title: MOST: most-similar ligand based approach to target prediction.
abstract: BACKGROUND
  Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power.
  
  
  RESULTS
  Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction.
  
  
  CONCLUSIONS
  Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.|Department of Gastroenterology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, People's Republic of China.
authors.forename: Hong
authors.initials: H
authors.lastname: Mi
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.|YMU-HKBU Joint Laboratory of Traditional Natural Medicine, Yunnan Minzu University, Kunming, 650500, People's Republic of China.
authors.forename: Cheng-Yuan
authors.initials: CY
authors.lastname: Lin
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
authors.forename: Ling
authors.initials: L
authors.lastname: Zhao
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.|Hong Kong Chinese Medicine Clinical Study Centre, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
authors.forename: Linda L D
authors.initials: LL
authors.lastname: Zhong
authors.affiliation: Department of Gastroenterology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, People's Republic of China.
authors.forename: Feng-Bin
authors.initials: FB
authors.lastname: Liu
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
authors.forename: Ge
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.|Hong Kong Chinese Medicine Clinical Study Centre, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China.
authors.forename: Ai-Ping
authors.initials: AP
authors.lastname: Lu
authors.affiliation: Lab of Brain and Gut Research, School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China. bianzxiang@gmail.com.|Hong Kong Chinese Medicine Clinical Study Centre, Hong Kong Baptist University, 7 Baptist University Road, Hong Kong, People's Republic of China. bianzxiang@gmail.com.
authors.forename: Zhao-Xiang
authors.identifier: http://orcid.org/0000-0002-3130-8417
authors.initials: ZX
authors.lastname: Bian
mesh_terms: D000110:Acetylcholinesterase / Q000737:chemistry / Q000378:metabolism; D000504:Aloe / Q000737:chemistry / Q000378:metabolism; D000818:Animals; D002400:Cathartics / Q000737:chemistry / Q000378:metabolism; D062126:Databases, Chemical; D004642:Emodin / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D007700:Kinetics; D008024:Ligands; D016015:Logistic Models; D000069550:Machine Learning
chemical_list: D002400:Cathartics; D008024:Ligands; D000110:Acetylcholinesterase; D004642:Emodin
keywords: Explicit bioactivity; False discovery rate; Logistic regression; Mechanism-of-action target; Most-similar ligand; Target prediction