22.07.2024

DFG: Priority Programme “Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning”

Deadline: 29.10.2024

The Senate of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) has established the Priority Programme „Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning” (SPP 2363). The programme is designed to run for six years; the present call invites proposals for the second three-year funding period.

This programme aims at connecting communities from the fields of machine learning and data science with scientists working in the areas of molecular chemistry and pharmacology. Machine learning for molecular applications and questions (Molecular Machine Learning, MML) has emerged as an area of interest with a high potential to change current workflows in all fields of chemistry as well as pharmacology. As such, it poses several outstanding challenges. This Priority Programme aims at tackling these challenges in a holistic fashion, covering a spectrum of topics ranging from data generation and the application of new algorithms to explainable artificial intelligence (ExAI). In general, all projects are required to contribute to the whole MML community by developing reusable tools, methodologies, datasets or broadly utilisable applications. Each proposal must be positioned at the interface of chemistry/pharmacology and machine learning in at least one of the following five areas: 

• design and evaluation of molecular representations for machine learning; 
• machine learning as a tool for theoretical and organic chemistry; 
• machine learning for medicinal chemistry and drug design; 
• overcoming data limitations by data generation, evaluation and data-free approaches;
• development of machine learning tools for molecular applications including ExAI, data augmentation strategies and software suites.

The funding period aims at improving methodologies for MML and understanding underlying principles. Therefore, new representations need to be developed, datasets are to be generated and methods need to be adapted, based on knowledge from the chemical and computer science domains. Within these topics, projects designed to gain deep knowledge about chemical and chemo-informatic relationships are highly encouraged. In addition, first feasibility studies should be carried out, examining state-of-the-art concepts on various applications. The focus of the second funding period is on using prior knowledge to develop these applications further and transform them into software tools that are usable in scientists’ day-to-day work. These tools should not only be applied in the MML domain but impact different areas of chemistry as well as pharmacology. As developments in the field of MML will further accelerate, it is necessary that, if required by the state of knowledge, all topics addressed can be eligible for funding within both periods. 

While machine learning has many applications in various overlapping fields, this programme specifically focuses on MML. This excludes the modelling of protein surfaces, properties of entire materials and periodic systems if these are not predominantly governed by the molecular constituents (e. g. molecular crystals). This also excludes projects that target the development or improvement of heterogeneous catalysts without explicitly describing them by their molecular structure. 

Proposals must be written in Englishand submitted by 29 October 2024.

For scientific enquiries, please contact the Priority Programme coordinator:
Professor Dr. Frank Glorius
Westfälische Wilhelms-Universität Münster 
Organisch-Chemisches Institut
Corrensstr. 36
48149 Münster
phone +49 251 83-33248
gloriusspam prevention@uni-muenster.de 

For questions related to the DFG review process, please contact:
Dr. Markus Behnke
phone +49 228 885-2181
markus.behnkespam prevention@dfg.de 

For administrative questions regarding the DFG proposal, please contact:
Susanne Schnitzler
phone +49 228 885-3084
susanne.schnitzlerspam prevention@dfg.de 

More Information:
https://www.dfg.de/de/aktuelles/neuigkeiten-themen/info-wissenschaft/2024/ifw-24-55