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Ce cours exploite les techniques de chimie quantique et de modélisation moléculaire dans le cadre d'un projet de recherche visant a résoudre un problème chimique en binôme.
The goal of this course is to learn to analyze a scientific paper critically, asking whether the data presented support the conclusions that are drawn. The analysis is presented in the form of a summa
This course will discuss the main methods for the simulation of quantum time dependent properties for molecular systems. Basic notions of density functional theory will be covered. An introduction to
The course provides an introduction to the use of path integral methods in atomistic simulations.
The path integral formalism allows to introduce quantum mechanical effects on the equilibrium and (ap
The course provides an introduction to the use of path integral methods in atomistic simulations.
The path integral formalism allows to introduce quantum mechanical effects on the equilibrium and (ap
This is a list of computer programs that are predominantly used for molecular mechanics calculations.
This is a table of notable computer programs implementing molecular mechanics force fields.
In the context of chemistry and molecular modelling, a force field is a computational method that is used to estimate the forces between atoms within molecules and also between molecules. More precisely, the force field refers to the functional form and parameter sets used to calculate the potential energy of a system of atoms or coarse-grained particles in molecular mechanics, molecular dynamics, or Monte Carlo simulations. The parameters for a chosen energy function may be derived from experiments in physics and chemistry, calculations in quantum mechanics, or both.
Explores basis set solutions for the time-dependent Schrödinger equation with a time-dependent Hamiltonian, including special cases like Gaussian wave packets.
Covers advanced topics in organic chemistry, focusing on stereochemistry and reaction mechanisms.
Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.
Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...
The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...