Front Cover: Designing Anticancer Peptides by Constructive Machine Learning (ChemMedChem 13/2018). (6th July 2018)

• Record Type: Journal Article
• Authors: Grisoni, Francesca; Neuhaus, Claudia S.; Gabernet, Gisela; Müller, Alex T.; Hiss, Jan A.; Schneider, Gisbert
• Is Part Of: ChemMedChem. Volume 13:Number 13(2018)
• Page Start: 1260
• Page End: 1260
• Publication Date: 2018-07-06
• Subjects: artificial intelligence -- de novo design -- deep learning -- drug discovery -- peptide design; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.1002/cmdc.201800415
• Languages: English
• ISSNs: 1860-7179

Abstract

Abstract : The Front Cover illustrates the membranolytic action of an anticancer peptide (ACP) that was designed de novo by constructive machine learning. A recurrent neural network model was developed for pattern recognition in the sequences of α‐helical cationic amphipathic peptides and then fine‐tuned on known ACPs by transfer learning. Novel ACPs were automatically generated by this constructive network model. Twelve de novo 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, and six had at least threefold selectivity on human erythrocytes. Cover artwork by Dr. Francesca Grisoni. More information can be found in the Communication by Gisbert Schneider et al. onpage 1300 in Issue 13, 2018 (DOI:10.1002/cmdc.201800204 ).