Title: Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks,
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.