SPP 2363:  Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning

Artificial intelligence is indisputably among the fastest developing and most demanded topics of our time. This technology makes everyday life easier and changes society as well as the workplace. While IT companies, and academic groups from the fields of computer science and mathematics rapidly adopted the new field, natural sciences such as biochemistry or chemistry only now begin to gradually explore the potential of machine learning (ML) methods. Our goal is to develop and apply modern ML algorithms in their entire range to molecular problems. While current approaches already help, for example, to determine molecular properties and to screen molecules virtually, future molecular machine learning should use generative models to suggest molecules with specific properties and activities, develop and optimize reactions independently, and evaluate and interpret analytical data within seconds. The first step is the design of molecular representations that increase the understanding of ML and enable robust and comparable applications. In clever combination with state-of-the-art machine learning algorithms, problems such as small data sets, highly complex questions and large experimental errors can be overcome, and previously unknown molecular relationships can be found. Ultimately, applications that are highly valuable in everyday laboratory work should be converted in easy-to-use software suites and experimental scientists should be trained on them. Thus, this priority program will help to modernize an entire subject area. To achieve this, it is necessary to unite existing innovative efforts in the fields of biochemistry, chemistry, computer science, mathematics and pharmacy in order to use all available knowledge on the one hand and to combine the most modern methods of the theoretical and practical world to develop advanced machine learning models and methods on the other. This program will fulfill the AI strategy of the Bundesregierung and can establish Germany internationally as a leading location for molecular machine learning.

DFG Programme Priority Programmes

International Connection Denmark, France, Switzerland, USA

• Advanced learning strategies for potential energy surfaces applied to organic electrolytes (Applicants Holm, Christian ;  Kästner, Johannes )
• AI-Empowered Universal Workflow for Molecular Design of Performant Photoswitches (Applicants Hecht, Stefan ;  Reuter, Karsten )
• Coordination Funds (Applicant Glorius, Frank )
• Development and application of improved ligand descriptors and representations for inverse catalyst design (Applicant Däschlein-Gessner, Viktoria H. )
• Elucidating Fingerprints – Towards a Holistic Explanatory Toolbox for Molecular Machine Learning (Applicants Glorius, Frank ;  Jiang, Xiaoyi )
• Exploring tailored Ru-triphos catalysts for hydrogenation reactions by combination of experimental, computational, and machine learning techniques (Applicants Bannwarth, Christoph ;  Klankermayer, Jürgen )
• Fourth-Generation Neural Network Potentials for Molecular Chemistry (Applicant Behler, Jörg )
• Machine learning approaches for faster discovery and adaptation of enzymes for difficult chemical reactions (MacBioSyn). Part I: providing solutions for regioselective oxygenations by 2OGD oxidases (Applicant Davari Dolatabadi, Ph.D., Mehdi )
• Machine Learning for developing and understanding novel, asymmetric 3d metal-catalyzed C–H activations (Applicant Ackermann, Lutz )
• Machine-Learning-guided chemical space exploration: automatic creation and navigation of ultra-large open-source molecular libraries (Applicant Kolb, Peter )
• Machine learning of hierarchical ultrafast molecular forcefields (HUMF) (Applicant Wenzel, Wolfgang )
• Molecular Descriptors in Matrix Completion Methods (Applicants Hasse, Hans ;  Jirasek, Fabian ;  Leitte, Heike )
• Molecular Machine Learning for Asymmetric (Organo-)Catalysis (Applicant Schreiner, Peter R. )
• Multi-fidelity, active learning strategies for exciton transfer among adsorbed molecules (Applicants Kleinekathöfer, Ulrich ;  Zaspel, Peter )
• Neural fingerprints as structure and activity-sensitive molecular representations (Applicants Koch, Oliver ;  Risse, Benjamin )
• Quantum Chemical Molecular Representations for Machine Learning (Applicant Grimme, Stefan )
• SAFE: Synthetically Accessible Fragment Space Extensions by Machine Learning-Based Approaches (Applicants Glorius, Frank ;  Rarey, Matthias )
• Understanding the interaction of organic molecules and metal ions by robot-based high-throughput experimentation and molecular machine-learning (Applicants Gräfe, Stefanie ;  Schubert, Ulrich S. )
• Virtual Drug Screening in the Chemical Space Accessible by Chemical Synthesis (Applicants Meiler, Jens ;  Stadler, Peter Florian )

Spokesperson Professor Dr. Frank Glorius