ArticleJournal of computer-aided molecular design2024
De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning.
Article in Journal of computer-aided molecular design, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- RelAgent: a multi-agent solution for molecular relationship grounding.Bioinformatics (Oxford, England) · 2026Article
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Medt5-bi: bidirectional translation between drug indications and molecular structures using a chemically-aware transformer.Journal of computer-aided molecular design · 2025Article
- AgentMol: Multi-Model AI System for Automatic Drug-Target Identification and Molecule Development.Methods and protocols · 2025Article
- Review
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Abstract
In recent years, generative machine learning algorithms have been successful in designing innovative drug-like molecules. SMILES is a sequence-like language used in most effective drug design models. Due to data's sequential structure, models such as recurrent neural networks and transformers can design pharmacological compounds with optimized efficacy. Large language models have advanced recently, but their implications on drug design have not yet been explored. Although one study successfully pre-trained a large chemistry model (LCM), its application to specific tasks in drug discovery is unknown. In this study, the drug design task is modeled as a causal language modeling problem. Thus, the procedure of reward modeling, supervised fine-tuning, and proximal policy optimization was used to transfer the LCM to drug design, similar to Open AI's ChatGPT and InstructGPT procedures. By combining the SMILES sequence with chemical descriptors, the novel efficacy evaluation model exceeded its performance compared to previous studies. After proximal policy optimization, the drug design model generated molecules with 99.2% having efficacy pIC
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