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Strasbourg Summer School in Chemoinformatics - 2020

Chemoinformatics Strasbourg Summer School, Strasbourg 2020

Chemoinformatics Strasbourg Summer School 2020

Most of lectures announced for the summer school 2020 have been delivered online. See the Program section.

A special issue of Molecular Informatics (Vol39, Issue12, Dec2020) has been released. See the detail here.

You can ask your questions by mail using this email address: chemoinfo-school@unistra.fr

The following topics are suggested for the scientific program:

  • Big Data in Chemistry
  • In silico pharmacology
  • Material Informatics
  • Text mining in chemistry
  • Virtual screening techniques
  • Deep learning
  • Data analysis and visualization

Confirmed lecturers:

  • J. Bajorath (Univ. Bonn, Germany)
  • T. Langer (Univ. Vienna, Austria)
  • C. Sotriffer (Univ. Wuerzburg, Germany)
  • A. Tropsha (Univ. North Carolina, Chappel Hill, USA)
  • K. Funatsu (Univ. Tokyo, Japan)
  • J. Medina-Franco (Univ. Mexico)
  • J. Kirchmair (Univ. Vienna, Austria)
  • R. Glen (Univ. Cambridge, UK)
  • H. Senderowitz (Univ. Bar Ilan, Israel)
  • R. Sayle (NextMove Software, UK)
  • O. Taboureau (Univ. Paris Diderot, France)
  • O. Engkvist (ASTRAZENECA)
  • A. Oganov (Skoltech Univ., Moscow, Russia)
  • M. Rarey (Univ. Hamburg, Germany)
  • A. Cherkasov (Univ. British Columbia, Canada)

Tiny URL of this page: https://tinyurl.com/CSSS-2020
QR code:

QR code for the Chemoinformatics Strasbourg Summer School from the 29 June to the 3 July 2019



  • Alexandre VARNEK (Chairman) - University of Strasbourg, France
  • Gilles Marcou - University of Strasbourg, France
  • Dragos Horvath - National Center for Scientific Research (CNRS), France
  • Olga Klimchuk - University of Strasbourg, France
  • Fanny Bonachera - National Center for Scientific Research (CNRS), France

3. Register

Registration for online lectures

Registration is closed. Thank you for your participation to the Online Strasbourg Summer School in Chemoinformatics 2020 !

Program of the School


The program includes plenary lectures, poster session, oral presentations and hands-on tutorials. It covers the following topics:

  • Big Data in chemistry
  • Material Informatics
  • Machine-Learning methods
  • Virtual screening techniques
  • In silico pharmacology
Chemoinformatics Strasbourg Summer School
(University of Strasbourg, 29 June - 3 July 2020)

Watch the lectures here!

Monday 29 June


(University of Bonn, Germany)

Evaluating Progress in Lead Optimization

Tuesday 30 June


(University of British Columbia, Canada)

Deep Docking – a DNN Enabled Approach for Virtual Screening and its Application for COVID-19 Drug Discovery

(ASTRAZENECA, Gothenburg, Sweden)


AI in drug discovery an industrial perspective

(University of North Carolina, USA)

Biomedical Big Data Analytics: From Knowledge Graphs to de novo Drug Discovery

(National Autonomous University of Mexico, Mexico)

StARs and constellations in chemical space: a visual representation of Structure-Activity Relationships (download pdf)

Wednesday 1 July

Matthias RAREY

(University of Hamburg, Germany)

SMARTS.plus Supporting Chemical Pattern Design

(University of Wuerzburg, Germany)

Simulation-driven model builing:
Towards prediction of site-specific bioconjugation

Thursday 2 July


(University of Vienna, Austria)

Chemoinformatics in Natural Product-Based Drug Discovery

(NextMove Software, UK)

Automated mining of a database of 9.4M reactions from the patent literature, and its application to synthesis planning (download pdf)

Friday 3 July


(Bar Ilan University, Israel)

Materials Informatics: The marriage of data and materials sciences

(Skolkovo Institute of Science and Technology, Russia)

Computational materials discovery guided by artificial intelligence (download pdf)
Thierry LANGER

(University of Vienna, Austria)

A Computational Approach to Identify Potential Novel Inhibitors Against The Coronavirus SARS-CoV-2

(Paris Diderot University, France)

Network Sciences applied in pharmacology

Molecular Informatics special issue

A special issue of Molecular Informatics has been released, following the Online Strasbourg Summer School in Chemoinformatics.

The issue can be found here.

Cover Pictures

 Free Access
 Cover Picture: Consistent Cell‐selective Analog Series as Constellation Luminaries in Chemical Space (Mol. Inf. 12/2020)
 J. Jesús Naveja José, L. Medina‐Franco
 2081201 First Published: 01 December 2020

This manuscript is dedicated to all people affected directly or indirectly by the COVID‐19 pandemic.Initially proposed by Bajorath and colleagues, the concept of analog series is evolving as a novel data‐driven paradigm useful in making sense of high‐throughput screening data and other large chemical libraries. This concept allows not to talk only of individual hits in a screening, but even about potential leads or families of compounds with good performance in any large‐scale testing. In this case, we have devised new statistics to identify consistently selective analog series against specific cancer cell lines; such analog series can be seen as constellations in the chemical space containing bright stars. The flexibility of the analog series can still lead to several applications. More details can be found in the Full Paper by Jesús Naveja, and José L. Medina‐Franco, please see DOI 10.1002/minf.202000061.

Abstract | Full text | PDF | Request permissions


Free Access
7 th Strasbourg Summer School in Chemoinformatics
2081331 First Published: 01 December 2020

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Open Access
Cheminformatics in Natural Product‐based Drug Discovery
Ya Chen, Johannes Kirchmair
2000171 First Published: 28 July 2020

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Full Access

The SAR Matrix Method and an Artificially Intelligent Variant for the Identification and Structural Organization of Analog Series, SAR Analysis, and Compound Design

Atsushi Yoshimori, Jürgen Bajorath

2000045 First Published: 09 April 2020

SAR Matrix. Shown is a small SAR Matrix (SARM) containing three analog series with structurally related cores (shown on the left with distinguishing substructures colored red). Each cell in the matrix contains a unique compound (core‐substituent combination). Cells are color‐coded by logarithmic potency values (spectrum on the right). Empty cells represent virtual compounds (non‐existing core‐substituent combinations).

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Full Access From SAR Diagnostics to Compound Design: Development Chronology of the Compound Optimization Monitor (COMO) Method

Dimitar Yonchev, Dr. Martin Vogt, Prof. Dr. Jürgen Bajorath

2000046 First Published: 13 April 2020

Evaluating progress in lead optimization. Shown is a scatter plot comparing computationally evaluated chemical saturation and SAR progression of different analog series (dots).

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Full Papers

Free Access

A Chemographic Audit of anti‐Coronavirus Structure‐activity Information from Public Databases (ChEMBL)

Dragos Horvath, Alexey Orlov, Dmitry I. Osolodkin, Aydar A. Ishmukhametov, Gilles Marcou, Alexandre Varnek

2000080 First Published: 04 May 2020

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Full Access

Consistent Cell‐selective Analog Series as Constellation Luminaries in Chemical Space

J. Jesús Naveja, José L. Medina‐Franco

2000061 First Published: 10 May 2020

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Open Access

Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling

Dr. Arkadii Lin, Dr. Igor I. Baskin, Dr. Gilles Marcou, Dr. Dragos Horvath, Dr. Bernd Beck, Prof. Alexandre Varnek

2000009 First Published: 29 April 2020

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Full Access

Drug‐target‐ADR Network and Possible Implications of Structural Variants in Adverse Events

Bryan Dafniet, Natacha Cerisier, Karine Audouze, Olivier Taboureau

2000116 First Published: 29 July 2020

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Full Access

Ligand‐based Activity Cliff Prediction Models with Applicability Domain

Shunsuke Tamura, Tomoyuki Miyao, Kimito Funatsu

2000103 First Published: 23 August 2020

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Application Notes

Open Access

SMARTS.plus – A Toolbox for Chemical Pattern Design

Christiane Ehrt, Bennet Krause, Robert Schmidt, Emanuel S. R. Ehmki, Matthias Rarey

2000216 First Published: 30 September 2020

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