2025/03/03 by Yifan Niu, Niu, Yifan, Ziqi Gao +13 · 1 citation
Computer Science · Engineering · Materials Science · #Artificial neural network #Chemical process #Chemical space #Computational Drug Discovery Methods #Dual (grammatical number) #Innovative Microfluidic and Catalytic Techniques Innovation #Inversion (geology) #Machine Learning in Materials Science #Minification #Pareto optimal #Path (computing)
paper · pdf · doi:10.48550/arxiv.2503.01488
openalex publication_date 2025/03/03 · openalex created_date 2025/10/12 · openalex updated_date 2026/08/05
Exploring chemical space to find novel molecules that simultaneously satisfy multiple properties is crucial in drug discovery. However, existing methods often struggle with trading off multiple properties due to the conflicting or correlated nature of chemical properties. To tackle this issue, we introduce InversionGNN framework, an effective yet sample-efficient dual-path graph neural network (GNN) for multi-objective drug discovery. In the direct prediction path of InversionGNN, we train the model for multi-property prediction to acquire knowledge of the optimal combination of functional groups. Then the learned chemical knowledge helps the inversion generation path to generate molecules with required properties. In order to decode the complex knowledge of multiple properties in the inversion path, we propose a gradient-based Pareto search method to balance conflicting properties and generate Pareto optimal molecules. Additionally, InversionGNN is able to search the full Pareto front approximately in discrete chemical space. Comprehensive experimental evaluations show that InversionGNN is both effective and sample-efficient in various discrete multi-objective settings including drug discovery.