Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development.

ABSTRACT: Predicting compound-protein interactions (CPIs) is of great importance for drug discovery and repositioning, yet still challenging mainly due to the sparse nature of CPI matrixes, resulting in poor generalization performance. Hence, unlike typical CPI prediction models focused on representation learning or model selection, we propose a deep neural network-based strategy, PCM-AAE, that re-explores and augments the pharmacological space of kinase inhibitors by introducing the adversarial auto-encoder model (AAE) to improve the generalization of the prediction model. To complete the data space, we constructed Ensemble of PCM-AAE (EPA), an ensemble model that quickly and accurately yields quantitative predictions of binding affinity between any human kinase and inhibitor. In rigorous internal

SUBMITTER: Bai X 

PROVIDER: S-EPMC8650415 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature