ArticleCommunications chemistry2025
Optimizing drug design by merging generative AI with a physics-based active learning framework.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery.Journal of computer-aided molecular design · 2026Review
- Metabolic reprogramming of myeloid cells in cancer: from lactate-NAMPT axis to AI-guided therapeutics.Experimental & molecular medicine · 2026Review
- DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.Chemical science · 2026Article
- Generative pretraining for drug molecule design with bidirectional structure-property optimization.Communications chemistry · 2026Article
- Identification of potential MenT3 inhibitors for Mycobacterium tuberculosis using the generative artificial intelligence and SilicoXplore platform.Scientific reports · 2026Article
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Efficient Design of AffilinInternational journal of molecular sciences · 2025Article
- Fragment-based drug discovery: A graphical review.Current research in pharmacology and drug discovery · 2025Review
- Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations.Journal of chemical theory and computation · 2024Article
- ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.ArXiv · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning is transforming drug discovery, with generative models (GMs) gaining attention for their ability to design molecules with specific properties. However, GMs often struggle with target engagement, synthetic accessibility, or generalization. To address these, we developed a GM workflow integrating a variational autoencoder with two nested active learning cycles. These iteratively refine their predictions using chemoinformatics and molecular modeling predictors. We tested our workflow on two systems, CDK2 and KRAS, successfully generating diverse, drug-like molecules with high predicted affinity and synthesis accessibility. Notably, we generated novel scaffolds distinct from those known for each target. For CDK2, we synthetized 9 molecules yielding 8 with in vitro activity, including one with nanomolar potency. For KRAS, in silico methods validated by CDK2 assays identified 4 molecules with potential activity. These findings showcase our GM workflow's ability to explore novel chemical spaces tailored for specific targets, thereby opening new avenues in drug discovery.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.