Molecule Generation

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`_.