Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks

TL;DR

This paper introduces an unsupervised algorithmic approach that automatically generates novel drug molecules from prototypes, significantly expanding the chemical space explored in drug discovery and identifying potential FDA-approved drugs.

Contribution

The work presents a new conditional diversity network that efficiently generates valid and diverse drug molecules from prototypes without supervision.

Findings

Generated molecules are valid and diverse.

Identified 35 FDA-approved drugs among generated compounds.

System successfully generated Isoniazid for Tuberculosis.

Abstract

Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required changes to the prototype. In this work, we develop an algorithmic unsupervised-approach that automatically generates potential drug molecules given a prototype drug. We show that the molecules generated by the system are valid molecules and significantly different from the prototype drug. Out of the compounds generated by the system, we identified 35 FDA-approved drugs. As an example, our system generated Isoniazid - one of the main drugs for Tuberculosis. The system is currently being deployed for use in collaboration with pharmaceutical companies to further analyze the additional generated molecules.