Designing Anticancer Peptides by Constructive Machine Learning

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...

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Published in:	ChemMedChem Vol. 13; no. 13; pp. 1300 - 1302
Main Authors:	Grisoni, Francesca, Neuhaus, Claudia S., Gabernet, Gisela, Müller, Alex T., Hiss, Jan A., Schneider, Gisbert
Format:	Journal Article
Language:	English
Published:	WEINHEIM Wiley 06.07.2018 Wiley Subscription Services, Inc
Subjects:	Adenocarcinoma Amino acids Artificial intelligence Automation Cancer Chemistry, Medicinal Computer memory de novo design deep learning Design drug discovery Erythrocytes Learning algorithms Life Sciences & Biomedicine Machine learning Memory cells Molecular chains Neural networks Organic chemistry peptide design Peptides Pharmacology & Pharmacy Recurrent neural networks Science & Technology Selectivity Transfer learning peptide design de novo design deep learning NK-LYSIN drug discovery artificial intelligence
ISSN:	1860-7179, 1860-7187, 1860-7187
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Abstract	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. Deep learning: We report the experimental application of deep machine learning for the automated design and generation of novel membranolytic anticancer peptides. This technique, which avoids the need for explicit molecular design rules, has proven applicable to automated peptide design in a prospective setting without having to synthesize and test large sets of peptides.
AbstractList	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.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. 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. Deep learning: We report the experimental application of deep machine learning for the automated design and generation of novel membranolytic anticancer peptides. This technique, which avoids the need for explicit molecular design rules, has proven applicable to automated peptide design in a prospective setting without having to synthesize and test large sets of peptides. 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 -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. 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.
Author	Schneider, Gisbert Gabernet, Gisela Hiss, Jan A. Müller, Alex T. Grisoni, Francesca Neuhaus, Claudia S.
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Keywords	peptide design de novo design deep learning NK-LYSIN drug discovery artificial intelligence
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Snippet	Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules....
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SubjectTerms	Adenocarcinoma Amino acids Artificial intelligence Automation Cancer Chemistry, Medicinal Computer memory de novo design deep learning Design drug discovery Erythrocytes Learning algorithms Life Sciences & Biomedicine Machine learning Memory cells Molecular chains Neural networks Organic chemistry peptide design Peptides Pharmacology & Pharmacy Recurrent neural networks Science & Technology Selectivity Transfer learning
Title	Designing Anticancer Peptides by Constructive Machine Learning
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