InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization
This addresses the challenge of multi-property molecular optimization in drug discovery, which is an incremental improvement over existing methods.
The paper tackled the problem of finding novel molecules that satisfy multiple conflicting properties in drug discovery by introducing the InversionGNN framework, a dual-path graph neural network that uses a gradient-based Pareto search method to generate Pareto optimal molecules, achieving effectiveness and sample efficiency in various discrete multi-objective settings.
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.