Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of binding affinity between compounds and targets greatly enhances the probability of finding lead compounds by reducing the number of wet-lab experiments. Machine-learning and deep-learning techniques using ligand-based and target-based approaches have been used to predict binding affinities, thereby saving time and cost in drug discovery efforts. In this review, we discuss about machine-learning and deep-learning models used in virtual screening to improve drug-target interaction (DTI) prediction. We also highlight current knowledge and future directions to guide further development in this field.

中文翻译：

---

用于药物-靶标相互作用的机器学习模型：当前知识和未来方向。

在药物发现中，以合理的准确性预测化合物与蛋白质之间的结合亲和力至关重要。化合物与靶标之间结合亲和力的计算预测通过减少湿实验室实验的数量大大提高了发现先导化合物的可能性。使用基于配体和基于靶标的方法的机器学习和深度学习技术已用于预测结合亲和力，从而节省了药物开发工作的时间和成本。在这篇综述中，我们讨论了用于虚拟筛选以改善药物-靶标相互作用（DTI）预测的机器学习和深度学习模型。我们还将重点介绍当前的知识和未来的方向，以指导该领域的进一步发展。