DrugAssist: A Large Language Model for Molecule Optimization

TL;DR

DrugAssist introduces an interactive LLM-based approach for molecule optimization in drug discovery, integrating expert feedback and iterative refinement, achieving leading results and providing a new dataset for future research.

Contribution

The paper presents DrugAssist, a novel interactive LLM framework for molecule optimization that incorporates expert feedback and introduces a large instruction-based dataset for fine-tuning.

Findings

Achieved top performance in single and multiple property optimization.

Demonstrated transferability and effectiveness in iterative optimization.

Released a new dataset, MolOpt-Instructions, for molecule optimization tasks.

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.