Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets.

ABSTRACT: Drug discovery is a process that finds new potential drug candidates for curing diseases and is also vital to improving the wellness of people. Enhancing deep learning approaches, e.g., molecular generation models, increases the drug discovery process's efficiency. However, there is a problem in this field in creating drug candidates with desired properties such as the quantitative estimate of druglikeness (QED), synthetic accessibility (SA), and binding affinity (BA), and there is a challenge for training a generative model for specific protein targets that has less pharmaceutical data. In this research, we present Mol-Zero-GAN, a framework that aims to solve the problem based on Bayesian optimization (BO) to find the model optimal weights' singular values, factorized by singular v

SUBMITTER: Aphikulvanich R 

PROVIDER: S-EPMC10714197 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature