Machine learning in chemoinformatics and drug discovery
•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint and similarity analysis.•Machine learning models for virtual screening.•Future challenges and direction in machine-learning-based drug discovery. Chemoinformatics is an established discipline focusing on extracting, proce...
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| Published in | Drug discovery today Vol. 23; no. 8; pp. 1538 - 1546 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
01.08.2018
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| Subjects | |
| Online Access | Get full text |
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| Abstract | •Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint and similarity analysis.•Machine learning models for virtual screening.•Future challenges and direction in machine-learning-based drug discovery.
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical ‘big’ data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field. |
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| AbstractList | Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical ‘big’ data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field. •Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint and similarity analysis.•Machine learning models for virtual screening.•Future challenges and direction in machine-learning-based drug discovery. Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical ‘big’ data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field. Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field. |
| Author | Rensi, Stefano E. Lo, Yu-Chen Altman, Russ B. Torng, Wen |
| Author_xml | – sequence: 1 givenname: Yu-Chen surname: Lo fullname: Lo, Yu-Chen – sequence: 2 givenname: Stefano E. surname: Rensi fullname: Rensi, Stefano E. – sequence: 3 givenname: Wen surname: Torng fullname: Torng, Wen – sequence: 4 givenname: Russ B. orcidid: 0000-0003-3859-2905 surname: Altman fullname: Altman, Russ B. email: rbaltman@stanford.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29750902$$D View this record in MEDLINE/PubMed |
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| Snippet | •Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint and similarity analysis.•Machine learning models for virtual screening.•Future... Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid... |
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| SubjectTerms | Animals Diffusion of Innovation Drug Discovery - methods High-Throughput Screening Assays Humans Informatics Machine Learning Molecular Structure Pattern Recognition, Automated Pharmaceutical Preparations - chemistry Quantitative Structure-Activity Relationship |
| Title | Machine learning in chemoinformatics and drug discovery |
| URI | https://dx.doi.org/10.1016/j.drudis.2018.05.010 https://www.ncbi.nlm.nih.gov/pubmed/29750902 https://www.proquest.com/docview/2038273609 https://pubmed.ncbi.nlm.nih.gov/PMC6078794 |
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