Deep Machine Learning for Computer-Aided Drug Design

Abstract

In recent years, deep learning (DL) has led to new scientific developments with immediate implications for computer-aided drug design (CADD). These include advances in both small molecular and macromolecular modeling, as highlighted herein. Going forward, these developments also challenge CADD in different ways and require further progress to fully realize their potential for drug discovery. For CADD, these are exciting times and at the very least, the dynamics of the discipline will further increase.