Deep learning for small-molecule drug discovery: From molecular design to clinical translation

Rita I. Oliveira; Tiago O. Pereira; Maryam Abbasi; Jorge A.R. Salvador; Joel P. Arrais

doi:10.1016/j.jpha.2025.101533

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Rita I. Oliveira, Tiago O. Pereira, Maryam Abbasi, Jorge A.R. Salvador, Joel P. Arrais. Deep learning for small-molecule drug discovery: From molecular design to clinical translation[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101533

Citation:

Rita I. Oliveira, Tiago O. Pereira, Maryam Abbasi, Jorge A.R. Salvador, Joel P. Arrais. Deep learning for small-molecule drug discovery: From molecular design to clinical translation[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101533

Citation:

Rita I. Oliveira, Tiago O. Pereira, Maryam Abbasi, Jorge A.R. Salvador, Joel P. Arrais. Deep learning for small-molecule drug discovery: From molecular design to clinical translation[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101533

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Deep learning for small-molecule drug discovery: From molecular design to clinical translation

doi: 10.1016/j.jpha.2025.101533

a. University of Coimbra, Laboratory of Pharmaceutical Chemistry, Faculty of Pharmacy, Coimbra, 3000-548, Portugal;
b. University of Coimbra, Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, Coimbra, 3004-504, Portugal;
c. University of Coimbra, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, 3030-290, Portugal;
d. Polytechnic University of Coimbra, Coimbra, 3030-785, Portugal;
e. Research Centre for Natural Resources Environment and Society (CERNAS), Polytechnic University of Coimbra, Coimbra, 3045-601, Portugal

Funds:

This work is financed through national funds by Foundation for Science and Technology (FCT), Portugal, (Project Nos.: UIDB/00326/2025 and UIDP/00326/2025). Rita I. Oliveira and Tiago O. Pereira thank the FCT for funding the individual PhD grants (Grant Nos.: 2021.07538.BD and 2021.151089.BD). Maryam Abbasi thanks the FCT through the institutional scientific employment program-contract (Contract No.: CEECINST/00077/2021).

Received Date: Jul. 09, 2025
Accepted Date: Dec. 17, 2025
Rev Recd Date: Dec. 17, 2025
Available Online: Dec. 27, 2025

Abstract

Abstract

Recent advances in artificial intelligence (AI) are increasingly transforming drug discovery, offering new approaches to accelerate the identification and optimization of therapeutic candidates. Deep learning (DL) methods have shown strong potential for generative modeling and molecular property prediction, enabling the design of compounds with tailored pharmacological profiles. In this review, we synthesize recent AI-driven progress in drug discovery, highlighting both achievements and ongoing challenges. We discuss key technical aspects underpinning DL-based models, including the use of curated molecular databases, molecular descriptors, and standardized evaluation strategies. We examine representative architectures and illustrate how they have been applied to molecular generation, binding affinity prediction, and multi-modal integration of ligand and protein data. In addition, we provide a comprehensive analysis of AI-enabled small-molecule drugs, including discovered and repurposed molecules in clinical trials, and explore the intellectual property information and chemical structures of these compounds. We demonstrate that AI-native companies are evolving pharmaceutical pipelines by integrating these tools at various stages of development. We also address the regulatory framework of agencies such as the United States Food and Drug Administration (FDA) and the European Medicines Agency (EMA) as they adapt to integrate AI innovations. Finally, we outline persistent limitations, such as data inconsistency, model interpretability, and the gap between benchmark performance and real-world applicability, which remain critical barriers to widespread adoption. By consolidating technical advances and open challenges, this review aims to provide a balanced perspective on the role of AI in reshaping modern drug discovery.
- Artificial intelligence,
- Drug design,
- Research and development,
- Deep learning,
- Generative model

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References(150)

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