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.

Molecule Generation from Input-Attribution over Graph Convolutional Networks / Savoia, Dylan; Ragno, Alessio; Capobianco, Roberto. - (2021). ((Intervento presentato al convegno ELLIS Machine Learning for Molecules Workshop tenutosi a Online.

Molecule Generation from Input-Attribution over Graph Convolutional Networks

Dylan Savoia;Alessio Ragno
;
Roberto Capobianco
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1645783
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