Generating molecules from scratch with bespoke properties is arguably one of the most challenging tasks in chemistry. In the past few years, generative deep learning has remarkably enhanced the field of de novo design, by allowing to generate novel bioactive molecules from scratch, without the need of human-engineered features and assembly rules. By learning from the underlying data distribution, methods like Recurrent Neural Networks (RNNs) can be used to generate novel molecules from scratch that possess the desired bioactivity profile. The Molecular ML group is currently active in boosting the potential of generative deep learning to support medicinal chemistry and drug discovery.

• Selected publications
• - Grisoni F, Huisman BJ, Button AL, Moret M, Atz K, Merk D, Schneider G. (2021). Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Science advances 7 , e3338. DOI: 10.1126/sciadv.abg3338t [link]
• - Moret M, Helmstädter M, Grisoni F, Schneider G, Merk D (2021). Beam search for automated design and scoring of novel ROR ligands with machine intelligence. Angewandte Chemie International Edition 60, 19477. DOI: 10.1002/anie.202104405 [link]
• - Moret M, Grisoni F, Katzberger P, Schneider G (2021). Perplexity-based molecule ranking and bias estimation of chemical language models. ChemRxiv, under review. [link]
• - Moret M, Grisoni F, Brunner C, Schneider G (2021). Leveraging molecular structure and bioactivity with chemical language models for drug design. ChemRxiv, under review. [link]
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