This page contains benchmarks of graph generative models for goal-directed property optimization, which is aimed at generating novel molecules with optimized chemical properties. We first pretrain the models on ZINC250k dataset, and then apply the reinforcement learning algorithms to finetune the networks towards desired chemical properties.
We choose penalized logP and QED score as our target property.
Penalized logP score is the octanol-water partition coefficient penalized by the synthetic accessibility score and the number of long cycles.
QED score measures the drug-likeness of the molecule.
We report the top-1 property scores of generated molecules by different models in the following table. We also report the top-1 property scores of molecules in ZINC250k dataset for reference. The maximum graph size is set as 38, which is the same as the maximum graph size of molecules in ZINC250k.