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.

近年来，药物发现行业一直缺乏创新。这主要是由于已知的位置耗尽 - 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有效性下降。我的跨学科发现赠款研究计划的总体目标是汇集物理原理和数据驱动的人工智能技术，以对多肽搜索空间的管理原理进行基础科学研究，并将其穿越它们。在物理学博士学位期间，我有机会在生物物理系统中采用理论和计算技能，以了解和设计用于治疗和生物电子应用的短蛋白。在我的博士后任命期间，我使用统计，人工智能技术和合成生物学来开发一种算法，该算法识别了一组化学基础块会议，会议上有关药物的能力，可以渗透到革兰氏阴性细菌的外部机制。展望未来，我设想在生物物理学和人工智能的界面上工作。我将通过将基于计算物理学的方法与深度学习的能力结合到访问和理解新的和不寻常的搜索空间的能力，将超越我以前的工作。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是新型治疗候选者发展的基础。深度学习已成为识别新药候选人的重要工具，但是像许多人工智能技术一样，它缺乏可解释性的阻碍，这使得很难从识别到更一般的设计规则。我将汇集统计物理学的力量，并具有深入学习的能力以及确定新的搜索空间，这将有助于解决未来药物行业生产率下降的严重问题，通过为以后的研究提供理论上的脚手架。我未来五年的具体研究目的是： - 目标1：研究构建多肽搜索空间所需的约束 - 目标2：多尺度模型和迭代积极学习，以了解健康状态中抗菌肽作用方法的基本科学的基础科学，目标3：AIM 3：通过对启用宠物及其启用的理论来开发深度学习模型，以使其对相应的宠物及其相应的特征来开发。加拿大的生物物理社区将为科学家和公司的计算物理学整合到将数据科学与计算物理的整合提供新的工具。在我的小组中工作的高素质人员将获得计算生物物理技术的彻底基础，重点是分子动力学和蒙特卡洛模拟，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。