Molecule Generation from Input-Attributions over Graph Convolutional Networks
This work addresses the need for efficient molecule generation in drug discovery, though it appears incremental as it builds on existing GCN and attribution methods.
The paper tackles the problem of costly drug design by developing an automatic process using Graph Convolutional Networks and input-attribution methods to generate new molecules, addressing over-optimization and applicability issues.
It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to determine which modifications are more promising or which aspects of a molecule are more influential for the final activity/property. Here we present an automatic process which involves Graph Convolutional Network models and input-attribution methods to generate new molecules. We also explore the problems of over-optimization and applicability, recognizing them as two important aspects in the practical use of such automatic tools.