DFG Priority Programme: Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning (SPP 2363)
The present call invites proposals for the first three-year funding period of the Priority Programme “Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning” (SPP 2363).
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 high potential to change current workflows in all fields of chemistry as well as pharmacology and thereby 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). All projects are required to contribute to the whole MML community by developing reusable tools, methodologies, datasets, or broadly utilizable 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 first funding period aims at improving methodologies for MML and understanding underlying principles. Therefore, new representations need to be developed, datasets shall be generated, and methods need to be adapted, based on knowledge from the chemical and computer scientific domain. Within these topics, projects designed to gain deep knowledge (ExAI) about chemical and chemoinformatic 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 aims at using prior knowledge to develop these applications further and transform them into software tools, usable in scientists’ every-day work.
Since MML is a highly interdisciplinary field of research, applicants will belong to various subject areas that can roughly be assigned to three groups: computer and data science (C), practical chemistry (P), and theoretical chemistry and chemoinformatics (T). Ideally, there will be tandem applications of researchers from complementary areas that can be closely linked.
Proposals must be written in English and submitted to the DFG by 15 August 2021 via elan. If you have not yet registered in elan, please do so by 1 August 2021.
In addition to submitting your proposal through elan, please send an electronic copy (pdf file) to the programme coordinator.
More information on the Priority Programme is available at: www.uni-muenster.de/SPP2363
For scientific enquiries, please contact the Priority Programme coordinator:
- Professor Dr. Frank Glorius
Westfälische Wilhelms-Universität Münster
phone +49 251 83-33248
For questions related to the DFG review process, please contact:
- Dr. Markus Behnke
phone +49 228 885-2181
For administrative questions regarding the DFG application, please contact:
- Angelika Spahn
phone +49 228 885-2440
For more information, please see the full call for proposals: www.dfg.de/foerderung/info_wissenschaft/2021/info_wissenschaft_21_44Zurück