ArticleJournal of computer-aided molecular design2026
A large language model-guided reinforcement learning framework for EGFR anticancer drug design.
Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- NetPolicy-RL: network-informed offline reinforcement learning for pharmacogenomic drug prioritization.Journal of computer-aided molecular design · 2026Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We introduce a generative drug-design framework that combines large chemical language models (CLMs) pretraining, target specific masked-language fine-tuning, and reinforcement learning (RL) to create novel small molecule inhibitors of EGFR. Using a multi-objective reward that balances predicted potency, drug-likeness, synthetic accessibility, and structural novelty, the model learns to explore chemically valid and diverse regions of EGFR-relevant chemical space beyond known inhibitors. The resulting compounds exhibit improved computational binding trends relative to reference EGFR inhibitors and include highly novel chemotypes with no close analogs in the training set. This study demonstrates how integrating pretrained chemical language models with reinforcement learning can accelerate target focused de novo molecular design and provides a generalizable framework for future applications in kinase inhibitor discovery.
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Registered trials
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