Small Molecule Optimization with Large Language Models

תקציר מקורי באנגליתarXiv:2407.18897v2 Announce Type: replace Abstract: Molecular optimization, the process of designing molecules with desirable properties, represents a critical challenge in drug discovery. Recent advancements in large language models (LLMs) have opened new opportunities for their integration with traditional molecular optimization algorithms to improve performance. In this work, we propose Molecular Language Model powered Evolutionary Algorithm (Mol-E), an evolutionary algorithm that relies on the generative capabilities of LLMs trained on molecules and molecular properties. Scientific Contribution. Mol-E establishes new state-of-the-art results on the Practical Molecular Optimization benchmark, with summed Top-10 AUC values of 17.500 in the task-agnostic regime, in which the oracle is tre