Machine Learning with Molecular Dynamics to improve rapid protein-ligand predictions.

在基于系综的、热力学严格的方法计算小分子与蛋白质结合的自由能方面取得了令人兴奋的进展，事实上，我们和其他人最近的工作已经证明，这些方法能够获得与实验相当的精度。然而，这些方法需要大量的计算机时间，虽然这在某些情况下可能是可以接受的，但它禁止在需要实时数据的情况下使用这些方法(例如结构改进或虚拟筛选)。因此，开发既快速又能达到所需精度的方法是可取的。深度学习和相关的机器学习技术在这一领域显示出巨大的前景，特别是在拥有大量数据集的情况下。分子动力学(MD)模拟可以提供大量关于蛋白质-配体相互作用的相关数据，但到目前为止，这两个学科还没有真正结合起来。我们的首要问题是：机器学习能否与MD相结合来提高蛋白质-配体的快速预测？机器学习方法的关键优势之一以及它们的速度，是它们解释非线性关系的能力，这在蛋白质和配体之间相互作用的背景下特别有用。我们在这里提出的工作将在机器学习的背景下使用MD数据(首先是神经网络，然后是深度神经网络)来提高亲和力，并提出小分子与蛋白质结合的预测。这是一个改善合理药物设计前景的令人兴奋的机会。

Exciting progress has been in made in ensemble-based, thermodynamically rigorous approaches to calculate the free energy of binding of small molecules to proteins and indeed recent work by us and others has demonstrated that these methods are capable of obtaining accuracy comparable to experiment. However, these approaches require large amounts of computer time and whilst that may be acceptable in some scenarios it prohibits the use of these approaches in scenarios where real time data is necessary (such as structural refinement or virtual screening). Thus, it would be desirable to develop approaches that are rapid, yet can deliver at the required level of accuracy. Deep learning and related machine learning technologies show great promise in this area, particularly where large data sets are available. Molecular dynamic (MD) simulations can provide huge amount of relevant data about protein-ligand interactions, but thus far these two disciplines have not really been combined. Our overarching question is: "Can machine-learning be combined with MD to improve rapid protein-ligand predictions?"One of the key advantages of machine learning methodologies, as well as their speed, is their capacity to explain non-linear relationships, which is especially useful in the context of interactions between a protein and a ligand. The work we are proposing here will use MD data within a machine-learning context (neural networks in the first instance, and then deep neural networks) to improve affinity and pose predictions of small molecule binding to proteins. This is an exciting opportunity to improve the prospects for rational drug design.