David Nippa | Scientist

David Nippa, Scientist, Roche

David Nippa is a Scientist in the Medicinal Chemistry department at the Roche Innovation Center Basel (RICB) working at the interface of automation, digitalization and artificial intelligence (AI). Before this role, David was a doctoral researcher at Roche and the LMU Munich. His PhD research focused on combining high-throughput experimentation (HTE) with data science and machine learning (ML) to enable late-stage drug diversification. David completed his undergraduate studies at the Technical University of Munich (TUM), Nanyang Technological University (NTU) Singapore and The Scripps Research Institute (TSRI) San Diego. In parallel to his studies, he was an intern at Wacker Chemie and Roche.


Day 1 (26th June) @ 17:30

Bridging the gap between the laboratory and data science to enable reaction forward prediction

Creating new chemical entities is a pivotal yet challenging task in the discovery of small molecule drugs, often representing the primary bottleneck that affects both time and cost. In particular, C-H activation reactions applied in the optimization process of lead compounds frequently necessitate extensive high throughput experimentation (HTE) to determine the appropriate conditions and substrates. Machine learning techniques, particularly those that effectively process three-dimensional (3D) molecular data, have been shown to be valuable across various chemical research areas. In this talk, we present the application of graph transformer neural networks (GTNNs) to C-H alkylation and borylation reactions. We highlight the significance of 3D structural data in predicting regioselectivity and assess the influence of electronic data on the accuracy of predictions. Furthermore, we demonstrate that GTNNs, once trained, can facilitate the virtual screening of chemical libraries to pinpoint suitable substrates efficiently. The ability to identify synthesizable chemical entities with the desired characteristics rapidly advances the exploration of chemical spaces relevant to drug development.

last published: 17/Jun/24 14:15 GMT

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