Multi-Objective Drug Molecule Optimization Based on Tanimoto Crowding Distance and Acceptance Probability

Background: Traditional molecular optimization methods struggle with high data dependency and significant computational demands. Additionally, conventional genetic algorithms often produce solutions with high similarity, leading to potential local optima and reduced molecular diversity, thereby limiting the exploration of chemical space. Methods: In order to address the above issues, this paper proposes an improved genetic algorithm for multi-objective drug molecular optimization (MoGA-TA). It uses the Tanimoto similarity-based crowding distance calculation and a dynamic acceptance probability population update strategy. The study employs a decoupled crossover and mutation strategy within chemical space for molecular optimization. The proposed crowding distance calculation method better captures molecular structural differences, enhancing search space exploration, maintaining population diversity, and preventing premature convergence. The dynamic acceptance probability strategy balances exploration and exploitation during evolution. Optimization continues until a predefined stopping condition is met. To assess MoGA-TA's effectiveness, the algorithm is evaluated using metrics like success rate, dominating hypervolume, geometric mean, and internal similarity. Results: Experimental results show that compared to the comparative method, MoGA-TA performs better in drug molecule optimization and significantly improves the efficiency and success rate. Conclusions: The method described in this paper has been proven to be an effective and reliable method for multi-objective molecular optimization tasks.