2025 (Current Year) Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Artificial Intelligence
Molecular Simulation
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Masakazu Sekijima / Ryunosuke Yoshino / Nobuaki Yasuo
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Intensive
- Class
- -
- Course Code
- ART.T545
- Number of credits
- 110
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Apr 3, 2025
- Language
- English
Syllabus
Course overview and goals
Molecular simulation with the aid of a supercomputer is an indispensable tool for a life and/or computational science study. This course gives a short overview of molecular orbital calculations by quantum mechanics and molecular dynamics calculations by Newtonian mechanics, followed by an exercise of these calculations mainly targeting biomolecules. Besides, invited speakers from industry provide on-scene use of these technologies.
This course has two aims. The first is to acquire a working knowledge of molecular simulation. The other is to be able to consider using molecular simulation for their studies. These aims would be achieved by experiencing molecular simulations in this course.
Course description and aims
1) Be able to imagine how molecules behave in atomistic level by visualization of simulation results.
2) Be able to interpret molecular simulation results.
3) Be able to utilize molecular simulation for their studies.
Keywords
Molecular orbital calculation, molecular dynamics calculation, docking simulation, Computer-aided drug discovery
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course is conducted face-to-face in a classroom equipped with computers for students. Students spend almost all the time exercising molecular simulation flows, i.e. an input build, calculation, interpretation of results, and visualization.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction - Overview of molecular simulation | Understanding backgrounding theory of molecular simulation and their applications. |
| Class 2 | What is supercomputer? How to use Linux system | Understanding fields where a supercomputer is utilized. An exercise of operating a Linux system. |
| Class 3 | Molecular orbital calculation 1: Structure optimization, configurational potential energy | Conducting structure optimisation and potential energy calculation of a small molecule. |
| Class 4 | Molecular orbital calculation 2: Infrared spectrum, enthalpy of formation | Calculating infrared spectrum and enthalpy of formation of a small molecule. Interpreting calculation results by using the corresponding experimental data. |
| Class 5 | Molecular orbital calculation 3: Transition state structure, reaction coordinate | Predicting reaction coordinate of small molecules, e.g. SN2 and Diels-Alder reactions. |
| Class 6 | Molecular orbital calculation 4: NMR and UV-vis spectra | Calculating NMR and UV-vis spectra of a small molecule and comparing calculated and experimental results. |
| Class 7 | Molecular dynamics 1: Molecular dynamics of protein molecule I | Conducting molecular dynamics simulation of a protein molecule and visualizing the results. |
| Class 8 | Molecular dynamics 2: Molecular dynamics of protein molecule II | Conducting molecular dynamics simulation of a protein-drug complex molecule and visualizing the interaction between protein and drug molecules. |
| Class 9 | Molecular dynamics 3: Free energy calculation | Conducting calculation of the change in binding free energy of protein drug molecules upon mutations in the protein. Calculating solvation free energy of small molecules. |
| Class 10 | Molecular dynamics 4: Exploration of free energy surface with the extended sampling method | Conducting free energy calculation of a molecule with an enhanced sampling method and visualizing results. |
| Class 11 | Computer-aided drug discovery 1: Ligand-based method | Predicting potential inhibitors of a target biomolecule based on known inhibitors’ information. |
| Class 12 | Computer-aided drug discovery 2: Structure-based method, docking simulation | Predicting potential inhibitors of a target biomolecule based on a structure of the target molecule. |
| Class 13 | Computer-aided drug discovery 3：Project based learning for drug discovery (1) | Designing a plan for a proposal of potential inhibitors against a designated target biomolecule by combining tools provided in this course. |
| Class 14 | Computer-aided drug discovery 4：Project based learning for drug discovery (2) | Designing a plan for a proposal of potential inhibitors against a designated target biomolecule by combining tools provided in this course. |
Study advice (preparation and review)
Textbook(s)
No textbook is set.
Reference books, course materials, etc.
Materials used in every lesson are handed out in the class.
Evaluation methods and criteria
Reports for relevant simulation results, interpretation of the results, and invited talks are taken into account.
Related courses
- LST.A211 ： Physical Chemistry III
- CSC.T353 ： Biological Data Analysis
- ART.T543 ： Bioimformatics
Prerequisites
No prerequisites.
Other
It is scheduled to be held in August and will be notified again.
This lecture will be held in Room 1, 3rd floor, South Bldg. 4. (Ookayama Campus）.
Lecture rooms are possibly subject to change.