Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design.

ABSTRACT: Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic molecules are being discovered in numerous proteins linked to various diseases. These valuable data offer new opportunities to build efficient computational models predicting binding molecules for target sites through the application of data mining and machine learning. In particular, deep neural networks are powerful techniques capable of learning from complex data in order to make informed drug binding predictions. In this communication, we describe Pocket2Drug, a deep graph neural network model t

SUBMITTER: Shi W 

PROVIDER: S-EPMC8962739 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature