Coordination Funds

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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.The requested coordination funds and the underlying will help to bring together the individual research groups, to foster strong and beneficial relationships and collaborations, to train and enable the doctoral students, to connect the PP with the international community and also to reach out to the general public. I will work hard to ensure that this PP will become a success story and a scientific highlight.