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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741781
• 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.

毒品的创新行业创新行业在某个时候创新了创新行业。驱动的人工智能技术将基本的斯科奇构成多肽搜索Spachs的管理，我有机会在理论和计算中使用技能来理解和设计矮个子的疗程我使用统计，人工技术来开发统计，确定了一组化学块，授予革兰氏阴性细菌的外膜。通过学习基于物理的搜索空间的能力，将基于计算的方法与基于物理的搜索空间结合在一起。技术，它由于缺乏解释性而阻碍了我太了解的统计物理学的力量，并确定了新的搜索空间，这将通过提供理论上的脚手架研究目的是下一个生命年：-AIM 1：研究建筑以构建多肽海面太空2：多规模模型和迭代学习，以了解健康状态下抗菌肽作用的BA SIC科学方法-aim 3：通过理论表征二硫化物肽及其相应的自由能表面，该计划将带给加拿大的生物物理Munity，并将为加拿大的生物物理Munity带来新的工具，以使用科学家和计算机物理学工具为科学家和科学家和在我小组中工作的合格人员将获得计算生物物理技术的基础，n宽广泛的数据科学技术，重点是数据驱动的机器学习。