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：
10683387
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

尽管在协助合理药物设计的计算方法方面取得了许多进步，但大多数到目前为止，方法对药物目标有静态的视野，忽略了与构象转变的动力学和热力学。因此，有迫切需要新的用于大规模药物发现的准确，可进行且可自动的计算方法化学生物学研究解释了通用药物靶标的不断变化的性质。建立在强的基础上理论和计算初步结果，该计划旨在理解和指导设计在机械引导的范式中的酪氨酸激酶和核糖开关的新抑制剂。我们的研究计划是由（a）机械意识配体设计的中心假设驱动的策略的表现只能以结构为指导的传统策略，（b）人工智能（AI） - 综合分子动力学（MD）模拟方法可以帮助学习高通量的机制时尚。我们的程序可以分为两个总体但完整的主题领域。在第一个区域，我们将在统计力学，MD模拟和AI的接口上开发采样算法稀有事件过程的探针机制，例如药物解开和构象变化。具体而言，我们将在使用神经信息处理中的想法和自然语言处理，并将其调整为学习反应坐标的高级抽样方法，随着仿真的进行，热力学和动力学会随时进行。此外，我们还将互动与其他领先的计算组紧密联系，以将我们的采样方法与他们的采样方法集成促进有效，准确的采样，以进行极化的力场发展。在第二区，我们将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核糖开关。我们将采取机械驱动的视角，其中我们将绘制不同给定目标的构象并了解现有配体如何与这些相互作用，然后提出建议基于此理解的配体修饰。我们将使用AI-Aigment MD方法来了解一个不间断的“原子与机制”工作流程中配体的分离机制通过最少的人类干预。我们所有的预测将由我们的 Stony Brook大学和国家癌症研究所的实验合作者