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.

翻译：精确的药物-靶点相互作用预测概率信息对于理解模型局限性和提升预测性能至关重要。高斯过程提供了一个可扩展的框架，能够整合最先进的DTI表示方法与贝叶斯推断，从而实现新颖的操作，例如带拒识的贝叶斯分类、top-$K$选择与排序。我们提出了一种基于深度核学习的高斯过程架构，该架构包含一个用于化学化合物与蛋白质靶点的联合神经嵌入模块以及一个高斯过程模块。工作流程随后从预测分布中进行采样，以估计贝叶斯优先矩阵，该矩阵被用于快速且准确的选择与排序操作。DTI-GP的性能优于现有最先进的解决方案，并且它支持：（1）构建贝叶斯准确率-置信度富集分数，（2）用于提升富集效果的拒识方案，以及（3）针对具有高期望效用的top-$K$选择与排序进行估计与搜索。