Deep Learning in Science and Engineering
- Texas A&M University
- College Station, TX
- Joe C. Richardson Petroleum Engineering Building (RICH) 910
- Yang Shen, Department of Electrical & Computer Engineering
- Interpretable Deep Learning for Drug Discovery
Rapid quantification of compound-protein interactions (CPI) is an important but daunting task for drug discovery, especially considering the enormous chemical and proteomic spaces. Clearly, there is a critical need of computational methods for CPI prediction. However, there is a lack of such methods with wide applicability, high accuracy, and mechanistic interpretability. In this talk I will present our recent work of integrating knowledge- and learning-based approaches to address the challenge. Specifically, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting CPI. Furthermore, attention mechanisms are embedded to our model for its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions.