用深度核高斯过程提升药物靶点相互作用预测的可靠性与可解释性。
DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes
- 结合神经嵌入与高斯过程，实现端到端概率化建模。
- 在多个数据集上超越现有方法，提升筛选准确率与置信度。
- 支持拒绝预测、前K名筛选和排序，适合药物研发场景。
精确的药物-靶点相互作用（DTI）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（GP）提供了一个可扩展的框架，可融合最先进的DTI表征与贝叶斯推断，支持新型操作，如带拒绝机制的贝叶斯分类、前-K选择和排序。我们提出基于深度核学习的高斯过程架构（DTI-GP），包含化学分子与蛋白靶点的联合神经嵌入模块及高斯过程模块。通过从预测分布采样生成贝叶斯优先级矩阵，实现快速精准的选择与排序。DTI-GP优于现有先进方法，支持：(1) 构建贝叶斯准确率-置信度增益评分；(2) 拒绝策略以提升富集效果；(3) 高期望效用下的前-K选择与排序估计。
原文摘要 · Abstract (English)
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.
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