Department of Chemistry, Cambridge University, Cambridge, UK.
Curr Opin Struct Biol. 2018 Apr;49:123-128. doi: 10.1016/j.sbi.2018.01.006. Epub 2018 Feb 20.
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to design novel ligands for protein targets of interest. This review summarizes the current state of the art in this field, emphasizing the recent development of deep neural networks for predicting protein-ligand binding. We explain the major technical challenges that have caused difficulty with predicting novel ligands, including the problems of sampling noise and the challenge of using benchmark datasets that are sufficiently unbiased that they allow the model to extrapolate to new regimes.
基于数据的计算方法在预测蛋白质-配体结合方面目前在保留的测试数据集上取得了前所未有的准确性。然而，到目前为止，这并没有导致我们设计新型配体以靶向感兴趣的蛋白质的能力有相应的突破。本文综述了该领域的最新进展，重点介绍了用于预测蛋白质-配体结合的深度神经网络的最新发展。我们解释了导致难以预测新型配体的主要技术挑战，包括采样噪声问题和使用基准数据集的挑战，这些数据集足够无偏，使模型能够外推到新的领域。
