REINVENT

This work introduces a method to tune a sequence-based generative model for
molecular de novo design that through augmented episodic likelihood can learn to
generate structures with certain specified desirable properties. We demonstrate
how this model can execute a range of tasks such as generating analogues to a
query structure and generating compounds predicted to be active against a
biological target. As a proof of principle, the model is first trained to
generate molecules that do not contain sulphur. As a second example, the model is
trained to generate analogues to the drug Celecoxib, a technique that could be
used for scaffold hopping or library expansion starting from a single molecule.
Finally, when tuning the model towards generating compounds predicted to be
active against the dopamine receptor type 2, the model generates structures of
which more than 95% are predicted to be active, including experimentally
confirmed actives that have not been included in either the generative model nor
the activity prediction model. Graphical abstract .

Molecular de-novo design through deep reinforcement learning.

REINVENT is a software tool for molecular de novo drug design using <a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" target="_blank" rel="noopener">recurrent neural networks</a> (RNNs) and <a href="https://en.wikipedia.org/wiki/Reinforcement_learning" target="_blank" rel="noopener">reinforcement learning</a>. The REINVENT implementation uses the <a href="https://pytorch.org/" target="_blank" rel="noopener">PyTorch</a> framework.

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