Artificial intelligence in drug development: present status and future prospects

Artificial intelligence in healthcare: past, present and future

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Can the pharmaceutical industry reduce attrition rates?

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Principles of early drug discovery

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Artificial intelligence in medicine

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Diagnosing the decline in pharmaceutical R&D efficiency

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Planning chemical syntheses with deep neural networks and symbolic AI

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The rise of deep learning in drug discovery

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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Deep Learning in Medical Imaging: General Overview

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Organs-on-chips at the frontiers of drug discovery

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An Orchestration Framework for Digital Innovation: Lessons From the Healthcare Industry

Satwekar, A., Volpentesta, T., Spagnoletti, P., & Rossi, M. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3167259 · Cited by 34

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu, P. C., & Obulose, C. N. (2024) · Drug Development Research · doi:10.1002/ddr.22159 · Cited by 33

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

ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents

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FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers

Monteiro, N. R. C., Pereira, T., Machado, A. C. D., Oliveira, J. L., Abbasi, M., & Arrais, J. P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107285 · Cited by 32

The Kv1.3 K+ channel in the immune system and its “precision pharmacology” using peptide toxins

Varga, Z., Tajti, G., & Panyi, G. (2021) · Biologia Futura · doi:10.1007/s42977-021-00071-7 · Cited by 28

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Moreira‐Filho, J. T., Silva, A. C., Dantas, R. F., Gomes, B. F., Neto, L. R. S., Brandão-Neto, J., Owens, R. J., Furnham, N., Neves, B. J., Silva, F. P., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.642383 · Cited by 36

Targeting adipokines in polycystic ovary syndrome and related metabolic disorders: from experimental insights to clinical studies

Xu, Y., Zhu, H., Li, W., Chen, D., Xu, Y., Xu, A., & Ye, D. (2022) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2022.108284 · Cited by 31

Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry

Maharjan, R., Lee, J., Lee, K., Han, H., Kim, K. H., & Jeong, S. H. (2023) · Journal of Pharmaceutical Investigation · doi:10.1007/s40005-023-00637-8 · Cited by 33

From Artificial Intelligence to Augmented Intelligence: A Shift in Perspective, Application, and Conceptualization of AI

French, A. M., & Shim, J. P. (2024) · Information Systems Frontiers · doi:10.1007/s10796-024-10562-2 · Cited by 42

The role of DMPK science in improving pharmaceutical research and development efficiency

Mak, K., Epemolu, O., & Pichika, M. R. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.11.005 · Cited by 30

UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks

Mészáros, L. A., Farkas, A., Madarász, L., Bicsár, R., Galata, D. L., Nagy, B., & Nagy, Z. K. (2022) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2022.121773 · Cited by 29

Machine learning and human‐machine trust in healthcare: A systematic survey

Lin, H., Han, J., Wu, P., Wang, J., Tu, J., Tang, H., & Zhu, L. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12268 · Cited by 33

Paper-Based Electrochemical Devices for the Pharmaceutical Field: State of the Art and Perspectives

Antonacci, A., Scognamiglio, V., Mazzaracchio, V., Caratelli, V., Fiore, L. D., Moscone, D., & Arduini, F. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00339 · Cited by 30

Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review

Kaushal, K., Sarma, P., Rana, S., Medhi, B., & Naithani, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1855250 · Cited by 29

Communication training for pharmacy students with standard patients using artificial intelligence

Nakagawa, N., Odanaka, K., Ohara, H., & Kisara, S. (2022) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2022.06.021 · Cited by 30

How digital technologies and AI contribute to achieving the health-related SDGs

Köbe, P. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100298 · Cited by 27

Application of artificial intelligence in microbial drug discovery: Unlocking new frontiers in biotechnology

Mulat, M., Banicod, R. J. S., Tabassum, N., Javaid, A., Kim, T., Kim, Y., & Khan, F. (2025) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2025.107232 · Cited by 31

Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies

Ahmad, B., Ouahada, K., & Hamam, H. (2026) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.12.033 · Cited by 32

AI Ethics and Value Alignment for Nonhuman Animals

Ziesche, S. (2021) · Philosophies · doi:10.3390/philosophies6020031 · Cited by 28

Forecasting cancer: from precision to predictive medicine

Fertig, E. J., Jaffee, E. M., Macklin, P., Stearns, V., & Wang, C. (2021) · Med · doi:10.1016/j.medj.2021.08.007 · Cited by 26

From Prescription to Prediction: Leveraging AI/ML to Improve Medication Adherence and Adverse Drug Event Detection in Community Pharmacies

Onyekaonwu, C. B., Peter-Anyebe, A. C., & Raphael, F. O. (2019) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst52310387 · Cited by 30

Advances in artificial intelligence-based technologies for increasing the quality of medical products

Srivastava, N., Verma, S., Singh, A., Shukla, P., Singh, Y., Oza, A. D., Kaur, T., Chowdhury, S., Kapoor, M., & Yadav, A. N. (2024) · DARU Journal of Pharmaceutical Sciences · doi:10.1007/s40199-024-00548-5 · Cited by 27

A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery

Li, Q., & Kang, C. (2020) · Molecules · doi:10.3390/molecules25132974 · Cited by 26

Emerging of artificial intelligence and technology in pharmaceuticals: review

Sultana, A., Maseera, R., Rahamanulla, A., & Misiriya, A. (2023) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-023-00517-w · Cited by 30

Artificial Intelligence for Organoids Multidimensional Assessment

Mo, Y., Wang, J., Yang, H., Bai, L., Xu, K., & Su, J. (2025) · SmartMat · doi:10.1002/smm2.70016 · Cited by 25

Structure‒tissue exposure/selectivity relationship (STR) correlates with clinical efficacy/safety

Gao, W., Hu, H., Dai, L., He, M., Yuan, H., Zhang, H., Liao, J., Wen, B., Li, Y., Palmisano, M., et al. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.02.015 · Cited by 25

Machine learning at the edge for AI-enabled multiplexed pathogen detection

Ganjalizadeh, V., Meena, G. G., Stott, M. A., Hawkins, A. R., & Schmidt, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-31694-6 · Cited by 24

Future of Pharmaceutical Industry: Role of Artificial Intelligence, Automation and Robotics

Manoj Kumar, T., Preethi, B., Nunavath, R. S., & Nagappan, K. (2024) · Journal of Pharmacology and Pharmacotherapeutics · doi:10.1177/0976500x241252295 · Cited by 24

Deregulation of Chromosome Segregation and Cancer

Curtis, N. L., Ruda, G. F., Brennan, P. E., & Bolaños-García, V. M. (2019) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-030419-033541 · Cited by 23

Translational bioinformatics methods for drug discovery and drug repurposing

Ahmad, S., Qazi, S., & Raza, K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89824-9.00010-0 · Cited by 26

A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier

Filippo, J. I. D., Bollini, M., & Cavasotto, C. N. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.714678 · Cited by 24

Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives

Costa, F., Gómez‐Doblas, J. J., Expósito, A. D., Adamo, M., D’Ascenzo, F., Kołtowski, Ł., Saba, L., Mendieta, G., Gragnano, F., Calabrò, P., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf474 · Cited by 28

Advances in machine learning for optimizing pharmaceutical drug discovery

Ogbonna, U. E., Ofoezie, E. F., Babalola, O. O., Ottu, P. O., Ogbonna, C. A., Olisakwe, S. C., George, E. T., Babarinde, S., Omaba, O. J., Chukwuemeka, C. G., et al. (2025) · Current Proteomics · doi:10.1016/j.curpro.2025.100015 · Cited by 25

In silico methods for drug-target interaction prediction

Ru, X., Xu, L., Wu, H., & Zou, Q. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101184 · Cited by 26

Chloroquine and hydroxychloroquine repositioning in times of COVID-19 pandemics, all that glitters is not gold

Paumgartten, F. J. R., Delgado, I. F., Pitta, L. D. R., & de Oliveira, A. C. A. X. (2020) · Cadernos de Saúde Pública · doi:10.1590/0102-311x00088520 · Cited by 23

Role of Artificial Intelligence in Minimizing Carbon Footprint: A Systematic Review of Recent Insights

Arya, A. K., Bachheti, A., Bachheti, R. K., Singh, M. M., & Chandel, A. K. (2024) · Green energy and technology · doi:10.1007/978-3-031-51601-6_14 · Cited by 28

Data-Driven Analysis of Radiologists’ Behavior for Diagnosing Thyroid Nodules

Chang, L., Fu, C., Wu, Z., Liu, W., & Yang, S. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2969322 · Cited by 23

Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction

He, S., Yun, L., & Yi, H.-C. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05705-w · Cited by 22

Recent Advances in Ferrite‐Based Materials for Biomedical Applications: A Comprehensive Review

Mhase, P. D., Pujari, V. C., Fulari, A. V., Patange, S. M., Li, S., Wang, D., & Shirsath, S. E. (2026) · Advanced Materials · doi:10.1002/adma.74149 · Cited by 23

Structure-based drug designing

Pant, S., Verma, S., Pathak, R. K., & Singh, D. B. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89775-4.00027-4 · Cited by 22

BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation

Wang, G., Feng, H., & Cao, C. (2024) · Journal of Computational Biology · doi:10.1089/cmb.2024.0476 · Cited by 22

Recent Progress of Deep Learning in Drug Discovery

Wang, F., Diao, X., Chang, S., & Xu, L. (2021) · Current Pharmaceutical Design · doi:10.2174/1381612827666210129123231 · Cited by 22

Exploring the future of biopharmaceutical drug discovery: can advanced AI platforms overcome current challenges?

Bettanti, A., Beccari, A. R., & Biccarino, M. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00188-3 · Cited by 28

AI in drug development: advances in response, combination therapy, repositioning, and molecular design

Qi, R., Liu, S., Hui, X., Shaytan, A. К., & Liu, B. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4461-0 · Cited by 22

Artificial Intelligence in Pharmaceutical Field - A Critical Review

Shanbhogue, M. H., Thirumaleshwar, S., Tegginamath, P. K., & Hemanth Kumar, S. (2021) · Current Drug Delivery · doi:10.2174/1567201818666210617100613 · Cited by 21

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Chong, L. C., Gandhi, G., Lee, J. M., Yeo, W. W. Y., & Choi, S. B. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22168962 · Cited by 23

Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare

Mishra, S. (2022) · Journal of Electronics Electromedical Engineering and Medical Informatics · doi:10.35882/jeeemi.v4i1.1 · Cited by 27

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives

Togo, M. V., Mastrolorito, F., Orfino, A., Graps, E. A., Tondo, A. R., Altomare, C., Ciriaco, F., Trisciuzzi, D., Nicolotti, O., & Amoroso, N. (2023) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2023.2298827 · Cited by 22

A comprehensive review of molecular optimization in artificial intelligence‐based drug discovery

Xia, Y., Wang, Y., Wang, Z., & Zhang, W. (2024) · Quantitative Biology · doi:10.1002/qub2.30 · Cited by 25

Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: De Novo Molecular Generation Based on a Low Activity Lead Compound

Cai, H. N., Chen, W., Jiang, J. H., Wen, H., Luo, X., Li, J., Lu, L., Zhao, R., Ni, X., Sun, Y., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c00091 · Cited by 22

Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era

Dai, J., Zhou, Z., Zhao, Y., Kong, F., Zhai, Z., Zhu, Z., Cai, J., Huang, S., Xu, Y., & Sun, T. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117162 · Cited by 21

The current landscape of aromatase inhibitors for the treatment of estrogen receptor-positive breast carcinoma

Bhutani, K., Vishwakarma, S., Yadav, P., & Yadav, M. K. (2025) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2025.106729 · Cited by 20

From Molecules to Medicines: The Role of AI-Driven Drug Discovery Against Alzheimer’s Disease and Other Neurological Disorders

Alghamdi, M. A. (2025) · Pharmaceuticals · doi:10.3390/ph18071041 · Cited by 23

Unlocking the potential: multimodal AI in biotechnology and digital medicine—economic impact and ethical challenges

Bhushan, A., & Misra, P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01992-6 · Cited by 26

“Feasibility test and application of AI in healthcare”—with special emphasis in clinical, pharmacovigilance, and regulatory practices

Kalaiselvan, V., Sharma, A., & Gupta, S. K. (2020) · Health and Technology · doi:10.1007/s12553-020-00495-6 · Cited by 21

GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds

Ahmed, M., Hasani, H. J., Kalyaanamoorthy, S., & Barakat, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-88939-5 · Cited by 22

Artificial Intelligence and Cancer Drug Development

Yang, M. Q., Wang, S., Yang, F., Darsey, J. A., Ghosh, A., & Li, H. (2021) · Recent Patents on Anti-Cancer Drug Discovery · doi:10.2174/1574892816666210728123758 · Cited by 23

Local Anesthetic Ropivacaine Exhibits Therapeutic Effects in Cancers

Xu, P., Zhang, S.-B., Tan, L., Wang, L., Yang, Z., & Li, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.836882 · Cited by 21

Artificial Intelligence and Machine Learning in Medicinal Chemistry and Validation of Emerging Drug Targets

Quazi, S., Jangi, R., Gavas, S., & Karpiński, T. M. M. (2022) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-7998-8908-3.ch002 · Cited by 17

Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling

Sakyi, P. O., Kwofie, S. K., Tuekpe, J. K. K., Gwira, T. M., Broni, E., Miller, W. A., Wilson, M. D., & Amewu, R. K. (2023) · Pharmaceuticals · doi:10.3390/ph16030330 · Cited by 20

TIWMFLP: Two-Tier Interactive Weighted Matrix Factorization and Label Propagation Based on Similarity Matrix Fusion for Drug-Disease Association Prediction

Liu, T., Wang, S., Zhang, Y., Li, Y., Liu, Y., & Huang, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01589 · Cited by 20

To err is human: Bias salience can help overcome resistance to medical AI

Isaac, M. S., Wang, R. J.-H., Napper, L. E., & Marsh, J. K. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108402 · Cited by 22

Artificial Intelligence Driven Applications in Analytical Chemistry, Drug Discovery, and Food Science: Advancements, Outlook, and Challenges

Srivastava, M., Nandan, S., Zaidi, A., Samani, A. S., Shukla, V., Aslam, H., Srivastava, A., Maurya, P., Khan, M. A., Khan, M. F., et al. (2025) · ChemistrySelect · doi:10.1002/slct.202404446 · Cited by 25

Cross-Regional Analysis of Global AI Healthcare Regulation

Ullagaddi, P. (2025) · Journal of Computer and Communications · doi:10.4236/jcc.2025.135005 · Cited by 18

Mechanism of Antiradical Activity of Newly Synthesized 4,7-Dihydroxycoumarin Derivatives-Experimental and Kinetic DFT Study

Milanović, Ž., Dimić, D., Žižić, M. V., Milenković, D., Marković, Z., & Avdović, E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222413273 · Cited by 19

Deep Learning Promotes the Screening of Natural Products with Potential Microtubule Inhibition Activity

Jia, X., Wang, W., Yin, B., Zhou, L., Zhen, Y., Zhang, L., Zhou, X., Song, H.-N., Tang, Y., & Gao, F. (2022) · ACS Omega · doi:10.1021/acsomega.2c02854 · Cited by 19

Artificial Intelligence Applications in Healthcare

Usmani, U. A., Happonen, A., Watada, J., & Khakurel, J. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3091-3_89 · Cited by 23

Integrating Artificial Intelligence to Biomedical Science: New Applications for Innovative Stem Cell Research and Drug Development

Kim, M., & Hong, S. (2024) · Technologies · doi:10.3390/technologies12070095 · Cited by 28

Less may be more: an informed reflection on molecular descriptors for drug design and discovery

Barnard, T., Hagan, H., Tseng, S., & Sosso, G. C. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00109c · Cited by 19

A patent review of discoidin domain receptor 1 (DDR1) modulators (2014-present)

Guo, J., Zhang, Z., & Ding, K. (2020) · Expert Opinion on Therapeutic Patents · doi:10.1080/13543776.2020.1732925 · Cited by 19

Systematizing serendipity for big science infrastructures: The ATTRACT project

Wareham, J. D., Priego, L. P., Romasanta, A. K. S., Mathiassen, T. B. W., Nordberg, M., & Tello, P. G. (2021) · Technovation · doi:10.1016/j.technovation.2021.102374 · Cited by 19

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition

Bharadwaj, S., Deepika, K., Kumar, A., Jaiswal, S., Miglani, S., Singh, D., Fartyal, P., Kumar, R., Singh, S., Singh, M. P., et al. (2024) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14639 · Cited by 19

Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management

Cao, F., Yang, Y., Guo, C. X., Zhang, H., Yu, Q., & Guo, J. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2484665 · Cited by 19

HealthCare 5.0: An industry 5.0 perspective for next-generation medical systems with synergistic integration of IoT, AI, and 6G

Younesi, A., Oustad, E., Ansari, M., Fahringer, T., & Buyya, R. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101815 · Cited by 24

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

Sakyi, P. O., Broni, E., Amewu, R. K., Miller, W. A., Wilson, M. D., & Kwofie, S. K. (2022) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2022.859981 · Cited by 18

Identification of small molecules as potential inhibitors of interleukin 6: a multi-computational investigation

Tran, Q.-H., Nguyen, Q., Tran, T.-T.-N., Tran, T., Le, M.-T., Trinh, D.-T. T., Tran, V.-T., Tran, V.-H., & Thai, K. (2022) · Molecular Diversity · doi:10.1007/s11030-022-10558-7 · Cited by 18

Application of Artificial Intelligence and Machine Learning in Drug Discovery and Development

Parvathaneni, M., Awol, A. K., Kumari, M., Lan, K., & Lingam, M. (2023) · Journal of Drug Delivery and Therapeutics · doi:10.22270/jddt.v13i1.5867 · Cited by 20

Machine learning for metabolomics research in drug discovery

Martinelli, D. D. (2023) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2023.100101 · Cited by 19

Artificial intelligence tools for optimising recruitment and retention in clinical trials: a scoping review protocol

Lu, X., Chen, M., Lu, Z., Shi, X., & Liang, L. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-080032 · Cited by 23

Opportunities and challenges for targeting HPK1 in cancer immunotherapy

Xu, J., Li, Y., Chen, X., Junyi, Y., Xia, H., Huang, W., & Zeng, S. (2024) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2024.107866 · Cited by 18

Involvement of NF‐κB/PI3K/AKT signaling pathway in the protective effect of prunetin against a diethylnitrosamine induced hepatocellular carcinogenesis in rats

Li, G., Li, Q., Chen, H., & Gendong, T. (2022) · Journal of Biochemical and Molecular Toxicology · doi:10.1002/jbt.23016 · Cited by 18

Artificial Intelligence and Machine Learning‐Based Manufacturing and Drug Product Marketing

Baviskar, K. P., Bedse, A. P., Raut, S., & Darapaneni, N. (2023) · Wiley eBooks · doi:10.1002/9781119865728.ch10 · Cited by 18

Advancements in contemporary pharmacological innovation: Mechanistic insights and emerging trends in drug discovery and development

Majumder, S., & Panigrahi, G. K. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.10.001 · Cited by 21

Computational Landscape in Drug Discovery: From AI/ML Models to Translational Application

Sharma, D., Anabala, M., Jain, V. V., Shyam, M., Prince, S. E., & Muniyan, R. (2025) · Scientifica · doi:10.1155/sci5/1688637 · Cited by 17

Explicating Artificial Intelligence: Applications in Medicine and Pharmacy

Patel, A. I., Khunti, P. K., Vyas, A. J., & Patel, A. B. (2022) · Asian Journal of Pharmacy and Technology · doi:10.52711/2231-5713.2022.00061 · Cited by 17

Identification of Potential Hits against Fungal Lysine Deacetylase Rpd3 via Molecular Docking, Molecular Dynamics Simulation, DFT, In-Silico ADMET and Drug-Likeness Assessment

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