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+Molecule Generation
+===================
+
+.. include:: ../bibliography.rst
+
+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`_.
+
++----------------+-----------------------+---------+------------+
+|                | `ZINC250k`_ (Dataset) | `GCPN`_ | `GraphAF`_ |
++================+=======================+=========+============+
+| Penalized LogP | 4.52                  | 6.560   | 5.630      |
++----------------+-----------------------+---------+------------+
+| QED            | 0.948                 | 0.948   | 0.948      |
++----------------+-----------------------+---------+------------+