DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes
This work addresses the need for precise probabilistic predictions in drug discovery, offering incremental improvements in accuracy and utility for researchers in computational biology.
The paper tackled the problem of predicting drug-target interactions with probabilistic uncertainty, proposing DTI-GP, a deep kernel Gaussian process method that outperformed state-of-the-art solutions and enabled operations like Bayesian classification with rejection and top-K selection.
Precise probabilistic information about drug-target interaction (DTI) predictions is vital for understanding limitations and boosting predictive performance. Gaussian processes (GP) offer a scalable framework to integrate state-of-the-art DTI representations and Bayesian inference, enabling novel operations, such as Bayesian classification with rejection, top-$K$ selection, and ranking. We propose a deep kernel learning-based GP architecture (DTI-GP), which incorporates a combined neural embedding module for chemical compounds and protein targets, and a GP module. The workflow continues with sampling from the predictive distribution to estimate a Bayesian precedence matrix, which is used in fast and accurate selection and ranking operations. DTI-GP outperforms state-of-the-art solutions, and it allows (1) the construction of a Bayesian accuracy-confidence enrichment score, (2) rejection schemes for improved enrichment, and (3) estimation and search for top-$K$ selections and ranking with high expected utility.