Details of CH4229 (Spring 2027)
| Level: 4 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| CH4229 | Computational Techniques in Chemical Biology | Saptarshi Pyne, Susmita Roy |
| Preamble |
|---|
| This course provides a comprehensive foundation to the most important concepts of computational methods used and their application in chemical biology and drug discovery.
By grounding physical and biochemical principles in practical simulation, students will learn to set up and analyze Molecular Dynamics (MD) trajectories for protein systems. The curriculum bridges the gap between atomic-level Force Fields and macroscopic observables, highlighting both the power and the limitations of current computational techniques. Furthermore, the course explores the subdisciplines of Structure-Based Drug Discovery. It also integrates modern Artificial Intelligence methods. Students will examine how AI-driven network biology can be leveraged to analyze, identify and validate functional target molecules within complex biological systems. |
| Syllabus |
|---|
| Foundations of Molecular Modelling and Simulation Techniques
(a) Introduction to chemical/biological processes of different timescale and length scale (b) Introduction to Model Building and Molecular Dynamics (c) Statistical Mechanics Fundamentals: Phase space; Ensembles; Postulates and Hypothesis; Connecting microscopic trajectories to macroscopic observables Learn and Run MD Engine (hands-on sessions) a. Empirical potentials: the Force Field b. Equations of motion; Integration Algorithms c. System Constraints & Boundary Conditions d. Thermodynamic control: thermostats and barostats Trajectory Analysis & Data Interpretation (hands-on sessions) a. Protein Simulation Workflow: System preparation, equilibration, and production runs b. Structural analysis c. Static vs. dynamic quantities d. Convergence Assessment 4. Advanced Applications: Free Energy & Drug Discovery a. Thermodynamics of Binding b. Determining Binding Kinetics Introduction to Machine Learning and Network Biology: a. Introduction to machine learning b. Application of unsupervised machine learning techniques for clustering c. Application of supervised machine learning techniques for network inference d. Network visualization e. Network analysis (topological and pathway analysis) |
| References |
|---|
| Text Book:
1. Understanding Molecular Simulation: D. Frenkel and B. Smit (Elsevier) 2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. An Introduction to Statistical Learning with Applications in Python. Springer, 2023. 3. Computer Simulations of Liquids, M.P Allen and D.J Tildesley (Oxford) 4. The Art of Molecular Dynamics Simulations: D. C. Rapaport (Cambridge) 5. A Practical Introduction to the Simulation of Molecular Systems, Martin J. Field (Cambridge) 6. Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, and Ralf Herwig. Systems Biology: a Textbook. John Wiley & Sons, 2016. 7. Uri Alon. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC, 2006. 8. Mark Newman, Networks: An Introduction, Oxford University Press, 2010. 9. Albert-Laszio Barabasi and Marton Posfai. Network Science. Cambridge University Press, 2016. |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 2 | Not Allowed |
| 2 | IP | 4 | Not Allowed |
| 3 | MP | 2 | Not Allowed |
| 4 | MP | 4 | Not Allowed |
| 5 | MR | 2 | Not Allowed |
| 6 | MR | 4 | Not Allowed |
| 7 | MS | 10 | Not Allowed |
| 8 | MS | 4 | Not Allowed |
| 9 | MS | 6 | Not Allowed |
| 10 | MS | 8 | Not Allowed |
| 11 | RS | 1 | Not Allowed |
| 12 | RS | 2 | Not Allowed |