Erasmus Mundus Joint Master - ChEMoinformatics+

Application form - Closed

ChEMoinformatics+ — 2026-03-16T10:31:11Z

The application form for the session 2026-2027 is closed. Next opening the 15/10/2026.

Our Erasmus Mundus Summer School in Ljubljana!

Chaïma Mohamed — 2025-10-13T08:33:18Z

We are thrilled to share that our 2025 Chemoinformatics + Erasmus Mundus Summer School has just concluded with great success. Held in the beautiful city of Ljubljana, Slovenia, this intensive week-long program brought together students, researchers, and professionals from across Europe and beyond for a unique learning and networking experience.

Organized in close collaboration with the University of Ljubljana, the Summer School offered a rich program combining advanced lectures, hands-on workshops, and interactive discussions covering key topics in chemoinformatics, data science, and molecular modeling. Participants also had the opportunity to explore real-world applications and recent innovations in the field, guided by leading experts from academia and industry.

We would like to express our heartfelt thanks to all the students who participated with such enthusiasm, curiosity, and commitment. Your energy and engagement throughout the week truly made this event special.

We also extend our deepest gratitude to our outstanding speakers and partners, whose contributions and insights played a crucial role in shaping the program and elevating the academic quality of the school.

Finally, a very special thank you goes to the University of Ljubljana for their exceptional hospitality and flawless organization. Their support and dedication were instrumental in creating an inspiring and welcoming environment for all participants.

We are proud of what we achieved together and already look forward to future editions of the Summer School. Until next time!

PyMOL: A Powerful Tool for Molecular Visualization and Structural Analysis

ChEMoinformatics+ — 2025-07-06T19:31:53Z

by Michele Brignoli, Track « Chemoinformatics and Physical Chemistry », Milan-Strasbourg, 2025

Molecular visualization is essential for gaining a deep understanding of molecular interactions and properties. Among the various tools available, PyMOL [1] stands out as one of the most versatile and powerful platforms for visualizing molecular structures. It is available in both a free, open-source version and a more feature-rich paid version, which includes access to advanced visualization capabilities. Numerous online tutorials and resources support new users in exploring both basic and advanced functionalities, from generating high-resolution molecular images to creating animations of dynamic molecular interactions.

The software is especially valuable for visualizing binding sites and analyzing biomolecular interactions (Figure 1), making it a crucial asset in rational drug design [2] by highlighting hydrogen bonds, hydrophobic contacts, and other key residues. PyMOL also integrates external tools and plugins, enhancing its functionality for advanced tasks like conformational analysis and molecular dynamics visualization.

With Python scripting, users can streamline workflows, execute complex manipulations, and automate routine tasks. This scripting capability empowers users to create complex visualizations, develop custom plugins, and tailor the software to meet specific research needs. Additionally, an active community of users contributes a wealth of scripts, plugins, and tutorials, offering robust support for both beginners and advanced users alike [3].

The latest versions of PyMOL include enhanced features such as real-time collaboration and integration with machine learning models. With its ongoing development and strong community support, PyMOL is positioned as one of the foundational tool for future innovations in molecular visualization and structural analysis. Its accessibility, precision, and versatility make it an invaluable resource for a wide range of users, from students to professional researchers in structural biology and computational chemistry. Therefore, I think that PyMOL is becoming indispensable in scientific research across bioinformatics and chemoinformatics disciplines.

Figure 1.

A fragment of TGF-β3 within the active site of integrin αVβ8 (PDB: 8VS6). On the left, polar contacts are highlighted with a red dashed line. On the right, the surface view shows polar residues in red and nonpolar residues in green.

Michele Brignoli

References:
[1] The PyMOL Molecular Graphics System, Version 3.0 Schrödinger, LLC.
[2] Shuguang Yuan et al. "Using PyMOL as a platform for computational drug design." Wiley Interdisciplinary Reviews: Computational Molecular Science, 7 (2017). https://doi.org/10.1002/wcms.1298.
[3] Magnus Kjaergaard et al. "A Semester-Long Learning Path Teaching Computational Skills via Molecular Graphics in PyMOL." The Biophysicist (2022). https://doi.org/10.35459/tbp.2022.000219.

Advances in Autonomous Chemical Research

ChEMoinformatics+ — 2025-07-06T19:21:44Z

by: Trung Le, Track « Chemoinformatics and Materials Informatics », Bar Ilan - Strasbourg, 2025

With the rise of artificial intelligence (AI) models such as ChatGPT, DeepSeek, Mistral AI, or DALL-E, AI has growing importance in many applications, including chemistry. In chemistry, artificial intelligence is developed as a subject of chemoinformatics. Chemoinformatics and automation have already been associated with running complex chemical experiments for screening, synthesis, and other tasks. The paper by Boiko et al. [1] envisions a "CoScientist," a multiple large language model (LLM) based intelligent agent, to support chemists in designing and running chemical experiments.

The CoScientist browses the internet and relevant documentation, and uses application programming interfaces (APIs) to control robotic devices. The prototype uses a modular architecture (Figure 1). The main module, the “Planner”, orchestrates the actions of software processes (workers) able to search the web (GOOGLE), browse web pages (BROWSE), prototype scripts for controllers (PYTHON) with the help of relevant documentation (DOCUMENTATION), and finally, run the experiment on the hardware (EXPERIMENT).

Figure 1.

(a) The Planner agent orchestrates the actions of workers to search the internet, design an experiment, generate the controller scripts, and perform the experiment. (b) A list of tasks successfully achieved with the help of the CoScientist. (c) An illustration of the CoScientist hardware.

Nature 624, 570–578 (2023). https://doi.org/10.1038/s41586-023-06792-0

The prototype has been assembled around a liquid handler and a heater-shaker to act autonomously using data from the internet, performing the necessary calculations, and ultimately writing and runner the controller code for the hardware. The system demonstrated "reasoning" capabilities as it was able to identify and search for missing information, solving multi-step problems.

Overall, the result presents a promising proof of concept for the future of autonomous experiments. Echoing the words of Derek Lowe, “It's not that machines are going to replace chemists. It's that the chemists who use machines will replace those that don't” [2].

References:
[1] Boiko, D.A., MacKnight, R., Kline, B. et al. Autonomous chemical research with large language models. Nature 624, 570–578 (2023). https://doi.org/10.1038/s41586-023-06792-0
[2] Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., Isayev, O., Curtalolo, S., Fourches, D., Cohen, Y., Aspuru-Guzik, A., Winkler, D. A., Agrafiotis, D., Cherkasov, A., & Tropsha, A. (2020). Qsar without borders. Chemical Society Reviews, 49(11), 3525–3564. https://doi.org/10.1039/d0cs00098a

Revolutionizing oncology with gene therapy: the role of computational methods

ChEMoinformatics+ — 2025-07-06T19:08:46Z

by: Toma Legrand, Track « Chemoinformatics for Organic Chemistry », Lisbon-Strasbourg, 2025

Cancer's complexity and adaptability make it one of the most challenging diseases to treat. Conventional therapies like radiation and chemotherapy often fail to distinguish between cancerous and healthy cells, resulting in many unwanted side effects.

Several gene therapy approaches have been approved recently as cures for cancers. Behind the scenes, a number of computational approaches, involving chemoinformatics and bioinformatics, have made these successes possible. More accurate, personalized, and successful gene therapies are likely to revolutionize oncology.

A spectacular step toward personalized medicine for cancer treatment is monitoring gene expression in tumors relative to healthy tissues. In my opinion, one such game-changing computational approach is mapping out gene networks (Figure 1). Indeed, atypical gene expression processes are frequently associated with cancers. Gene networks rationalize the interactions between genes as observed in cells. Mapping such networks is a powerful way to identify potential weak gene therapy targets as they appear as anomalies in cancer cells compared to healthy ones.

Figure 1

Example of gene-networks workflow

Nat Protoc 18, 1745–1759 (2023). https://doi.org/10.1038/s41596-022-00797-1

Identification of a gene can lead to the identification of relevant protein targets—such as those resulting from the transcription of the identified gene. It is then possible to design small molecules targeting these proteins. Their 3D structures are invaluable for this task when known experimentally. If not, they can be deduced from homology modeling or artificial intelligence models, using AlphaFold [2], for instance.

Analogous to gene interactions, protein-protein interaction networks have become an extremely valuable strategy. This is illustrated, for instance, by the MaSIF software [3]: it builds a network connecting two proteins if their shape allows for molecular recognition. This complements the potential to develop personalized anti-cancer drugs disrupting presumably pathogenic protein-protein recognition or protein sequences suitable for gene therapy.

Developing a one-size-fits-all treatment is extremely difficult because tumors within the same person might differ greatly from one another. Computational techniques, on the other hand, are becoming increasingly efficient and enable the examination of large quantities of genetic data from various malignancies, facilitating personalized gene therapy. However, there exists a huge gap between numerical models of a tumor and reality: how can we ensure gene treatments are effectively delivered to the appropriate cells? Are these treatments safe? What are the ethical pitfalls of such therapies?

References:
[1] Rosenthal, S.B., Wright, S.N., Liu, S. et al. “Mapping the common gene networks that underlie related diseases.” Nat Protoc 18, 1745–1759 (2023). https://doi.org/10.1038/s41596-022-00797-1
[2] Jumper, J., Evans, R., Pritzel, A. et al. “Highly accurate protein structure prediction with AlphaFold.” Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2
[3] Gainza, P., Sverrisson, F., Monti, F. et al. “Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.” Nat Methods 17, 184–192 (2020). https://doi.org/10.1038/s41592-019-0666-6

Registration for our next summer school in Ljubljana, Slovenia, is open!

Chaïma Mohamed — 2025-04-08T09:17:09Z

We are delighted to announce that registration for the CS3-2025 conference, to be held in Ljubljana, Slovenia, is now open! Applications are warmly welcome.

This flagship event will bring together leading experts from the international chemoinformatics+ community, offering a unique opportunity for knowledge exchange, networking, and inspiration. We are honoured to welcome an exceptional line-up of speakers, including:

Prof. Alexandre Varnek — University of Strasbourg

Prof. Hanoch Sendorowitz — Bar Ilan University

Prof. Thierry Langer — University of Vienna

Prof. Maria-Paola Costi — University of Modena

Prof. Sheraz Gul — Fraunhofer Institute, Hamburg

Prof. Martin Šicho — University of Chemistry and Technology, Prague

Prof. Marc Baaden — Université Paris Cité

Prof. Pavel Polishchuk — Palacký University Olomouc

Prof. Dragosh Horvath — University of Strasbourg

Prof. Matija Strlič — University of Ljubljana

In addition, our first-year Erasmus Mundus students will have the opportunity to present their research, take part in a dynamic hackathon, and attend a rich programme of lectures and interactive sessions.

Registration is possible here.

We look forward to welcoming you in Ljubljana for what promises to be an exciting and inspiring edition of CS3!

The EM-ACT annual meeting took place in Rome!

Chaïma Mohamed — 2025-01-29T08:36:12Z

Members of the EM-ACT board are moderating the 4th in-person meeting

Les membres du Conseil d'administratif de l'EM-ACT animent la 4ème réunion en présentiel

Members of the Erasmus Mundus Association for Consortia Cooperation (EM-MACT) gathered in Rome on January 10, 2025, for their fourth in-person meeting.

The event brought together board members as well as pedagogical and administrative coordinators of Erasmus Mundus Joint Masters, who discussed various aspects of program management.

Such meetings provide a valuable opportunity for coordinators to strengthen their network, enhance the visibility of Erasmus Mundus staff and students, and, most importantly, collaborate to further improve the quality of these excellence-driven master's programs.

The Erasmus Mundus Association (EMA) was also represented by Nishkan Paudel, Head of the Students and Alumni Relations Unit at EMA.

The Erasmus Mundus Joint Master's in Chemoinformatics+ was represented by Chaïma Mohamed, Project Manager at the University of Strasbourg, France.

Role of chemoinformatics in the research field of flavor compounds

Laura Belvisi — 2025-01-21T10:36:58Z

by: Pierre-Alexandre Ho, Track «In Silico Drug Design», Strasbourg-Milan-Paris, 2023

The aim of chemoinformatics is to use chemical information to predict the compounds behavior. Largely used in pharmaceutical research, its scope extends to other domains including the food industry. For example, flavor ingredients, which are used for many applications (e.g. enhancing taste), can sometimes be toxic, while others have a health benefits. Naturally, the question of the prediction of the properties of these molecules can be asked. The purpose of this blog article is to provide examples of chemoinformatic applications in food industry.

In food materials, there is a category called “GRAS” (Generally Recognized As Safe) for compounds with no risks for humans. Many tests are performed to obtain this qualification but the literature suggests to replace / combine some of them with QSAR (Quantitative Structure Activity Relationship) technics to infer biological activities based on the chemical structure of a molecule. Alternatively, the biological profile of flavor compounds can be assessed by the comparison between GRAS flavors, natural and drug datasets (1). Chemical space analysis have been used in the aim identifying, for instance, similarities between GRAS flavors and approved antidepressant drugs.

The biomolecular basis of flavor perception are also explored using molecular dynamics simulation. For example, this method was performed to analyze the interaction between peptides and taste receptors enabling the discovery of new flavor compounds. It was used also to explore the behavior of flavor compounds in interaction with plastic packaging and in the strong alcoholic environment of spirit beverage such as Scotch whiskey (2).

Currently, artificial intelligence is used to characterize and identify flavor compounds. These techniques are coupled to high resolution analytical chemistry techniques. The aim is to supplement, to de-risk and to make more objective the work of human panelist in odor identification (3). Such tools are being developed for the flavor engineering industry to design new flavors (4).

In a nutshell, chemoinformatics has emerged as a versatile toolkit (Figure 1) for characterizing, identifying and predicting future flavor compounds.

Figure 1: Chemoinformatics use for flavors compounds discovery (4).

References
1. Medina-Franco JL, Martínez-Mayorga K, Peppard TL, Del Rio A. Chemoinformatic Analysis of GRAS (Generally Recognized as Safe) Flavor Chemicals and Natural Products. Taylor P, editor. PLoS ONE. 2012;7(11):e50798. https://doi.org/10.1371/journal.pone.0050798
2. Shuttleworth EE, Apóstolo RFG, Camp PJ, Conner JM, Harrison B, Jack F, et al. Molecular dynamics simulations of flavour molecules in Scotch whisky. J Mol Liq. 2023, 383:122152. https://doi.org/10.1016/j.molliq.2023.122152
3. Shang L, Liu C, Tang F, Chen B, Liu L, Hayashi K. Artificial intelligence-based gas chromatography-olfactometry for sensory evaluation of key compounds in food ingredients. 2022 Apr 22 [cited 2023 Oct 23]; Available from: http://biorxiv.org/lookup/doi/10.1101/2022.04.20.488977
4. Kou X, Shi P, Gao C, Ma P, Xing H, Ke Q, et al. Data-Driven Elucidation of Flavor Chemistry. J Agric Food Chem. 2023;71(18):6789–6802. https://doi.org/10.1021/acs.jafc.3c00909

Chemoinformatics: Past, Present, Future

Laura Belvisi — 2025-01-21T10:12:26Z

by:Eliya Davidov, Track «Chemoinformatics and Materials Informatics», Bar Ilan-Strasbourg, 2023

In 1998, Frank Brown coined the term Chemoinformatics, defining it as "all the information resources that a scientist needs to optimize the properties of a ligand to become a drug." Yet, the foundations of chemoinformatics trace back much earlier, to the 1950s and 60s, when computational chemistry first began taking shape [1].

In 1957, Ray and Kirsch published the first algorithm for substructure searching. Their groundbreaking paper described "a collection of machines... capable of performing a complete data processing task involving data storage facilities." This laid the groundwork for structure, similarity, and substructure searching in databases — core concepts that would later become vital in chemoinformatics. By 1963, Vleduts proposed the concept of "skeleton reaction schemes" and reaction centers, suggesting the possibility of machine-aided synthesis: "the possibility of a machine solution... the selection of ways synthesizing a given compound" [2]. Another pivotal moment came in 1962 when Hansch introduced QSAR (Quantitative Structure–Activity Relationships), which link a biological activity to chemical structure using factors (molecular descriptors) such as steric effects, electronic properties, and hydrophobicity.

In recent decades, with the rise of artificial intelligence, chemoinformatics has evolved. Its scope now extends beyond ligand optimization to encompass "the application of informatics methods to solve chemical problems" [3]. Without exhaustivity, this includes predictive modeling for biological activity, drug discovery, ligand-based design, 3D molecular docking, protein-ligand interactions, virtual screening, simulations, and molecular dynamics (Figure 1). Although much of the field focuses on biology, chemoinformatics also plays a role in materials science, aiding in the design of batteries, energetic materials, and other physical systems.

What lies ahead for chemoinformatics? With AI, increasing computational power, and the surge of big data, the future promises new breakthroughs. AI is expected to push chemoinformatics into uncharted territories, such as drug discovery for rare diseases. Quantum computing will certainly be a major game changer in the realm of simulations and modeling, allowing for new algorithmic approaches to solve, for instance, complex graph isomorphism problems.

Figure 1. Chemoinformatics emerged as a field from the solutions found to data related problems shared by many other scientific domains. Medicinal chemistry and drug discovery subjects are still today strong driving forces in chemoinformatics.

References
1. P. Willett, Chemoinformatics: a history. WIREs Comput. Mol. Sci., 2011, 1, 46-56. https://doi.org/10.1002/wcms.1
2. G.E. Vleduts, Concerning one system of classification and codification of organic reactions. Inf. Stor. Ret. 1963, 1, 117–146. https://doi.org/10.1016/0020-0271(63)90013-5
3. J. Gasteiger, The central role of chemoinformatics. Chemometr. Intell. Lab. Syst. 2006, 82, 200–209. https://doi.org/10.1016/j.chemolab.2005.06.022

Welcome to our new EMA Programme Representative!

Chaïma Mohamed — 2024-10-02T12:09:59Z

We are pleased to introduce Fernando as the new Erasmus Mundus Association (EMA) Programme Representative for the Erasmus Mundus Joint Master in Chemoinformatics+.

Fernando is currently in his first year of the the programme, in the study track 'Chemoinformatics for physical chemistry', which brought him to Milan, Italy, where he now lives and studies.

Best of luck to Fernando and congratulations for this achievement!

Are you passionate about chemoinformatics? Do you want to take part in this master of excellence and thrive among students hosted in renowned institutions, in Europe and beyond?
Our application portal opens on 15 October! Don't miss the date!