ArticleBriefings in bioinformatics2024
DrugAssist: a large language model for molecule optimization.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- Prompt-to-Pill: Multi-Agent Drug Discovery and Clinical Simulation Pipeline.Bioinformatics advances · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Diffusion-based generative drug-like molecular editing with chemical natural language.Journal of pharmaceutical analysis · 2025Article
- Artificial Intelligence in Molecular Optimization: Current Paradigms and Future Frontiers.International journal of molecular sciences · 2025Review
- Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations.Journal of chemical information and modeling · 2025Article
- Bioinformatics and biomedical informatics with ChatGPT: Year one review.Quantitative biology (Beijing, China) · 2024Article
- LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset.... Conference on Language Modeling · 2024Article
- ChatMol: interactive molecular discovery with natural language.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback. These non-interactive approaches overlook the fact that the drug discovery process is actually one that requires the integration of expert experience and iterative refinement. To address this gap, we propose DrugAssist, an interactive molecule optimization model which performs optimization through human-machine dialogue by leveraging LLM's strong interactivity and generalizability. DrugAssist has achieved leading results in both single and multiple property optimization, simultaneously showcasing immense potential in transferability and iterative optimization. In addition, we publicly release a large instruction-based dataset called 'MolOpt-Instructions' for fine-tuning language models on molecule optimization tasks. We have made our code and data publicly available at https://github.com/blazerye/DrugAssist, which we hope to pave the way for future research in LLMs' application for drug discovery.
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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.