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

点击翻译按钮获取中文摘要

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.