  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
