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Designing Anticancer Peptides by Constructive Machine Learning
Author(s) -
Grisoni Francesca,
Neuhaus Claudia S.,
Gabernet Gisela,
Müller Alex T.,
Hiss Jan A.,
Schneider Gisbert
Publication year - 2018
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201800204
Subject(s) - artificial intelligence , constructive , computer science , artificial neural network , peptide , generative grammar , computational biology , amino acid , deep learning , machine learning , chemistry , biochemistry , biology , process (computing) , programming language
Constructive (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.