Francesca Grisoni1,2,3, Claudia S. Neuhaus1,2, Gisela Gabernet1,2, Alex T. Müller1, Jan A. Hiss1, Gisbert Schneider1
1Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
2these authors contributed equally to this work
3University of Milano-Bicocca, Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, 20126 Milan, Italy
Tóm tắt
AbstractConstructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short‐term memory cells was trained on α‐helical cationic amphipathic peptide sequences and then fine‐tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities.
