<br>打破药物靶点亲和力预测中数据稀缺的障碍。,Briefings in Bioinformatics

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打破药物靶点亲和力预测中数据稀缺的障碍。
Briefings in Bioinformatics ( IF 6.8 ) Pub Date : 2023-09-22 , DOI: 10.1093/bib/bbad386
Qizhi Pei ₁ , Lijun Wu ₂ , Jinhua Zhu ₃ , Yingce Xia ₂ , Shufang Xie ₁ , Tao Qin ₄ , Haiguang Liu ₂ , Tie-Yan Liu ₂ , Rui Yan ₅

Affiliation

准确预测药物靶点亲和力（DTA）对于早期药物发现至关重要，有助于识别能够有效与特定靶点相互作用并调节其活性的药物。虽然湿实验仍然是最可靠的方法，但它们非常耗时且资源密集，导致数据可用性有限，这给深度学习方法带来了挑战。现有方法主要侧重于基于可用 DTA 数据开发技术，而没有充分解决数据稀缺问题。为了克服这一挑战，我们提出了用于 DTA 预测的半监督多任务训练（SSM）框架，该框架包含三种简单但高效的策略：（1）一种多任务训练方法，将 DTA 预测与掩码语言建模相结合，使用配对的药物-靶标数据。（2）一种半监督训练方法，利用大规模不配对的分子和蛋白质来增强药物和靶标的表征。这种方法不同于以前仅在预训练中使用分子或蛋白质的方法。（3）集成轻量级交叉注意力模块，改善药物与靶点之间的相互作用，进一步提高预测精度。通过在 BindingDB、DAVIS 和 KIBA 等基准数据集上进行大量实验，我们展示了我们框架的卓越性能。此外，我们还对特定药物靶点结合活性、虚拟筛选实验、药物特征可视化和实际应用进行案例研究，所有这些都展示了我们工作的巨大潜力。总之，我们提出的 SSM-DTA 框架解决了 DTA 预测中的数据限制挑战，并产生了有希望的结果，为更高效、更准确的药物发现过程铺平了道路。

"点击查看英文标题和摘要"

Breaking the barriers of data scarcity in drug-target affinity prediction.

Accurate prediction of drug-target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the Semi-Supervised Multi-task training (SSM) framework for DTA prediction, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes.

更新日期：2023-09-22

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