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529-0002-10L 6 Credits BSC , DR , MSC D-CHAB
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Cheminformatics and Computer-Aided Drug Design

Lecturers & Examiners: Prof. Dr. Sereina Riniker, Dr. Gregory Landrum
VVZ CR n/a

Last Updated: 2026-06-01 11:30:38

Abstract

The lecture series introduces methods and applications in cheminformatics and computer-aided drug design. The topics cover molecular representations (2D & 3D), fingerprints and similarity, basics of machine learning, ligand-based and structure-based virtual screening. Theoretical concepts and algorithms presented are illustrated by practical applications and case studies.Progr. language: Python

Objective

The students will learn how molecules can be represented in computers and how molecular similarity is calculated. They will learn the basics of machine learning and the concepts of ligand-based and structure-based virtual screening to identify potential drug candidates. They will learn to understand possibilities and limitations of cheminformatics and computer-aided drug design methods in pharmaceutical chemistry.

Content

The topics include molecular representations (2D and 3D), fingerprints and similarity, basics of machine learning, ligand-based virtual screening (similarity search, QSAR, etc.), and structure-based virtual screening (docking, physics-based methods). Programming language is Python.

Resources

Lecture Notes

Script will be available onwww.riniker.ethz.ch.

Literature

Recommended textbooks: 1) J. Gasteiger, T. Engel (2018), “Chemoinformatics : Basic Concepts and Methods”, Wiley-VCH. 2) N. Brown (2016), “In Silico Medicinal Chemistry: Computational Methods to Support Drug Design”, Royal Society of Chemistry. 3) A. Bender, R. Guha (2012), “Computational Approaches in Cheminformatics and Bioinformatics”, John Wiley & Sons. 4) G. Schneider, K.-H. Baringhaus (2008) "Molecular Design – Concepts and Applications", Wiley-VCH.

Learning Materials (Links)

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 60 minutes
Aids
Keine Hilfsmittel.
Lernelement: Bei erfolgreicher Durchführung der Übungen kann die Gesamtnote additiv um bis zu 0.25 Notenpunkte verbessert werden.

Course Components

Type Title Time & Place Hours
lecture with exercise Cheminformatics and Computer-Aided Drug Design
  • Wed 13:45-15:30 (HCI J 6)
  • Thu 11:45-12:30 (HIL E 9)
3 h weekly

Offered In