Free Energy Calculation (John Chodera) and AI Assisted Committor - PowerPoint PPT Presentation

Selected talk at the Molecular Kinetics conference held in Berlin: Free Energy Calculation (John Chodera) and AI Assisted Committor Discovery (Gerhard Hummer) Siqin Cao Department of Chemistry The Hong Kong University of Science and Technology HKUST Deutschland ZIB of Freie U

Molecular Kinetics and Free Energy Calculation John Chodera

Drug discovery usually ends in failure R&D model yielding costs to successfully discover and develop a single new molecular entity: p(TS): probability to next stage WIP: work in progress 4.5 years and NME: new molecule entity $219M Unit of money: million USD S. Paul, D. Mytelka, ..., A. Schacht, Nature Reviews Drug Discovery 9 , 203 (2010)

AMBER/GAFF for Relative Free Energy Calculations OPLS2.1 + REST2 : MUE = 0.9 kcal/mol, RMSE = 1.14 kcal/mol AMBER99SB/GAFF +MD for 330 mutations: MUE = 1.15 kcal/mol, RMSE = 1.5 kcal/mol Lin Frank Songa, Tai-Sung Leeb, Chun-Zhua, Darrin M. Yorkb, and Kenneth M. Merz Jr, ChemRxiv 7653434 (2019)

SMIRNOFF: SMIRks Native Open Force Field FF frustrations: FF optimization: Rely on expert intuition; Auomated through Bayesian Poor choice for data structure; approach. Massive reduction in parameters. SMARTS: direct chemical perception Atom types: indirect chemical perception

Machine learning will have a large impact on physical modeling We can learn: • more accurate potential functions • optimal alchemical protocols for each transformation • “difficulty” of transformations • estimates of Δ G and ΔΔ G directly • estimates of conformational reorganization energies

AI-Assisted Discovery of Molecular Mechanisms from Simulations Gerhard Hummer Challenges in MD of biological systems: 1. Sampling problem 2. Interpretation problem Transition Path Sampling Peter G. Bolhuis, David Chandler, Christoph Dellago, and Phillip L. Geissler, Annu. Rev. Phys. Chem. 53 , 291 (2002)

Transition path “shooting from the top” Hendrik Jung , Kei-ichi Okazaki, and Gerhard Hummer, JCP 147 , 152716 (2017)

Machine learning of committor P. G. Bolhuis, D. Chandler, C. Dellago, & P. L. Geissler, Annu. Rev. Phys. Chem. 53 , 291 (2002); H. Jung , K. Okazaki, & G. Hummer, JCP 147 , 152716 (2017); H. Jung, R. Covino, & G. Hummer, arxiv 1901.04595 (2019)

AI assisted discovery of reaction coordinates Committor (p) vs reaction coordinate (q): Transition state ensumble: Committor modeled with self-normalizing ANN Total likelihood: Loss function to minimize: Metropolis-Hastings criterion of path acceptance: Hendrik Jung, Roberto Covino, and Gerhard Hummer, arxiv 1901.04595 (2019)

AI assisted discovery of reaction coordinates Hendrik Jung, Roberto Covino, and Gerhard Hummer, arxiv 1901.04595 (2019)