A Molecular Language Model Driven Genetic Algorithm for Multi-Objective Molecular Generation

De novo therapeutic design is a complex multi-objective optimization problem over an enormous chemical space. Traditional virtual screening methods and deep generative models are constrained by predefined molecular libraries, unstable reinforcement learning fine-tuning, and weak controllability over multiple pharmacological objectives. Evolutionary algorithms (EAs) offer robust population-based search but often rely on heuristic, rule-based mutation operators that confine exploration to known scaffolds and lead to premature convergence. We introduce FragEvo, a novel language-model-guided evolutionary framework that deeply integrates a fragment-based molecular language model (FragMLM) with multi-objective optimization through NSGA-II. Its core innovation lies in embedding a pretrained FragMLM directly into the genetic variation process, where the model's contextual priors are used to parameterize the mutation and crossover distributions. This transforms genetic operators from static, rule-driven mechanisms into probabilistic, language-informed generators capable of producing chemically valid and semantically novel offspring. An adaptive feedback loop further aligns the semantic likelihood of generated molecules with evolving fitness signals, enabling a dynamic balance between exploration and exploitation. Across multiple molecular optimization benchmarks, FragEvo achieves stronger docking performance and Pareto-front quality while maintaining competitive scaffold diversity and favorable drug-likeness/syntheticaccessibility profiles, supporting a language-model-guided evolutionary computation paradigm for in silico molecular design.