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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A comprehensive review on the application of artificial intelligence in drug discovery.

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ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN

Kalayil, N., D'Souza, S. S., Khan, S., & PAUL, P. (2022) · Asian Journal of Pharmaceutical and Clinical Research · doi:10.22159/ajpcr.2022.v15i4.43890 · Cited by 9

The future of artificial intelligence in nursing

Özsezer, G. (2022) · Journal of Human Sciences · doi:10.14687/jhs.v19i2.6217 · Cited by 18

Use of Artificial Intelligence in Drug Discovery and its Application in Drug Development

Shaikh, H. S. (2023) · Asian Journal of Research in Chemistry · doi:10.52711/0974-4150.2023.00014 · Cited by 9

Electroporation combined with intelligent drug delivery promising new and clean approach to cancer treatment

Aghayari, S. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101379 · Cited by 9

Dynamical diversity of mitochondrial BK channels located in different cell types

Wawrzkiewicz–Jałowiecka, A., Trybek, P., Machura, Ł., & Bednarczyk, P. (2020) · Biosystems · doi:10.1016/j.biosystems.2020.104310 · Cited by 9

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug

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Artificial Intelligence and Machine Learning in Drug Discovery and Development

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GeneDisco: A Benchmark for Experimental Design in Drug DiscoveryPreprint

Mehrjou, A., Soleymani, A., Jesson, A., Notin, P., Gal, Y., Bauer, S., & Schwab, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11875 · Cited by 18

Machine Learning Approaches to Improve Prediction of Target‐Drug Interactions

Balatti, G. E., Barletta, P. G., D. Perez, A., Giudicessi, S. L., & Martínez‐Ceron, M. C. (2022) · Wiley eBooks · doi:10.1002/9781394167258.ch2 · Cited by 9

AI in Clinical Trials and Drug Development: Challenges and Potential Advancements

Gupta, D., Wal, P., Wal, A., R, S., Kumar, M., Panda, K. C., & Sharma, M. C. (2024) · Current Drug Discovery Technologies · doi:10.2174/0115701638314252241016165345 · Cited by 8

From Patterns to Pills: How Informatics Is Shaping Medicinal Chemistry

Trachtenberg, A., & Akabayov, B. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17050612 · Cited by 8

Artificial intelligence in predicting personalized nanocarrier formulations for herbal drugs: Bridging phytomedicine and precision nanotechnology

Sridhar, D., Manikandan, R., Dhanapal, Y., Khute, S., & Subash, P. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2025.08.001 · Cited by 12

Enhancing Microparticle Separation Efficiency in Acoustofluidic Chips via Machine Learning and Numerical Modeling

Klymkovych, T., Bokla, N., Zabierowski, W., & Klymkovych, D. (2025) · Sensors · doi:10.3390/s25206427 · Cited by 8

Gut microbiota-liver-kidney axis in diabetic kidney disease: mechanistic insights into amino acid metabolism and nutritional intervention strategies targeting natural bioactive compounds

Sun, L.-Y., Sun, G.-Y., Nan, Y., Wang, H., Sun, Y., Shi, Y., Yang, Y., & Wang, Q.-F. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1778473 · Cited by 8

Industry 4.0 (IR 4.0) for Pharmaceutical Industry: An Exploratory Review

Mukhlas, A., Talip, B. A., Mustapha, J. C., & Ismail, S. (2022) · International Visualization, Informatics and Technology Conference (IVIT) · doi:10.1109/ivit55443.2022.10033386 · Cited by 8

Understanding the Landscape: The Emergence of Artificial Intelligence (AI), ChatGPT, and Google Bard in Gastroenterology

Rammohan, R., Joy, M. V., Magam, S. G., Natt, D., Magam, S. R., Pannikodu, L. E., Desai, J., Akande, O., Bunting, S., Yost, R. M., et al. (2024) · Cureus · doi:10.7759/cureus.51848 · Cited by 9

REVOLUTIONIZING DRUG DELIVERY INNOVATION: LEVERAGING AI-DRIVEN CHATBOTS FOR ENHANCED EFFICIENCY

Malkawi, R. (2024) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2024v16i2.50182 · Cited by 8

AI and The Future of Medicine: Pioneering Drug Discovery with Language Models

Nuthakki, S. K. S. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24304173757 · Cited by 14

Artificial Intelligence in Pharmaceutical Drug Delivery

Agrawal, G., Tushir, S., Tushir, S., Arora, D., & Sangwan, K. (2024) · International Conference on Computational Intelligence and Computing Applications (ICCICA) · doi:10.1109/iccica60014.2024.10585200 · Cited by 8

Doctor AI? A pilot study examining responses of artificial intelligence to common questions asked by geriatric patients

Moore, I., Magnante, C., Embry, E., Mathis, J., Mooney, S., Haj-Hassan, S., Cottingham, M., & Padala, P. R. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1438012 · Cited by 8

Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design

Dash, T., Srinivasan, A., Vig, L., & Roy, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97454-1_6 · Cited by 8

Targeting Leishmania donovani sterol methyltransferase for leads using pharmacophore modeling and computational molecular mechanics studies

Sakyi, P. O., Broni, E., Amewu, R. K., Miller, W. A., Wilson, M. D., & Kwofie, S. K. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101162 · Cited by 8

Artificial Intelligence for Computer-Aided Drug Discovery

Kate, A., Seth, E., Singh, A., Chakole, C. M., Chauhan, M. K., Singh, R. K., Maddalwar, S. R., & Mishra, M. S. (2023) · Drug Research · doi:10.1055/a-2076-3359 · Cited by 8

Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results

Richter, M., Trybek, P., Bednarczyk, P., & Wawrzkiewicz–Jałowiecka, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22020840 · Cited by 8

Images of chemical structures as molecular representations for deep learning

Wilkinson, M. R., Martínez-Hernández, U., Wilson, C. C., & Dominguez, B. C. (2022) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/s43578-022-00628-9 · Cited by 8

Small-molecule inhibitors get pro-inflammatory TNF into shape

Savvides, S. N., & Elewaut, D. S. (2020) · Nature Reviews Rheumatology · doi:10.1038/s41584-020-0388-2 · Cited by 8

Screening of potential inhibitors of Leishmania major N-myristoyltransferase from Azadirachta indica phytochemicals for leishmaniasis drug discovery by molecular docking, molecular dynamics simulation and density functional theory methods

Tewari, D., Rawat, K., Bisht, A., Almoyad, M. A. A., Wahab, S., Chandra, S., & Pande, V. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2279281 · Cited by 7

Harnessing Computational Strategies to Overcome Challenges in mRNA Vaccines

Zhao, S., Chen, J., Dai, T., Li, G., Huang, L., Xin, J., Zhang, Y., Chen, Y., He, X., Huang, H., et al. (2025) · Physiology · doi:10.1152/physiol.00047.2024 · Cited by 7

Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research

Kumar, P., Chaudhary, B., Arya, P., Chauhan, R., Devi, S., Parejiya, P. B., & Gupta, M. M. (2025) · Bioengineering · doi:10.3390/bioengineering12040363 · Cited by 10

Harnessing AI and Quantum Computing for Revolutionizing Drug Discovery and Approval Processes: Case Example for Collagen Toxicity

Braga, D. M., & Rawal, B. (2025) · JMIR Bioinformatics and Biotechnology · doi:10.2196/69800 · Cited by 7

Leveraging large language models in pharmacometrics: evaluation of NONMEM output interpretation and simulation capabilities

Cha, H. J., Choe, K., Shin, E., Ramanathan, M. K., & Han, S. (2025) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-025-09982-7 · Cited by 9

The rise of generative AI frameworks in drug discovery

Kotkondawar, R. R., Sutar, S. R., Kiwelekar, A. W., Kadam, V. J., & Jadhav, S. M. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00831-x · Cited by 8

A Systematic Review of AI-Driven Innovations in Drug Development, Precision Medicine, and Healthcare

Uriti, S. V. (2025) · Journal of Pharma Insights and Research. · doi:10.69613/nvj8x122 · Cited by 7

Bridging traditional and contemporary approaches in computational medicinal chemistry: opportunities for innovation in drug discovery

de Oliveira, A. S. (2025) · RSC Medicinal Chemistry · doi:10.1039/d5md00700c · Cited by 10

Artificial intelligence and multi-omics in drug discovery: A deep learning-powered revolution

Vora, N., Shah, S., Patel, P., & Shah, M. (2025) · Cure & Care · doi:10.1016/j.ccwv.2025.100011 · Cited by 11

Next-generation drug discovery: The AI revolution in pharmaceutical research

Bilotta, M., Rocca, R., & Alcaro, S. (2025) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2025.100149 · Cited by 11

From Algorithm to Medicine: AI in the Discovery and Development of New Drugs

Lopes, A. B. S., Rodrigues, C. F., & Silva, F. A. M. (2026) · AI · doi:10.3390/ai7010026 · Cited by 10

From Algorithms to Assets: A Comprehensive Review of AI’s Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation

Cai, M., & Liu, T. (2026) · Pharmaceuticals · doi:10.3390/ph19050696 · Cited by 8

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer

Das, K., Paltani, M., Tripathi, P. K., Kumar, R., Verma, S., Kumar, S., & Jain, C. K. (2023) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2023.00197 · Cited by 8

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Yin, M., Xu, C., Zhu, J., Xue, Y., Zhou, Y., He, Y., Lin, J., Liu, L., Gao, J., Liu, X., et al. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01211-w · Cited by 7

Applications of AI in Drug Discovery

Rahate, K., & Mondal, R. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-2238-3.ch005 · Cited by 8

Antitumoral activity of different Amaryllidaceae alkaloids: In vitro and in silico assays

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Assessing the Impact of AI: The Case of the Pharmaceutical Industry

Rakočević, T., & Marković, M. (2024) · European Journal of Business Management and Research · doi:10.24018/ejbmr.2024.9.5.2461 · Cited by 8

Exploring the room for repurposed hydroxychloroquine to impede COVID-19: toxicities and multipronged combination approaches with pharmaceutical insights

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Artificial Intelligence: An Emerging Intellectual Sword for Battling Carcinomas

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FDA Modernization Act 2.0: An insight from nondeveloping country

Akash, S. R., Arnob, M. A. J. B., & Uddin, M. J. (2023) · Drug Development Research · doi:10.1002/ddr.22108 · Cited by 8

A REVIEW PAPER ON INDUSTRIES REVOLUTION 4.0 POWERING THE FUTURE OF HEALTH CARE SECTOR

Mishra, S., Singh, S., Kreetika, Joshi, S., Tomar, R., & Alpana (2023) · International Journal of Pharma Professional’s Research (IJPPR) · doi:10.48165/ijppronline.2023.14201 · Cited by 7

Prediction of antischistosomal small molecules using machine learning in the era of big data

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A general prediction model for compound-protein interactions based on deep learning

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Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models

Lai, C. H.-L., Kwok, A. P. K., & Wong, K.-C. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14090981 · Cited by 6

A Wide Application of Artificial Intelligence in Pharma Field

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A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023)

Trejo-Castro, A. I., Martínez-Ledesma, E., & Martínez‐Torteya, A. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42750 · Cited by 6

On the usage of artificial intelligence in leprosy care: A systematic literature review

de Andrade, H. G. V., Rocha, É. D. S., Monteiro, K. H. D. C., de Morais, C. M., dos Santos, D. C. M., Nascimento, D. C., Dourado, R. A. D. S., Lynn, T., & Endo, P. T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012550 · Cited by 6

Advancements in breast cancer therapy: Integrating AI tools for drug discovery and clinical trials

Pandey, V., Verma, P., Natesan, G., & Chaube, U. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111067 · Cited by 6

A comprehensive review on magnetic nanoparticles (ferrites): synthesis, surface functionalization and therapeutic applications

Phor, L., Suryawanshi, M. V., Acharya, A., Pallavi, M. S., Dash, A., Chauhan, A. S., Singh, A., & Chahal, S. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.126304 · Cited by 7

Advancing cancer care through artificial intelligence: from innovative models to clinical decision-making and regulatory integration

Subhan, A., & Manoharan, G. (2025) · Clinical Cancer Bulletin · doi:10.1007/s44272-025-00052-0 · Cited by 9

An artificial neural network–based deep learning model to predict combined stress impact and interaction in plants

Priya, P., Pandey, P., Jain, R., Kandpal, M., Jain, S., Chaudhury, R., Ramegowda, V., & Senthil‐Kumar, M. (2026) · Applications in Plant Sciences · doi:10.1002/aps3.70047 · Cited by 6

İLAÇ KEŞFİ VE GELİŞTİRİLMESİNDE YAPAY ZEKÂ

Çelik, İ. N., ARSLAN, F. K., Tunç, R., & Yıldız, İ. (2021) · Ankara Universitesi Eczacilik Fakultesi Dergisi · doi:10.33483/jfpau.878041 · Cited by 6

Insight into Quantum Computing and Deep Learning Approach for Drug Design

Paliwal, D., Gudhanti, S. N. K. R., Yadav, D., & Raj, P. (2023) · Letters in Drug Design & Discovery · doi:10.2174/1570180820666230427151812 · Cited by 6

Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development

Chen, X., Zhang, J., Zhao, Q., Ding, L. J., Wu, Z., Jia, Z., & He, D. (2023) · Open Life Sciences · doi:10.1515/biol-2022-0675 · Cited by 8

Computational resources and chemoinformatics for translational health research

Tripathi, T., Singh, D. B., & Tripathi, T. (2024) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2023.11.003 · Cited by 6

Revolutionizing Cancer Research and Drug Discovery: The Role of Artificial Intelligence and Machine Learning

Paliwal, A., Alam, A., Sharma, P. A., Jain, S., & Dhoundiyal, S. (2024) · Current Cancer Therapy Reviews · doi:10.2174/0115733947288355240305080236 · Cited by 7

A Comprehensive Review of Artificial Intelligence and Machine Learning Methods for Modern Healthcare Systems

Ahmed, K. M., Das, B. C., Saadati, Y., & Amini, M. H. (2024) · Big and integrated artificial intelligence · doi:10.1007/978-3-031-57567-9_4 · Cited by 6

GDMol: Generative Double‐Masking Self‐Supervised Learning for Molecular Property Prediction

Liu, Y., Fan, Q., Xu, C., Ning, X., Wang, Y., Liu, Y., Xie, Y., Zhang, Y., Chen, Y., & Liu, H. (2024) · Molecular Informatics · doi:10.1002/minf.202400146 · Cited by 6

Machine Learning and Artificial Intelligence in Modern Medicine

Rahmani, P., Mortazavi, P., Meybodi, H. R. A., Patrinos, G. P., Sarhangi, N., Nikfar, S., Larijani, B. A., Khodayari, N., & Hasanzad, M. (2024) · Springer eBooks · doi:10.1007/978-981-97-4974-4_4 · Cited by 6

Ascent of Artificial Intelligence (AI) in Pharmacy

Ingale, S., Shrisunder, N., Gophane, G., & Birajdar, A. (2024) · International Journal of Technology · doi:10.52711/2231-3915.2024.00008 · Cited by 6

Sağlık Sektöründe Karar Destek Araçları: İş Zekâsı, Makine Öğrenmesi, Derin Öğrenme ve Yapay Zeka Uygulamaları

Damar, M. (2024) · İzmir Sosyal Bilimler Dergisi · doi:10.47899/ijss.1591168 · Cited by 6

Ethical and Regulatory Consideration in AI-Assisted Drug Development

Warokar, P., & Lote, S. B. (2024) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei61867.2024.10842700 · Cited by 9

Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development

Kumar, P., Mahor, A., & Tomar, R. (2025) · Current Drug Discovery Technologies · doi:10.2174/0115701638364199250123062248 · Cited by 6

Emerging horizons of AI in pharmaceutical research

Bachhar, S., Kumar, S. P. J., Dutta, B., & Das, S. (2025) · Advances in pharmacology · doi:10.1016/bs.apha.2025.01.016 · Cited by 6

Artificial Intelligence in HIV Diagnosis and Treatment: A Comprehensive Review

Senel, B., & Beka, H. (2025) · Cumhuriyet Tıp Dergisi/Cumhuriyet Üniversitesi Tıp Fakültesi dergisi · doi:10.7197/cmj.1630164 · Cited by 6

Clearance prediction for Amgen molecules against Extended Clearance Classification System (ECCS) and future directions

Agarwal, P., Ishida, K., Reid, D. L., & Gupta, A. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.008 · Cited by 6

Use of Artificial Intelligence in Research and Clinical Decision Making for Combating Mycobacterial Diseases

Sharma, M., & Singh, P. (2021) · Springer eBooks · doi:10.1007/978-981-16-0811-7_9 · Cited by 6

Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach

Srivastava, R. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.99018 · Cited by 7

Artificial intelligence-based drug screening and drug repositioning tools and their application in the present scenario

Krishnamurthy, A., & Goel, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91172-6.00010-8 · Cited by 6

GB‐score: Minimally designed machine learning scoring function based on distance‐weighted interatomic contact features

Rayka, M., & Firouzi, R. (2023) · Molecular Informatics · doi:10.1002/minf.202200135 · Cited by 6

Inteligencia artificial en el diseño de fármacos: hacia la inteligencia aumentada

Saldívar‐González, F. I., Gortari, E. F., & Medina‐Franco, J. L. (2023) · Educación Química · doi:10.22201/fq.18708404e.2023.2.83233 · Cited by 6

Artificial intelligence representation model for drug–target interaction with contemporary knowledge and development

Arvindhan, M., Daniel, A., Partheeban, N., & Balusamy, B. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00005-9 · Cited by 6

Geometry-Augmented Molecular Representation Learning for Property Prediction

Zhang, Y., & Bai, X. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3402337 · Cited by 7

How AI Can Revolutionize the Pharmaceutical Industry

Dave, P. (2024) · Journal of Drug Delivery and Therapeutics · doi:10.22270/jddt.v14i6.6657 · Cited by 6

Exploring AI's Role in Supporting Diversity and Inclusion Initiatives in Multicultural Marketplaces

Mariyono, D., & Hidayatullah, A. N. A. (2024) · International Journal of Religion · doi:10.61707/apmwg008 · Cited by 10

Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition

Behei, N., Тryhubchak, О. V., & Pryymak, B. I. (2022) · Pharmacia · doi:10.3897/pharmacia.69.e86876 · Cited by 6

Pushing Boundaries

Gadade, D. D., Kulkarni, D. A., Raj, R., Patil, S. G., & Modi, A. (2024) · Wiley eBooks · doi:10.1002/9781394234196.ch17 · Cited by 6

Multi-granularity physicochemical-inspired molecular representation learning for property prediction

Guan, K., Wang, H., Zhuang, L., & Sun, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126115 · Cited by 5

Add-GNN: A Dual-Representation Fusion Molecular Property Prediction Based on Graph Neural Networks with Additive Attention

Zhou, R., Zhang, Y., He, K., & Liu, H. (2025) · Symmetry · doi:10.3390/sym17060873 · Cited by 6

Artificial Intelligence and Omics in Health and Diseases

Sabha, S. U. (2025) · Springer eBooks · doi:10.1007/978-981-96-8176-1_5 · Cited by 5

New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology

Jiang, S., Tian, Z., Yang, Y., Li, X., Zhou, F., Cheng, J., Lyu, J., Gao, T., Zhang, P., Han, H., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.08.015 · Cited by 6

TECH-DRIVEN TRUST: THE ROLE OF AI AND EMERGING TECHNOLOGIES IN PHARMACEUTICAL QUALITY ASSURANCE

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