Annotation
The goal of this course is to teach atomistic-level computational methods useful for pharmaceutical science and drug development. Students will learn the principles and practical applications of atomistic simulations. The course teaches how to predict experimental properties and critically interpret the results of atomistic simulations.
Topics include:
Molecular representations: molecular graph, conformations, SMILES.
Quantum mechanics: Schrödinger equation, Hartree-Fock method, ground state, potential energy surface, ab initio forces, geometry optimization.
Classical molecular dynamics: equation of motion, force field, Verlet algorithm, system preparation, periodic boundary conditions, solvation, thermostat, barostat, equilibration.
Molecular dynamics analysis: root-mean-square deviation (RMSD), hydrogen bond analysis, data dimension reduction, principal component analysis (PCA), kinetic models, Markov model.
Atomistic machine learning: molecular property prediction, protein structure prediction, de novo protein design.
Structural modeling: molecular docking, binding site detection, binding affinity prediction.