Molecular discovery, when formulated as an optimization problem, presents
significant computational challenges because optimization objectives can be
non-differentiable. Evolutionary Algorithms (EAs), often used to optimize
black-box objectives in molecular discovery, traverse chemical space by
performing random mutations and crossovers, leading to a large number of
expensive objective evaluations. In this work, we ameliorate this shortcoming
by incorporating chemistry-aware Large Language Models (LLMs) into EAs. Namely,
we redesign crossover and mutation operations in EAs using LLMs trained on
large corpora of chemical information. We perform extensive empirical studies
on both commercial and open-source models on multiple tasks involving property
optimization, molecular rediscovery, and structure-based drug design,
demonstrating that the joint usage of LLMs with EAs yields superior performance
over all baseline models across single- and multi-objective settings. We
demonstrate that our algorithm improves both the quality of the final solution
and convergence speed, thereby reducing the number of required objective
evaluations. Our code is available at this http URL

融入大型语言模型的进化算法为分子发现问题的优化提供了一个优越性能的解决方案，通过重新设计进化算法的交叉和变异操作，利用大型化学信息语言模型进行大规模的实证研究，在单目标和多目标环境中，对于性能优化、分子重发现和基于结构的药物设计等多个任务表现出优于基线模型的优越性能，提高了最终解的质量和收敛速度，同时减少了所需的目标评估次数。