The process of discovering a drug follows a time-consuming and expensive pipeline that explores the chemical space of potential drugs today mainly based on wet-lab experiments and database searches. High expectations are placed on deep learning methods to simplify this process.

We believe that such neuro-explicit approaches are a key concept for substantial advances in drug discovery. Our research focuses on hybrid approaches where domain knowledge is integrated into neural learning models in various forms. This can significantly improve the generalization capabilities of neural models, allow extrapolation beyond training data, and thus need less data to learn from.



Backenköhler, Michael; Kramer, Paula Linh; Groß, Joschka; Großmann, Gerrit; Joeres, Roman; Tagirdzhanov, Azat; Sydow, Dominique; Ibrahim, Hamza; Odje, Floriane; Wolf, Verena; others,

TeachOpenCADD goes Deep Learning: Open-source Teaching Platform Exploring Molecular DL Applications Working paper


Abstract | Links | BibTeX

Volkamer, Andrea; Riniker, Sereina; Nittinger, Eva; Lanini, Jessica; Grisoni, Francesca; Evertsson, Emma; Rodríguez-Pérez, Raquel; Schneider, Nadine

Machine learning for small molecule drug discovery in academia and industry Journal Article

In: Artificial Intelligence in the Life Sciences, vol. 3, pp. 100056, 2023, ISSN: 2667-3185.

Abstract | Links | BibTeX

Born, Jannis; Markert, Greta; Janakarajan, Nikita; Kimber, Talia B.; Volkamer, Andrea; Martínez, María Rodríguez; Manica, Matteo

Chemical representation learning for toxicity prediction Journal Article

In: Digital Discovery, pp. -, 2023.

Abstract | Links | BibTeX

Bachelor’s/Master’s Thesis Topics

We offer a variety of different topics for Bachelor’s/Master’s theses in the area of deep learning for computer-aided drug discovery and design. If you are interested in doing your thesis with our group, please contact Gerrit Großmann.