From atoms to mechanisms - Artificial Intelligence augmented molecular simulations for mechanistic ligand design

虽然在帮助合理药物设计的计算方法方面有许多进步，但大多数 到目前为止的方法对药物靶点采取静态观点，忽略了与药物靶点相关的复杂性。 构象转变的动力学和热力学。因此，迫切需要新的 计算方法是准确的，易处理的和自动化的大规模药物发现， 化学生物学研究，解释了仿制药靶标的变化性质。建立在强大的 理论和计算的初步结果，该计划旨在了解和指导设计， 新的酪氨酸激酶抑制剂和核糖开关模型的机械引导范例。 我们的研究计划是由中心假设驱动的，（a）机械意识配体设计 策略可以优于仅由结构指导的传统策略，以及（B）人工智能（AI）- 集成的分子动力学（MD）模拟方法可以帮助学习机制，在高通量 时尚.我们的计划可以分为两个总体但互补的主题领域。在第一个领域， 我们将在统计力学、MD模拟和人工智能的接口上开发采样算法， 罕见事件过程的探针机制，如药物解结合和构象变化。 具体来说，我们将建立在我们的初步工作中使用的想法，从神经信息处理， 自然语言处理，并使其适应学习反应坐标的高级采样方法， 热力学和动力学的动态模拟进展。此外，我们还将与 与其他领先的计算小组密切合作，将我们的采样方法与他们的方法结合起来， 为极化力场的开发提供高效、准确的采样。在第二个领域，我们将 使用我们的算法来指导Src，Abl激酶和PreQ 1抑制剂的机械驱动设计 核糖开关我们将采取一种机械驱动的观点，其中我们将绘制出不同的 给定目标的构象，并了解现有配体如何与这些相互作用，然后提出 基于这种理解的配体修饰。我们将使用我们的AI增强MD方法来 在一个不间断的“原子到机制”工作流程中了解配体的解离机制 以最少的人为干预。我们所有的预测都将以不同的方式得到验证， 斯托尼布鲁克大学和国家癌症研究所的实验合作者

While there have been numerous advances in computational methods aiding in rational drug design, most approaches so far take a static view of the drug target, ignoring the complexities associated with the dynamics and thermodynamics of conformational transitions. There is thus a pressing need for new computational methods that are accurate, tractable and automatable for large scale drug discovery and chemical biology studies that account for the changing nature of a generic drug target. Built on strong theoretical and computational preliminary results, this program seeks to understand and guide design of new inhibitors of tyrosine kinases and model riboswitches in a mechanistically guided paradigm. Our research program is driven by the central hypotheses that (a) mechanistically aware ligand design strategies can outperform traditional strategies guided only by structure, and (b) artificial intelligence (AI)- integrated molecular dynamics (MD) simulation methods can help learn mechanisms in a high-throughput fashion. Our program can be split into two overarching yet complementary thematic areas. In the first area, we will develop sampling algorithms at the interface of statistical mechanics, MD simulations and AI to probe mechanisms for rare event processes, such as drug unbinding and conformational change. Specifically we will build on our preliminary work in using ideas from neural information processing and natural language processing, and adapt them for advanced sampling methods that learn reaction coordinate, thermodynamics and kinetics on-the-fly as the simulation progresses. In addition, we will also be interacting closely with other leading computational groups to integrate our sampling methods with theirs and to facilitate efficient, accurate sampling for polarizable force-field development. In the second area, we will use our algorithms to guide mechanistically driven design of inhibitors of Src, Abl kinases and PreQ1 riboswitches. We will take a mechanistically driven perspective wherein we will map out the different conformations of a given target and understand how an existing ligand interacts with these, and then propose ligand modifications based upon this understanding. We will use our AI-augmented MD methods to understand the dissociation mechanisms of ligands in one uninterrupted “atoms to mechanism” workflow with minimal human intervention. All our predictions will be validated in different ways by our experimental collaborators at Stony Brook University and the National Cancer Institute