 2

Therapeutic potential of leucine-rich repeat kinase 2 inhibitors for Parkinson's disease treatment

Sahoo, S., Padhy, A. A., Kumari, V., Shivani, K., & Mishra, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91287-7.00011-9 · Cited by 2

Phytochemical Investigation, Molecular Docking Studies, and Biological Efficacy of Rhizome Essential Oil Composition of Costus speciosus

Thambi, M., & Cherian, T. (2022) · Springer protocols handbooks/Springer protocols · doi:10.1007/978-1-0716-2683-2_30 · Cited by 2

Utilising the Computational Power of Blockchain Proof-ofWork in Computer-Aided Drug Design

Jaiyeoba, O., Samsudin, A., & Sulaiman, S. (2022) · International Journal of Emerging Technology and Advanced Engineering · doi:10.46338/ijetae1022_05 · Cited by 2

Emerging technologies for COVID-19, diagnosis, prevention, and management

Uğur, A. R., & Özdemir, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91794-0.00019-6 · Cited by 2

Investigating the potential of natural compounds as novel inhibitors of SARS-CoV-2 RdRP using computational approaches

Bagabir, S. A. (2023) · Biotechnology and Genetic Engineering Reviews · doi:10.1080/02648725.2023.2195240 · Cited by 2

Computer‐aided drug design course for pharmacy major students in Shenyang Pharmaceutical University following the COVID ‐19 pandemic: Challenges and opportunities

Huang, M., Du, Y. J., Liu, Y., Zhao, Y., Guo, Y., Liu, D., Zhao, L., & Wang, J. (2023) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.21772 · Cited by 2

Utilization of computational methods for the identification of new natural inhibitors of human neutrophil elastase in inflammation therapy

Alzain, A. A., Elbadwi, F. A., Al-Karmalawy, A. A., Elhag, R. I., Osman, W., & Mothana, R. A. (2023) · Open Chemistry · doi:10.1515/chem-2023-0161 · Cited by 2

Screening approach by a combination of computational and in vitro experiments: identification of fluvastatin sodium as a potential SIRT2 inhibitor

Yang, J. A., Wang, H., Liu, J., Ma, E., Jin, X., Li, Y.-C., & Ma, C. (2024) · Journal of Molecular Modeling · doi:10.1007/s00894-024-05988-z · Cited by 2

Computational Design of Potent Inhibitors for SARS-CoV-2’s Main ProteasePreprint

Abu‐Saleh, A. A. A., Awad, I. E., Yadav, A., & Poirier, R. A. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12548003 · Cited by 2

Plasmodial enzymes in metabolic pathways as therapeutic targets and contemporary strategies to discover new antimalarial drugs: a review

Salim, N. O., Yusuf, N. A. M., & Fuad, F. A. A. (2019) · Asia-Pacific Journal of Molecular Biology and Biotechnology · doi:10.35118/apjmbb.2019.027.4.05 · Cited by 1

Novel Drug Design

Sonawane, S. D., Bais, S. K., & Kamble, S. (2023) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-7911 · Cited by 1

Introduction to Computer-Based Simulations and Methodologies in Pharmaceutical Research

Roy, S. P. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815223019124010003 · Cited by 2

Molecular docking based comparative study of antiviral compounds on SARS-CoV-2 spike protein

Nagavarapu, S., Kumar, J., & Biswal, P. K. (2024) · Natural Product Research · doi:10.1080/14786419.2024.2355589 · Cited by 1

PREDICTION OF BIOLOGICAL ACTIVITY BY MEANS OF SYNTHESIS AND QSAR MODEL OF PHENYLHYDRAZONE BASED ON METHYL 4-FORMYLBENZOATE

Ibrahimova, S. A. (2024) · New Materials Compounds and Applications · doi:10.62476/nmca83373 · Cited by 1

QSAR Modeling and Its Application in Veterinary Drug Design

Sharma, S., & Darlami, J. (2025) · Springer eBooks · doi:10.1007/978-981-97-7395-4_15 · Cited by 1

Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery

Bhojwani, H. R., Rajnani, N., Hare, A., & Kurup, N. (2025) · Advances in pharmacology · doi:10.1016/bs.apha.2025.01.014 · Cited by 1

Experimental Assays: Chemical Properties, Biochemical and Cellular Assays,and In Vivo Evaluations

Serafim, M. S. M., Reis, E. V. D. S., Coelho-dos-Reis, J. G. A., Abrahão, J. S., & O’Donoghue, A. J. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_12 · Cited by 1

Chemoinformatics analysis of Mangifera indica leaves extracted phytochemicals as potential EGFR kinase modulators

Mashud, M. A. A., Kumer, A., Jahan, I., Somrat, M. M. H., Talukder, M. E. K., Rahman, M. M., Uddin, A. F. M. S., Harun-Or-Rashid, M., Rahman, M., Harun‐Ur‐Rashid, M., et al. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1524384 · Cited by 1

Integrative Analysis of Drug Co-Prescriptions in Peritoneal Dialysis Reveals Molecular Targets and Novel Strategies for Intervention

Evgeniou, M., Perco, P., Eibensteiner, F., Unterwurzacher, M., Vychytil, A., Herzog, R., & Kratochwill, K. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14113733 · Cited by 1

Exploring Novel Y140F/H Mutant Fungal CYP51 Inhibitors: A Molecular Docking and Dynamics Study on Thiophene Compounds

Alzain, A. A., Edris, A., Mukhtar, R. M., Mohamed, H. M., Aldowimar, M. A., Alqarni, A. A., Mohamed, G. A., & Ibrahim, S. R. M. (2025) · Journal of Pharmacy And Bioallied Sciences · doi:10.4103/jpbs.jpbs_404_25 · Cited by 1

Unlocking the Potential of Citrus limon : Molecular Docking and Simulation for Designing Anti‐Necrotising Fasciitis Drug

Bibi, R., Zainab, A., Dawood, S., Khan, H. A., Shah, M. A., Nijabat, A., Ahmad, M., Dawoud, T. M. S., Mughram, M. H. A., & Bourhia, M. (2025) · Chemistry & Biodiversity · doi:10.1002/cbdv.202500372 · Cited by 1

Unveiling the structural, medicinal, and toxicological potential of ciprofloxacin and its major degradation products: a DFT and in-silico approach

Rahman, M. O., Saha, S. R., Abdullah, M. S., Hasan, M. M., Protiva, A. A., Siddique, I. H., Uzzaman, M., & Akter, B. (2025) · Monatshefte für Chemie - Chemical Monthly · doi:10.1007/s00706-025-03401-w · Cited by 1

Machine Learning‐Guided Discovery of Natural MDM2 Inhibitors: A Multistage In Silico Pipeline from Screening to ADMET Profiling

Budha, B., Mohyeldin, M. M., Ayub, A. R., Khanal, M., & Acharya, A. (2025) · Advanced Theory and Simulations · doi:10.1002/adts.202501502 · Cited by 1

The Constituents of Phyllanthus emblica Fruit Ameliorate Hyperlipidemia Through the Modulation of SREBPs, HMG‐CoA Reductase, and LDL Receptor Pathway

Kuddus, S. A., Shohag, M. H., Yaseen, Q. I., Ahmed, L., Kashfia, A. T., Islam, F. B., Khan, S. I., Karim, R., Hossain, M., Alam, M. A., et al. (2025) · Scientifica · doi:10.1155/sci5/7941857 · Cited by 1

Discovery of New 7-Propanamide Benzoxaborole as Potent Anti-SKOV3 Agent via 3D-QSAR Models

Ji, L., Zhang, J., Zhou, H., & Zhao, Y. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27010472 · Cited by 1

Probing the intermolecular interactions, binding affinity, charge density distribution of N-(3-chlorophenyl)-2-cyanoacetamide (NCPC) against Alzheimer’s disease: Insights of experimental and computational techniques

C, V. K. D., Nagaraja, N., Neetha, S., M., S. C., Ali, M. S., Chethan, B. S., & Lohith, T. N. (2026) · Molecular Crystals and Liquid Crystals · doi:10.1080/15421406.2026.2613087 · Cited by 1

DTAP: a unified graph transformer framework for joint prediction of drug–target affinity and docking pose

Liu, J., Ding, Y., Yan, Y., Zheng, L., & Pan, Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag069 · Cited by 1

Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions

Kumar, S., Pal, A., & Chatterjee, S. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00676-5 · Cited by 1

Design and Screening of the Peptide SAMP-12aa Derived from LL-37, Which Exhibits Anti-H. Pylori Activity and Immunomodulatory Effects

Lu, J., Wang, Q., Qin, M., Dou, J., Xiong, Y., & Zhang, X. (2026) · Molecules · doi:10.3390/molecules31061002 · Cited by 1

A Review on the Development of Semisynthetic Phytochemicals in the Discovery of Anticancer Drugs

Alkac, F. Z., & Yamalı, C. (2026) · Current Topics in Medicinal Chemistry · doi:10.2174/0115680266467530260405204054 · Cited by 1

Discovery and Design of Electroactive Molecules for Aqueous Redox Flow Batteries

Zhang, Q., Zhang, L., Zhao, X., Xu, K., Chen, Z., & Ji, Y. (2026) · ChemEngineering · doi:10.3390/chemengineering10040052 · Cited by 1

In search of novel PD1 inhibitor from natural products by high-throughput virtual screening and molecular dynamics simulation

Sharma, N., Debnath, A., Mazumder, R., Rai, P., & Singh, R. K. (2026) · PLoS ONE · doi:10.1371/journal.pone.0339355 · Cited by 1

Proanthocyanidins inhibit methane emissions by interacting with methyl-coenzyme M reductase and reshaping rumen microbiome function

Liu, Z. H., Guo, Y., Xiao, L., Guo, J., Chen, Y., Wang, H., Nan, X., Zhou, M., Zhang, F., He, Y., et al. (2026) · Microbiome · doi:10.1186/s40168-026-02406-9 · Cited by 1

Targeting the EGFR G-quadruplex DNA in liver cancer: Discovery of alkynyl-modified phenanthroimidazole derivatives as potent stabilizers

Chen, W., Liang, J., Wu, Z., Shi, X., She, M., Chen, R., Deng, R., Zeng, H., Liu, C., Yu, A., et al. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152742 · Cited by 1

AI-driven computational drug design: tools, workflow and challenges

Dhir, R., Ghosh, P., Sharma, D., & Asati, V. (2026) · RSC Advances · doi:10.1039/d6ra02374f · Cited by 1

In silico and In vivo Evaluation of Oxidative Stress Inhibitors Against Parkinson's Disease using the C. elegans Model

Pradeep, H., Ashok, A., Prakash, I., Priya, C. I., Zinzala, J., Marigowda, V. V., & Sosalegowda, A. H. (2020) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207323666200514074128 · Cited by 1

Identification of potential inhibitors of Trichomonas vaginalis iron-containing superoxide dismutase by computer-aided drug design approach

Koçer, S., & Mutlu, O. (2021) · Structural Chemistry · doi:10.1007/s11224-021-01766-2 · Cited by 1

Revealing Anti-viral Potential of Siddha Formulation Manjal noi Kudineer against Hepatitis C Viral - RNA Dependent RNA Polymerase Using in-silico Docking Technique

Visweswaran, S., Iyswarya, S., & Muthukumar, N. J. (2022) · Pharmacognosy Research · doi:10.5530/pres.14.3.41 · Cited by 1

Automated Portfolio Generation for Selection Hyper-heuristics: an Application to Protein Structure Prediction on 2D HP Model

Mısır, M. (2022) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb55180.2022.9863039 · Cited by 1

Nitrofuran Antibiotics and Their Derivatives: A Computational Chemistry Analysis

Aizpuru, A. P. L., Quezada-García, Y. A., Ramírez-Alonso, G. M. D. J., Hinojos-Gallardo, L. C., & Camarillo‐Cisneros, J. (2022) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-18256-3_20 · Cited by 1

Investigating the Use of Ensemble Learning for Drug Target Affinity Model

Handoyo, A. T., Lucky, H., Wicaksono, P., Suhartono, D., & Majiid, M. R. N. (2022) · International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA) · doi:10.1109/icicyta57421.2022.10038046 · Cited by 1

"Insilico Identification and Exploring the Potential Hits to Combat SARS-CoV-2 RDRP"

Zheng, G. (2023) · Biomedical Journal of Scientific & Technical Research · doi:10.26717/bjstr.2023.48.007721 · Cited by 1

In Silico Analysis of Phytocompounds from Aegle marmelos Against Potential Targets of Irritable Bowel Syndrome

Gopakumar, G., Ramesh, B., Michaelson, D. I. S., Kunjiappan, S., Kabilan, S. J., & Pavadai, P. (2023) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s273741652350045x · Cited by 1

Multi‐Technique Approach to Identify Potent Antimicrobial Agents from Calotropis procera : Insight into Pharmacophore Modeling, Molecular docking, MD simulation, and DFT Approaches

Rashid, M., Sajjad, N., Shafiq, N., Parveen, S., Khan, R. A., Farooq, A., Shahab, M., Dawoud, T. M. S., Brogi, S., & Bourhia, M. (2024) · ChemistrySelect · doi:10.1002/slct.202303642 · Cited by 1

An interdisciplinary course on computer‐aided drug discovery to broaden student participation in original scientific research

Stratton, C. F., Christensen, A., Jordan, C., Salvatore, B. A., & Mahdavian, E. (2024) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.21811 · Cited by 1

Role of Metabolomics in the Discovery of Modern Drugs from Traditional Resources

Saikia, S., Puzari, M., & Chetia, P. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-4600-2_9 · Cited by 1

Guaiacol–chalcone and nitroguaiacol–chalcone derivatives: In vitro and in silico antibacterial evaluation

Hoepfner, A., Petzer, A., Petzer, J. P., & Cloete, T. T. (2024) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2024.130088 · Cited by 1

Molecular docking, molecular dynamics simulation, and in vitro evaluation of hydroxythioxanthone derivatives as antimalarial agents

Hermawan, F., Jumina, J., Ernawati, T., Asih, P. B. S., Rozi, I. E., Coutrier, E. F. N., & Syafruddin, D. (2024) · The Thai Journal of Pharmaceutical Sciences · doi:10.56808/3027-7922.3018 · Cited by 1

Omics Approaches to Drug and Drug-Non-Drug Interactions

Reis, Â. A. D. S., & Santos, R. D. S. (2025) · Springer eBooks · doi:10.1007/978-3-031-80107-5_10 · Cited by 1

Advances in the Design of Novel Antidiabetic Agents Using In-silico Approaches

Jadhav, S., Yeram, P. B., Alavala, R. R., & Vora, A. (2025) · Springer eBooks · doi:10.1007/978-981-96-4154-3_21 · Cited by 1

A systematic review on revolutionizing veterinary drug discovery: harnessing omics data to combat complex diseases in domestic animals

Avashthi, H., Angadi, U. B., Majumdar, S. G., Bhati, J., Singh, D. B., Chandra, M., Singh, J. K., Sinha, A., Shah, S., & Mishra, D. C. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00558-6 · Cited by 3

Molecular docking and density functional theory studies of flavonoids of Holy basil plant against COX-2 enzyme

Hari, K. C., Neupane, K., Chhetri, K. B., Ojha, R., & Acharya, R. K. (2025) · Biophysical Chemistry · doi:10.1016/j.bpc.2025.107533 · Cited by 1

Drag design of sulfanilamide based on the technology of immune network modeling and ontological approach

Samigulina, G. A., & Самигулина, З. (2016) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/icaict.2016.7991821 · Cited by 1

Molecular Modelling of Potential Candidates for the Treatment of Depression

Silva, D. R., Barigye, S. J., Santos‐Garcia, L., & da Cunha, E. F. F. (2019) · Molecular Informatics · doi:10.1002/minf.201900024 · Cited by 1

Computational Modeling of ACE2-Mediated Cell Entry Inhibitors for the Development of Drugs Against Coronaviruses

De, P., & Roy, K. (2021) · Methods in pharmacology and toxicology · doi:10.1007/7653_2020_49 · Cited by 1

Pharmaceutical interest of in-silico approaches

Kumar, D., Sharma, P., Mahajan, A., Dhawan, R. K., & Dua, K. (2022) · Physical Sciences Reviews · doi:10.1515/psr-2018-0157 · Cited by 1

Role of in silico Drug Design in Pharmaceutical Sciences

Islam, R., Maji, S., Kabiraj, S., Habib, U., Pal, R., Bhattacharya, S., Chakraborty, S., & Bhattacharjee, D. A. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.42836 · Cited by 1

Introduction to drug design and discovery

de Oliveira, A. M., & Rudrapal, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90608-1.00008-3 · Cited by 1

Binding energy analysis and molecular dynamic simulation studies of the designed orally active, non-toxic GABARAP modulators

Nambiar, M. P., Ashwanikumar, N., Anoop, A., & Biju, A. R. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2022.2107571 · Cited by 1

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

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

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