Sun Jinghong, Wang Han, Mi Jia, Wan Jing, Gao Jingyang
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
BMC Bioinformatics. 2024 Dec 5;25(1):375. doi: 10.1186/s12859-024-05984-3.
The development of drug-target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to machine learning-based prediction. This transition enables a more expedient exploration of potential interactions between drugs and targets, leading to substantial savings in time and funding resources. However, existing methods still face several challenges, such as drug information loss, lack of calculation of the contribution of each modality, and lack of simulation regarding the drug-target binding mechanisms.
We propose MTAF-DTA, a method for drug-target binding affinity prediction to solve the above problems. The drug representation module extracts three modalities of features from drugs and uses an attention mechanism to update their respective contribution weights. Additionally, we design a Spiral-Attention Block (SAB) as drug-target feature fusion module based on multi-type attention mechanisms, facilitating a triple fusion process between them. The SAB, to some extent, simulates the interactions between drugs and targets, thereby enabling outstanding performance in the DTA task. Our regression task on the Davis and KIBA datasets demonstrates the predictive capability of MTAF-DTA, with CI and MSE metrics showing respective improvements of 1.1% and 9.2% over the state-of-the-art (SOTA) method in the novel target settings. Furthermore, downstream tasks further validate MTAF-DTA's superiority in DTA prediction.
Experimental results and case study demonstrate the superior performance of our approach in DTA prediction tasks, showing its potential in practical applications such as drug discovery and disease treatment.
药物-靶点结合亲和力（DTA）预测任务的发展极大地推动了药物发现进程。借助人工智能的快速发展，DTA预测任务已从湿实验室实验经历了变革性转变，发展为基于机器学习的预测。这种转变使得能够更便捷地探索药物与靶点之间的潜在相互作用，从而大幅节省时间和资金资源。然而，现有方法仍面临若干挑战，例如药物信息丢失、缺乏对每种模态贡献的计算以及缺乏对药物-靶点结合机制的模拟。
我们提出了MTAF-DTA，一种用于药物-靶点结合亲和力预测的方法，以解决上述问题。药物表示模块从药物中提取三种特征模态，并使用注意力机制更新它们各自的贡献权重。此外，我们基于多类型注意力机制设计了一个螺旋注意力模块（SAB）作为药物-靶点特征融合模块，促进它们之间的三重融合过程。SAB在一定程度上模拟了药物与靶点之间的相互作用，从而在DTA任务中实现了出色的性能。我们在Davis和KIBA数据集上的回归任务展示了MTAF-DTA的预测能力，在新靶点设置下，CI和MSE指标相对于最先进（SOTA）方法分别提高了1.1%和9.2%。此外，下游任务进一步验证了MTAF-DTA在DTA预测方面的优越性。
实验结果和案例研究证明了我们的方法在DTA预测任务中的卓越性能，显示了其在药物发现和疾病治疗等实际应用中的潜力。
