MolDeriveX: Multi-Objective Optimization via Adaptive Structural Inheritance

Multi-objective molecular optimization requires translating competing property objectives into effective structural edits. A key challenge is to identify and reuse substructures that support different properties within the current molecule, while updating this structural guidance as optimization proceeds. We introduce MolDeriveX, a graph optimization framework based on adaptive structural inheritance. It identifies property-specific anchors by combining isolated-fragment scores with changes in parent-level property scores after fragment deletion. These anchors guide Pareto-based graph optimization through soft retention penalties, favoring the retention of property-supporting regions while keeping them editable. Selected high-quality descendants become new structural references, with anchors recomputed to guide subsequent search. Across five multi-objective evaluated tasks, MolDeriveX achieves the highest hypervolume on four and the highest Top-100 joint score on all four target activity tasks. In a challenging, real-world-motivated antibiotic derivatization case study involving three single-parent optimization tasks, MolDeriveX achieves the highest hypervolume for predicted antibacterial activity and parent-molecule similarity among the evaluated methods.