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

从原子到机制 - 人工智能增强机械配体设计的分子模拟

基本信息

批准号：
10490317
负责人：
Pratyush Tiwary
金额：
$ 37.38万
依托单位：
UNIV OF MARYLAND, COLLEGE PARK
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-18 至 2026-08-31
项目状态：
未结题

项目摘要

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

尽管在有助于合理药物设计的计算方法方面取得了许多进展，但大多数迄今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因此，迫切需要新的构象转变动力学和热力学。准确、易于处理且可自动化的计算方法，适用于大规模药物发现和化学生物学研究解释了仿制药靶点性质的变化。理论和计算的初步结果，该计划旨在理解和指导设计新型酪氨酸激酶抑制剂和机械引导范例中的核糖开关模型。我们的研究计划由以下中心假设驱动：(a) 机械感知配体设计策略可以优于仅由结构指导的传统策略，并且（b）人工智能（AI）- 集成分子动力学（MD）模拟方法可以帮助学习高通量的机制我们的计划可以分为两个总体但互补的主题领域：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测机制，例如药物解离和构象变化。具体来说，我们将在利用神经信息处理和自然语言处理，并使它们适应学习反应坐标的高级采样方法，随着模拟的进行，我们还将进行动态热力学和动力学的交互。与其他领先的计算小组密切合作，将我们的抽样方法与他们的抽样方法相结合，并在第二个领域，我们将促进极化力场开发的高效、准确采样。使用我们的算法指导 Src、Abl 激酶和 PreQ1 抑制剂的机械驱动设计我们将采取机械驱动的视角，因为我们将绘制出不同的图。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，我们将使用我们的人工智能增强 MD 方法来进行配体修饰。在不间断的“原子到机制”工作流程中了解配体的解离机制我们的所有预测都将以不同的方式得到验证。石溪大学和国家癌症研究所的实验合作者