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Scalable Prediction of Compound‐protein Interaction on Compressed Molecular Fingerprints
Author(s) -
Tabei Yasuo
Publication year - 2020
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201900130
Subject(s) - computer science , lossy compression , lossless compression , scalability , fingerprint (computing) , data mining , kernel (algebra) , artificial intelligence , pattern recognition (psychology) , machine learning , data compression , mathematics , combinatorics , database
Prediction of compound‐protein interactions with fingerprints has recently become challenging in recent pharmaceutical science for an efficient drug discovery. We review two scalable methods for predicting drug‐protein interactions on fingerprints. Especially, we introduce two techniques of learning statistical models using lossless and lossy data compressions. The first one is a method using a trie representation of fingerprints which enables us to learn predictive models on the compressed format. The second one is a method using lossy data compression called feature maps (FMs). Recently, quite a few numbers of FMs for kernel approximations have been proposed and minwise hashing, one method of this kind. has been applied to predictions of compound‐protein interactions and shows an effectiveness of the method. Overall, we show learning statistical models on the compressed format is effective for predicting compound‐protein interactions on a large‐scale.