End-to-end learning for compound activity prediction based on binding pocket information.

ABSTRACT:

Background

Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, docking simulation is generally applied because it only requires a tertiary structure of the target protein. However, the conformation search and the evaluation of binding energy of docking simulation are computationally heavy and thus docking simulation needs huge computational resources. Thus, if we can apply a machine learning-based activity prediction method for a novel target protein, such methods would be highly useful. Recently, Tsubaki et al. proposed an end-to-end learning method to predict the activity of compounds for novel targ

SUBMITTER: Tanebe T 

PROVIDER: S-EPMC8555120 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature