Year: 2026
Type: preprint
Abstract: Multi objective optimization (MOO) is central to molecular design, yet its impact on generative chemistry outcomes remains poorly quantified. Consequently, it is unclear how different MOO strategies influence chemical space exploration and property optimization in generative methods. Here, we introduce a benchmarking framework for evaluating MOO strategies in generative chemistry. Six MOO approaches are highlighted including arithmetic and geometric means, non dominated sorting, Chebyshev scalarization, generational distance, and binary thresholding within a lead optimization setting. Across 60 scaffolds derived from six potency datasets, we explore a many-objective task for the simultaneous optimization of various compound properties, including potency, solubility and lipophilicity. We evaluate methods using metrics aligned to recurring medicinal chemistry use cases or decision scenarios and assess differences across datasets and scaffolds. We find that the MOO choice substantially changes both optimization outcomes and explored chemical space across datasets, scaffold and intended use cases. Overall, our results provide practical, use case driven guidance for selecting and comparing MOO strategies in generative chemistry workflows.
Source: ChemRxiv
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204775202