A physics-based approach to artificial intelligence for understanding of biophysical search spaces

用于理解生物物理搜索空间的基于物理的人工智能方法

• 批准号: RGPIN-2021-03470
• 负责人: Mansbach, Rachael
• 金额: $ 2.84万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762530
• 关键词: physics; based; approach; artificial; intelligence

The drug discovery industry has been lacking innovation in recent years. This is mostly due to the exhaustion of known places to look--traditional search spaces. In addition, traditional chemistry methods show declining efficacy in finding novel search spaces. The overall goal of my interdisciplinary discovery grant research program is to bring together physical principles and data-driven artificial intelligence techniques to perform basic science research into the governing principles of multi-peptide search spaces and ways to traverse them. During my doctoral work in physics, I had the opportunity to employ skills in theory and computation to biophysical systems to understand and design short proteins for therapeutic and bioelectronic applications. During my postdoctoral appointment, I used statistics, artificial intelligence techniques and synthetic biology to develop an algorithm that identifies a set of chemical building blocks conferring on drugs the ability to permeate the outer membranes of Gram-negative bacteria. Going forward, I envision working at the interface of biophysics and artificial intelligence.  I will go beyond my previous work by marrying the interpretability of computational physics-based approaches with the power of deep learning to access and understand new and unusual search spaces. By identifying important physics-based design rules, I will clarify the basic science and theory that underlies the development of novel therapeutic candidates. Deep learning has emerged as an important tool for identifying new drug candidates, but like many artificial intelligence techniques, it is hampered by its lack of interpretability, which makes it difficult to go from the identification to more general design rules. I will bring together the power of statistical physics with the power of deep learning to understand as well as identify new search spaces, which will help combat the serious problem of dwindling productivity of the drug industry in the future by providing a theoretical scaffold for later research. My specific research aims for the next five years are: - Aim 1: Studying the constraints necessary to construct a multi-peptide search space - Aim 2: Multi-scale model and iterative active learning to understand the basic science of antimicrobial peptide methods of action in a healthy state - Aim 3: Development of deep learning models through theoretical characterization of disulfide-rich peptides and their corresponding free energy surfaces This program will bring new techniques to the biophysical community in Canada and will provide new tools for the integration of data science with computational physics for scientists and companies alike. Highly qualified personnel working in my group will receive a thorough grounding in computational biophysics techniques, with a focus on molecular dynamics and Monte Carlo simulations, and in broadly-applicable data science techniques, with a focus on data-driven machine learning.

药物研发行业近年来一直缺乏创新。这主要是由于已知的搜索空间已经耗尽-传统的搜索空间。此外，传统的化学方法在寻找新的搜索空间方面表现出下降的功效。我的跨学科发现资助研究计划的总体目标是将物理原理和数据驱动的人工智能技术结合起来，以进行基础科学研究，研究多肽搜索空间的管理原则和遍历它们的方法。 在我的物理学博士工作期间，我有机会将理论和计算技能应用于生物物理系统，以理解和设计用于治疗和生物电子应用的短蛋白质。在我的博士后任命期间，我使用统计学，人工智能技术和合成生物学开发了一种算法，该算法可以识别一组化学构建模块，这些化学构建模块赋予药物渗透革兰氏阴性细菌外膜的能力。展望未来，我设想在生物物理学和人工智能的界面上工作。我将超越我以前的工作，将基于计算物理学的方法的可解释性与深度学习的力量结合起来，以访问和理解新的和不寻常的搜索空间。通过确定重要的基于物理的设计规则，我将阐明基础科学和理论，基础的新的治疗候选人的发展。深度学习已经成为识别新药候选物的重要工具，但与许多人工智能技术一样，它也受到缺乏可解释性的阻碍，这使得它很难从识别到更通用的设计规则。我将把统计物理学的力量与深度学习的力量结合起来，以理解和识别新的搜索空间，这将有助于通过为以后的研究提供理论框架来解决未来制药行业生产力下降的严重问题。我未来五年的具体研究目标是：-目标1：研究构建多肽搜索空间所必需的约束条件-目标2：多尺度模型和迭代主动学习，以了解健康状态下抗菌肽作用方法的基础科学-目标3：通过二硫化物的理论表征开发深度学习模型-该计划将为加拿大的生物物理界带来新技术，并将为科学家和公司提供数据科学与计算物理相结合的新工具。在我的团队中工作的高素质人员将获得计算生物物理学技术的全面基础，重点是分子动力学和蒙特卡罗模拟，以及广泛适用的数据科学技术，重点是数据驱动的机器学习。