Course Overview

From this course students will learn the concepts of biomedical data and molecular simulation analysis leading to establish the basis of in silico drug discovery research.

Learning Achievement

Upon completion of this course, students will be trained and able to work on:
1. Clinical data processing and analysis using computational techniques
2. Perform molecular simulation studies
3. Perform drug discovery studies for disease targets.

Competence

Expertise, English language proficiency

Course prerequisites

Grading Philosophy

The students will be evaluated by report submissions (Assignment submission).
Note that grades (A+, A, B, C and D) are to be determined based on the score (> 90% : A+, 75-90%:A,60-80%:B,50-60%:C,<50%:D).

Course schedule

Background of informatics for drug discovery
Ligand based drug design
Structure based drug design
Clinical statistics and data mining
Text mining for drug discovery
Basics of molecular dynamics (MD) simulations
Applications and analysis of MD simulations for biological systems
Basics of the Density Functional Theory (DFT)
DFT analysis of biomolecules and DFT-MD simulations
Large-scale DFT techniques and machine-learning methods for complex biological systems

Course type

Lectures

Online Course Requirement

Instructor

Shirai Hiroki,Miyazaki Tsuyoshi,Futamura Yasunori

Other information

Site for Inquiry

Link to the syllabus provided by the university