| ELECTRONIC ENGINEERING |
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Research progress on drug representation learning |
| CHEN Xin, LIU Xien, WU Ji |
| Department of Electronic Engineering, Tsinghua University, Beijing 100084, China |
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Abstract The drug development process is characterized by large capital density, high risk and long cycles; thus, drug development requires much capital, manpower and resources. While traditional machine learning methods can aid drug development some, they require molecular descriptors as inputs. The selection of the molecular descriptors then greatly impacts the performance of the machine learning models. Therefore, most traditional machine learning methods require complex and time-consuming feature engineering. The emerging deep learning methods can directly learn the features from raw representations of the drugs which bypasses the feature engineering and shortens the drug development cycle. In this paper, the drug representation learning methods are divided into simplified molecular input line entry specification (SMILES) expression based drug representation learning methods and molecular graph based representation learning methods. This paper then surveys the innovations and limitations of various drug representation learning methods. This paper then identifies major challenges in current drug representation learning methods and presents possible solutions.
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| Keywords
drug
representation learning
simplified molecular input line entry specification (SMILES)
molecular graph
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Issue Date: 15 January 2020
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2020,
Vol. 60
