Modeling chemistry with active learning, and what active learning teaches us about the reality of machine learning

Abstract

There are hopes that virtual screening can reduced pharmaceutical development costs by predicting pharmacological activity. While deep learning has become a central method for many, chemogenomic active learning (CGAL) has demonstrated the ability to obtain the same prediction performance by efficient use of less data. Even when data is sparse or receptors are not in chemogenomic data, CGAL can be successful. In this light, how should we think of machine learning in drug discovery. The CGAL method will be introduced and the truth about machine learning will be argued.