2017 Molecular Simulation

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Artificial Intelligence
Sakurai Minoru  Sekijima Masakazu  Yoshino Ryunosuke  Kimura Suguru Roy 
Class Format
Lecture / Exercise     
Media-enhanced courses
Day/Period(Room No.)
Tue1-2(情報ネットワーク演習室(すずかけ台))  Fri1-2(情報ネットワーク演習室(すずかけ台))  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

Molecular simulation with the aid of a super computer is an indispensable tool for a life and/or computational science study. This course gives a short overview of molecular orbital calculations on the basis of quantum mechanics and molecular dynamics calculations on the basis of Newtonian mechanics, followed by exercise of these calculation 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.

Student learning outcomes

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.


Molecular orbital calculation, molecular dynamics calculation, docking simulation, Computer-aided drug discovery

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

The course is given 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/Required learning

  Course schedule Required learning
Class 1 Introduction - Overview of molecular simulation Understanding backgrounding theory of molecular simulation and their applications.
Class 2 What is super computer? How to use Linux system Understanding fields where a super computer is utilized. Exercise of operating a Linux system.
Class 3 Molecular orbital calculation 1: Structure optimization, configurational potential energy Conducting structure optimization 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 Invited talk Understand how molecular simulations are utilized in industry.
Class 8 Molecular dynamics 1: Molecular dynamics of protein molecule I Conducting molecular dynamics simulation of a protein molecule and visualizing the results.
Class 9 Molecular dynamics 2: Molecular dynamics of protein molecule II Conducting molecular dynamics simulation of a protein-drug complex molecule and visualizing interaction between protein and drug molecules.
Class 10 Molecular dynamics 3: Free energy calculation Conducting calculation of change in binding free energy of protein drug molecules upon mutations in the protein. Calculating solvation free energy of small molecules.
Class 11 Molecular dynamics 4: Exploration of free energy surface with extended sampling method Conducting free energy calculation of a molecule with an enhanced sampling method and visualizing results.
Class 12 Invited talk Understand how molecular simulations are utilized in industry.
Class 13 Computer-aided drug discovery 1: Ligand-based method Predicting potential inhibitors of a target biomolecule based on known inhibitors’ information.
Class 14 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 15 Computer-aided drug discovery 3:Project based learning for drug discovery Designing a plan for proposal of potential inhibitors against a designated target biomolecule by combining tools provided in this course.


No textbook is set.

Reference books, course materials, etc.

Materials used in every lesson are handed out in the class.

Assessment criteria and methods

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 (i.e., required knowledge, skills, courses, etc.)

No prerequisites.



Page Top