WITHDRAWN: Exploration and Augmentation of Pharmacological Space via Adversarial Auto-encoder Model for Facilitating Kinase-centric Drug Development

Abstract

Predicting drug-protein interactions (DPIs) is of great importance for drug discovery and repositioning, yet still challenging mainly due to the sparse nature of DPI matrixes, resulting in poor generalization performance. Hence, unlike typical DPI prediction models which 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 adversarial auto-encoder model (AAE) to improve the generalization of the prediction model. To complete the pharmacological 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 validation, EPA showed excellent performance, consistently outperforming the model trained with the imbalanced set, especially for targets with relatively fewer training data points. Improved prediction accuracy of EPA to external datasets again demonstrated enhanced generalization ability of EPA that could gracefully handle previously unseen kinases or inhibitors. Further analysis showed promising potential when EPA was directly applied to virtual screening and off-target prediction, exhibiting the practicality of the EPA model in hit prediction. Our strategy is expected to facilitate kinase-centric drug development, as well as to solve more challenging prediction problems with insufficient data points.