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 http://github.com/zoom-wang112358/MOLLEO

翻译：分子发现被形式化为优化问题时，由于优化目标可能不可微分，会带来显著的计算挑战。进化算法通常用于优化分子发现中的黑盒目标，其通过执行随机突变和交叉操作遍历化学空间，导致需要进行大量昂贵的目标准确评估。本研究通过将化学感知的大语言模型融入进化算法来改善这一缺陷。具体而言，我们利用在大型化学信息语料库上训练的大语言模型，重新设计了进化算法中的交叉和突变操作。我们在涉及性质优化、分子再发现和基于结构的药物设计等多个任务上，对商业和开源模型进行了广泛的实证研究，结果表明：在单目标和多目标设置下，大语言模型与进化算法的联合使用均优于所有基线模型。我们证明该算法同时提升了最终解的质量和收敛速度，从而减少了所需的目标准确评估次数。代码已开源：http://github.com/zoom-wang112358/MOLLEO