Molecular Optimization Based on a Monte Carlo Tree Search and Multiobjective Genetic Algorithm

In the realm of medicinal chemistry, the predominant challenge in molecular design lies in managing extensive molecular data sets and effectively screening for, as well as preserving, molecules with potential value. Traditional methodologies typically utilize deep learning models or genetic algorithms (GA) for optimization, yet each approach has inherent limitations: deep learning models are constrained by substantial computational resource demands; genetic algorithms often yield molecular structures with low validity and feasibility. To overcome these challenges, we have developed the Molecular multiobjective optimization of Monte Carlo Tree Search (MCTS) and Non-Superiority Ranking Genetic Algorithm II (NSGA-II)-MNopt, which ingeniously integrates MCTS with NSGA-II. Specifically, NSGA-II demonstrates unique strengths in balancing multiple optimization objectives and achieves rapid performance through its crowding distance and nondominated ordering mechanisms, while MCTS focuses on enhancing the validity of molecular structures to ensure that the generated molecules are both desirable and feasible. Notably, MNopt does not require reliance on extensive molecular training data sets in the initial stages, effectively mitigating excessive resource consumption. Experimental results demonstrate that MNopt surpasses existing techniques in multiobjective optimization, generating effective and diverse molecular structures, thereby offering a crucial tool for novel drug discovery and materials science.