Generative Network Complex for the Automated Generation of Drug-like Molecules.

ABSTRACT: Current drug discovery is expensive and time-consuming. It remains a challenging task to create a wide variety of novel compounds that not only have desirable pharmacological properties but also are cheaply available to low-income people. In this work, we develop a generative network complex (GNC) to generate new drug-like molecules based on the multiproperty optimization via the gradient descent in the latent space of an autoencoder. In our GNC, both multiple chemical properties and similarity scores are optimized to generate drug-like molecules with desired chemical properties. To further validate the reliability of the predictions, these molecules are reevaluated and screened by independent 2D fingerprint-based predictors to come up with a few hundreds of new drug candidates. As a demon

SUBMITTER: Gao K 

PROVIDER: S-EPMC8142330 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature