Deep learning for drug design
Miha Škalič · Dialnet (Universidad de la Rioja) · 2019
Designing novel drugs is a complex process which requires finding molecules in a vast chemical space that bind to a specific biomolecular target and have favorable physio-chemical properties. Machine learning methods can leverage previous data and use it for new predictions helping the processes of selection of molecule candidate without relying exclusively on experiments. Particularly, deep learning can be applied to extract complex patterns from simple representations. In this work we leverage deep learning to extract patterns from three-dimensional representations of molecules. We apply classification and regression models to predict bioactivity and binding affinity, respectively. Furthermore, we show that it is possible to predict ligand properties for a particular protein pocket. Finally, we employ deep generative modeling for compound design. Given a ligand shape we show that we can generate similar compounds, and given a protein pocket we can generate potentially binding compounds.