SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.

ABSTRACT: Generative machine learning models for small molecule drug discovery have shown immense promise, but many molecules they generate are too difficult to synthesize, making them impractical for further investigation or development. In this work, we present a novel approach by fine-tuning Meta's Llama3 Large Language Models (LLMs) to create SynLlama, which generates full synthetic pathways made of commonly accessible building blocks and robust organic reaction templates. SynLlama explores a large synthesizable space using significantly less data compared to other state-of-the-art methods, and offers strong performance in bottom-up synthesis, synthesizable analog generation, and hit expansion, offering medicinal chemists a valuable tool for drug discovery developments. We find that SynLlama, ev

SUBMITTER: Sun K 

PROVIDER: S-EPMC12047903 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature