MTech Chemoinformatics And Computational Medicinal Chemistry syllabus for 2 Sem 2020 scheme 20BBI241

Introduction to Chemoinformatics:

Fundamental concepts - molecular descriptors and chemical spaces, chemical spaces and molecular similarity, modification and simplification of chemical spaces. Compound classification and selection – cluster analysis, partitioning, support vectors machines. Predicting reactivity of biologically important molecules, combining screening and structure - 'SAR by NMR', computer storage of chemical information, data formats, OLE, XML, web design and delivery. Representing intermolecular forces: ab initio potentials, statistical potentials, force fields, molecular mechanics.

Chemoinformatics Databases:

Compound availability databases, SAR databases, chemical reaction databases,patent databases and other compound and drug discover databases. Database search methods: Chemical indexing, Proximity searching, 2D and 3D Structure and Substructure searching. Similarity Searching: Structural queries and Graphs, Pharmacophores, Fingerprints. Topological analysis. Machine learning methods for similarity search – Generic and Neural networks. Library design – Diverse libraries, Diversity estimation, Multiobjective designand Focused libraries.

Computational Models:

Introduction, Historical Overview, Deriving a QSAR Equation. Simple and Multiple Linear Regression. Designing a QSAR "Experiment". Principal Components Regression, Partial Least Squares. Molecular Field Analysis and Partial Least Squares. Quantitative Structure-Activity Relationship Analysis: Model building, Model evaluation, 3DQSAR, 4DQSAR. Methods of QSAR analysis - Monte Carlo methods, Simulated annealing, Molecular dynamics and Probabilistic methods. Virtual screening and Compound filtering.

Virtual Screening:

Introduction. "Drug-Likeness" and Compound filters. Structure-based virtual screening and Prediction of ADMET Properties. Discussions with case studies. Combinatorial Chemistry and Library Design: Introduction. Diverse and Focused libraries. Library enumeration. Combinatorial library design strategies. Discussions with case studies.

Drug Discovery:

Interaction of ‗receptors‘ with agonists and antagonists. Receptor structure prediction methods. Enzyme kinetics and Interaction of enzymes with inhibitors (competitive, noncompetitive). Drug discovery pipeline. Optimization of lead compound, SAR (structure-activity relationships), Physicochemical and ADME properties of drugs and Prodrugs. QSAR (Quantitative structure activity relationships), Combinatorial synthesis. Case studies (e.g. G-coupled protein receptor agonists and antagonists, antibacterial agents etc).

Course outcomes:

At the end of the course the student will be able to:

• Learn about various chemoinformatics databases and their importance in drug discovery process.
• Gain knowledge about chemistry of medicinal compounds and Virtual Screening process.

Question paper pattern:

The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60.

• The question paper will have ten full questions carrying equal marks.
• Each full question is for 20 marks.
• There will be two full questions (with a maximum of four sub questions) from each module.
• Each full question will have sub question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each module.

Textbook/ Textbooks

1 Chemoinformatics: Theory, Practice, & Products Barry A. Bunin, Jürgen Bajorath, Brian Siesel, Guillermo Morales, Springer 2005

2 An Introduction to Chemoinformatics Andrew R. Leach, Valerie J. Gillet, Springer 2007

Reference Books

1 Chemoinformatics Johann Gasteiger Wiley-VCH 2003

2 An introduction to medicinal chemistry G. L. Patrick OxfordUniversity Press,New York. 5th edition

3 Computational Drug Design: A Guide for Computational and Medicinal Chemists, Young D. C., John Wiley & Sons, 2009.