Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea.
School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea.
Int J Mol Sci. 2022 Jul 30;23(15):8453. doi: 10.3390/ijms23158453.
Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug-target affinity is crucial. The proposed model, the prediction of drug-target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug-target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.
药物发现有助于鉴定潜在的新型治疗方法，涉及化学、药理学和生物学等多个科学领域。在药物开发的早期阶段，预测药物-靶标亲和力是至关重要的。所提出的模型，即使用具有自注意力的卷积模型预测药物-靶标亲和力（CSatDTA），应用基于卷积的自注意力机制对分子药物和靶标序列进行预测，与之前的卷积方法不同，这些方法在这方面存在显著的局限性。卷积神经网络（CNN）仅在信息的特定区域上工作，排除了全面的细节。另一方面，自注意力是一种用于捕获长程交互的相对较新的技术，主要用于序列建模任务。比较实验的结果表明，CSatDTA 优于以前的基于序列的或其他方法，具有出色的保留能力。
