New framework uses LLMs for AI-driven drug discovery optimization

Researchers have developed SABLE (Synthetically-accessible Agentic Bayesian Ligand Exploration), an open-source framework designed to streamline hit-to-lead optimization in drug discovery. SABLE utilizes natural-language instructions to guide chemical structure design, integrating an LLM with specialized tools for analog enumeration, property prediction, and Bayesian optimization. This modular system aims to accelerate the design-make-test-analyze cycle by providing a computational twin for analytical and prioritization stages, ultimately enriching candidate sets for desired computational objectives. AI

IMPACT This framework could accelerate early-stage drug discovery by improving the efficiency of computational design and prioritization of synthetic analogs.

RANK_REASON The cluster contains a research paper detailing a new framework for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Bayesian Ligand Exploration
• Hugging Face
• Kelvin Idanwekhai
• SABLE

• paper
• product

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