Unleashing the power of generative AI in drug discovery

Attention Is All You Need

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Variational Inference with Normalizing FlowsPreprint

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A Survey on Deep Transfer Learning

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The ChEMBL database in 2017

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Federated Learning with Non-IID Data

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Semi-Supervised Learning with Deep Generative Models

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Adversarial Autoencoders

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018

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Software for molecular docking: a review

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A survey of transformers

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Deep reinforcement learning for de novo drug design

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Molecular de-novo design through deep reinforcement learning

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Normalizing Flows: An Introduction and Review of Current Methods

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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

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GuacaMol: Benchmarking Models for de Novo Molecular Design

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Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

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Automated design of ligands to polypharmacological profiles

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Machine-learning approaches in drug discovery: methods and applications

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Applications of Deep Learning in Biomedicine

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Virtual Screening Strategies in Drug Discovery: A Critical Review

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Deep learning for molecular design—a review of the state of the art

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Graph Convolutional Policy Network for Goal-Directed Molecular Graph GenerationPreprint

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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

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Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

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Optimization of Molecules via Deep Reinforcement Learning

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Molecular representations in AI-driven drug discovery: a review and practical guide

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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation ModelsPreprint

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Learning Deep Generative Models

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De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

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Molecular generative model based on conditional variational autoencoder for de novo molecular design

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Application of Generative Autoencoder in <i>De Novo</i> Molecular Design

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High-Throughput Screening: today’s biochemical and cell-based approaches

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Reinvent 4: Modern AI–driven generative molecule design

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Randomized SMILES strings improve the quality of molecular generative models

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Reinforced Adversarial Neural Computer for de Novo Molecular Design

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<i>De Novo</i> Design of Bioactive Small Molecules by Artificial Intelligence

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Grammar Variational AutoencoderPreprint

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Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

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Recent Advances in Scaffold Hopping

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Deep learning in drug discovery: opportunities, challenges and future prospects

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The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

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Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery

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AI-powered therapeutic target discovery

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Exposing the Limitations of Molecular Machine Learning with Activity Cliffs

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AI in drug discovery and its clinical relevance

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Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

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An overview of molecular fingerprint similarity search in virtual screening

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ChemTS: an efficient python library for de novo molecular generation

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GraphAF: a Flow-based Autoregressive Model for Molecular Graph GenerationPreprint

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MoFlow: An Invertible Flow Model for Generating Molecular Graphs

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Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks

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Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

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Catastrophic forgetting and mode collapse in GANs

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Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks

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In silico methods to address polypharmacology: current status, applications and future perspectives

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Grammatical error correction using neural machine translation

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Syntax-Directed Variational Autoencoder for Structured DataPreprint

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Adversarial Threshold Neural Computer for Molecular de Novo Design

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Generative molecular design in low data regimes

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DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical StructuresPreprint

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Conditional Molecular Design with Deep Generative Models

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Graph neural networks for automated de novo drug design

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Exploring the GDB-13 chemical space using deep generative models

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Combining generative artificial intelligence and on-chip synthesis for de novo drug design

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AI in small-molecule drug discovery: a coming wave?

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Evaluation guidelines for machine learning tools in the chemical sciences

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MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information

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De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping

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Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

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The drug-maker's guide to the galaxy

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Discovery of Highly Potent, Selective, and Orally Efficacious p300/CBP Histone Acetyltransferases Inhibitors

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<i>De Novo</i> Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence

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DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology

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New avenues in artificial-intelligence-assisted drug discovery

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De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models

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A Grammar-Based Structural CNN Decoder for Code Generation

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Inside the nascent industry of AI-designed drugs

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An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor

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Role of Artificial Intelligence in Transforming Diagnosis, Treatment, and Patient Care: A Review

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Current Approaches and Implications in Discovery of Novel Bioactive Products from Microbial Sources

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Ongoing Implementation and Prospective Validation of Artificial Intelligence/Machine Learning Tools at an African Drug Discovery Center

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The rise of generative AI frameworks in drug discovery

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MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network

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Refined ADME Profiles for ATC Drug Classes

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Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority

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The LSD1/HDAC dual-target inhibitor for cancer therapy: Challenge and opportunity

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Immune-evasive stem cells: engineering tolerance and reprogramming microenvironments for regenerative therapy

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Generative AI in Drug Designing: Current State-of-the-Art and Perspectives

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Framework to identify innovative sources of value creation from platform technologies

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AI-driven drug discovery using a context-aware hybrid model to optimize drug-target interactions

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Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review

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Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision

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Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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A Comprehensive Investigation on Leveraging Generative AI and Large Language Models in the Healthcare Domain

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Utility of Artificial Intelligence in Antibiotic Development: Accelerating Discovery in the Age of Resistance

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Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery

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Artificial Intelligence

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Diff4VS: HIV-inhibiting Molecules Generation with Classifier Guidance Diffusion for Virtual Screening

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Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review

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Persona-driven generative AI in pharmaceuticals

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Computation-driven discovery of novel chromone derivatives for the treatment of triple-negative breast cancer via mTOR-targeted inhibition and triggering antitumor immunity

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Interpretable machine learning rationalizes carbonic anhydrase inhibition via conformal and counterfactual prediction

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Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy

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A Centrality-Weighted Bidirectional Encoder Representation from Transformers Model for Enhanced Sequence Labeling in Key Phrase Extraction from Scientific Texts

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Evaluating AutoGrow4 – an open-source toolkit for semi-automated computer-aided drug discovery

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Global reform population health management as stewarded by Higher Expert Medical Science Safety (HEMSS)

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Molecular Optimization Based on a Monte Carlo Tree Search and Multiobjective Genetic Algorithm

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Phenotypic and targeted drug discovery in immune therapeutics: challenges, opportunities, and future directions

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Empowering scientific discovery with explainable small domain-specific and large language models

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Transforming drug discovery through the fusion of AI-driven analysis and protein micropatterning

Roach, P. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2025.2567300 · Cited by 3 · see citing works

Deep learning-assisted discovery of a potent and cell-active inhibitor of RNA N6-methyladenosine recognition protein YTHDC2

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Generative AI in pharmaceutical R&D: From large language models to AI agents to regulation

Weatherall, J., & Meier, C. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104593 · Cited by 3 · see citing works

A large language model-guided reinforcement learning framework for EGFR anticancer drug design

Chai, Y., & Huang, X. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-025-00753-7 · Cited by 3 · see citing works

Artificial Intelligence in Drug Discovery and Pharmaceutical Development: Machine Learning Approaches for Molecular Design, Nanomedicine, and Precision Therapeutics

Dave, P., Gandhi, A., & Dudhat, K. R. (2026) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-026-10634-7 · Cited by 3 · see citing works

The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care

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In silico drug discovery pipelines targeting antibiotic resistance: from genomes to leads

Gupta, R., Singh, P., Ravi, K., Abomughaid, M. M., Lakhanpal, S., Sharma, R. V., Kumar, N., & Jha, N. K. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1839606 · Cited by 3 · see citing works

Artificial Intelligence‐Driven Natural Product Drug Discovery: From Computational Genome Mining to Clinical Translation

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Inhibitors as a therapeutic frontier in lung cancer: Mechanism, opportunities, and molecular docking studies

Patel, R., Rathod, D., Shah, N. J., Vaghela, V., & Patadiya, N. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110889 · Cited by 3 · see citing works

Biological Function Analysis of MicroRNAs and Proteins in the Cerebrospinal Fluid of Patients with Parkinson’s Disease

Hwang, J. S., Kim, S. G., George, N. P., Kwon, M., Jang, Y. E., Lee, S. S., & Lee, G. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413260 · Cited by 3 · see citing works

Generative AI for Healthcare: Concepts and Applications

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Legal questions of AI-generated immunological products for infectious diseases

Zumárraga, J., McConnell, M. J., López, D., & Martín-Galiano, A. J. (2025) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2025.2570962 · Cited by 2 · see citing works

Mechanistic insights into marine-derived PDE6D inhibitors disrupting prenyl-binding to modulate leukemia-associated RAS trafficking

Alam, P., Akhtar, A., Ahmed, S., & Hasan, Z. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108821 · Cited by 2 · see citing works

From lesion detection to outcome prediction: artificial intelligence and deep learning applications in multiple sclerosis

Prasad, A., & Sharma, A. (2025) · Neurological Sciences · doi:10.1007/s10072-025-08661-2 · Cited by 2 · see citing works

Therapeutic targeting LRPPRC-mediated OXPHOS synthesis for cancer intervention

Liang, Y., Wang, L., Yang, Z., Chen, W., Jiang, R., Wang, D., Wang, W., Zhou, W., & Fang, X. (2025) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2025.2608028 · Cited by 2 · see citing works

Harnessing AI in drug discovery without losing patent protection

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Artificial Intelligence in Drug Discovery

Aadesh Varpe, O. U. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.17444646

Generative artificial intelligence for de novo drug design

Ali, A. M., Qazi, S., Almuqdadi, H. T. A., Neeraj, Majeed, O. S., Majeed, A. A., Temur, A. A., Murshedi, G. H. M. A., & Waheed, M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44415-9.00018-1

Application of Generative AI in Drug Discovery

Farhadi, A., Zamanifar, A., & Faezipour, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-82963-5_6 · Cited by 13 · see citing works

Artificial Intelligence in De Novo Drug Design

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