CHM 825 LECTURE NOTE
Course Information
Course Title: Introduction to Computational Chemistry
Course Code: CHM 825
Course Synopsis
Overview of Computational Chemistry
Application of chemical, mathematical, and computing skills to resolve chemical problems using computers.
Capabilities include:
Calculating molecular geometry (bond lengths, angles, dihedral angles).
Estimating energy of molecules at equilibrium and transition states.
Evaluating reaction rates and properties (IR, UV-Vis, NMR, etc).
Components of Computational Chemistry
Molecular mechanics.
Ab initio and semi-empirical quantum mechanics.
Density functional methods.
Molecular dynamics simulations.
Predictions prior to experiments to prepare better observations.
Chemo-informatics and Computational Techniques
Molecular Modelling
Ab initio Molecular Dynamics Techniques
Density Functional Theory (DFT)
Quantitative Structure-Activity Relationship (QSAR)
Drug Design
Chemometrics
Numerical Methods and Mathematical Modelling
Modelling Software
Computer Programming Languages
C, C++, Java
References
A.R. Leach, "Molecular Modelling Principles and Applications", Addison Wesley Longman, 1996.
G.H. Grant, W.G. Richards, "Computational Chemistry", Oxford, 1995.
F. Jensen, "Introduction to Computational Chemistry", John Wiley & Sons, 1999.
J.H. Jensen, "Molecular Modelling Basics", CRC Press, 2009.
S.M. Bachrach, "Computational Organic Chemistry", Wiley, 2007.
C.J. Cramer, "Essentials of Computational Chemistry Theories and Models (2nd ed.)", Wiley, 2004.
Key Questions Addressed by Computational Chemistry
Mechanistic Questions
What intermediates and transition states occur?
What factors influence selectivity?
Do molecules follow the minimum energy path from reactants to products?
Physical Questions
What is the equilibrium geometry of a molecule?
How do molecular spectra (IR, UV-vis, NMR) appear and what do they signify?
Conceptual Questions
Where are the charges in a molecule?
What do the molecular orbitals look like?
What stabilizes certain molecules over others?
What interactions are important (e.g. hyperconjugative)?
Definitions of Chemoinformatics
Chemoinformatics
A field integrating design, creation, organization, management, retrieval, analysis, dissemination, visualization, and use of chemical information.
Originally defined as transforming data into information and then into knowledge for better decision-making in drug lead identification and optimization.
Employs computer science to solve chemical problems.
Involves molecular objects in multidimensional chemical space.
Evolution of Chemoinformatics
Originally focused on chemical structure representation and data manipulation.
Has shifted towards exploring extensive chemical databases and discovering new compounds due to advancements in biological data production (e.g., High-Throughput Screening).
The Role of Big Data and Computational Tools
Relevance to Chemoinformatics
The combination of biological and chemical data necessitates computational tools for data retrieval and analysis.
Integrates with computational chemistry, molecular modelling, and drug design.
Computer-Aided Drug Design (CADD)
Overview
CADD merges applied and theoretical fields to accelerate drug discovery and development processes.
Techniques employed include:
Virtual Screening
Pharmacophore Modelling
Molecular Docking
Structure-Activity Relationship Modelling
Machine Learning approaches.
Computational Chemistry
Broad Areas
Molecular Mechanics/Molecular Dynamics: Based on classical mechanics principles.
Electronic Structure Methods: Based on quantum mechanics principles.
Categories:
Ab initio methods
Semi-empirical methods
Ab Initio Methods
Define calculations derived from the theoretical principles based purely on mathematical approximations.
Solve the Schrödinger equation for molecular systems, focusing on eigenvalue problems for many-electron molecules.
Schrödinger Equation Complexity
Solutions become complex for systems with multiple electrons due to electron-electron repulsion, necessitating approximations.
Born-Oppenheimer Approximation
Concept
Neglects nuclear kinetic energy in favor of electron dynamics due to mass differences between electrons and nuclei.
Allows for effective separation of electronic and nuclear motion, simplifying calculations.
Quantum Mechanics Requirements
Wavefunction Representation: Total wavefunction is dependent on electronic and nuclear coordinates.
Hartree-Fock Approximation
Overview
Central to ab initio methods, calculating the average effect of electron-electron repulsion.
Variational nature means calculated energies are greater or equal to exact energies.
Utilizes linear combinations of Gaussian-type orbitals to represent wavefunctions.
Functions are defined with respect to spatial and angular variables yielding different orbital symmetries.
Determinant and Anti-Symmetry Requirements
Electrons must be indistinguishable; satisfies quantum mechanics through antisymmetry in wavefunction.
Hartree-Fock Calculation Process
Begins with initial guesses for orbital coefficients, followed by iterative adjustments until convergence is achieved in energy and coefficients.
Method Variants
Restricted Hartree-Fock (RHF): Used for singlet spin configurations.
Unrestricted Hartree-Fock (UHF): Applicable for systems with unpaired electrons. Introduces potential spin contamination errors.