title: Discovery of multitarget-directed ligands against Alzheimer's disease through systematic prediction of chemical-protein interactions.
abstract: To determine chemical-protein interactions (CPI) is costly, time-consuming, and labor-intensive. In silico prediction of CPI can facilitate the target identification and drug discovery. Although many in silico target prediction tools have been developed, few of them could predict active molecules against multitarget for a single disease. In this investigation, naive Bayesian (NB) and recursive partitioning (RP) algorithms were applied to construct classifiers for predicting the active molecules against 25 key targets toward Alzheimer's disease (AD) using the multitarget-quantitative structure-activity relationships (mt-QSAR) method. Each molecule was initially represented with two kinds of fingerprint descriptors (ECFP6 and MACCS). One hundred classifiers were constructed, and their performance was evaluated and verified with internally 5-fold cross-validation and external test set validation. The range of the area under the receiver operating characteristic curve (ROC) for the test sets was from 0.741 to 1.0, with an average of 0.965. In addition, the important fragments for multitarget against AD given by NB classifiers were also analyzed. Finally, the validated models were employed to systematically predict the potential targets for six approved anti-AD drugs and 19 known active compounds related to AD. The prediction results were confirmed by reported bioactivity data and our in vitro experimental validation, resulting in several multitarget-directed ligands (MTDLs) against AD, including seven acetylcholinesterase (AChE) inhibitors ranging from 0.442 to 72.26 μM and four histamine receptor 3 (H3R) antagonists ranging from 0.308 to 58.6 μM. To be exciting, the best MTDL DL0410 was identified as an dual cholinesterase inhibitor with IC50 values of 0.442 μM (AChE) and 3.57 μM (BuChE) as well as a H3R antagonist with an IC50 of 0.308 μM. This investigation is the first report using mt-QASR approach to predict chemical-protein interaction for a single disease and discovering highly potent MTDLs. This protocol may be useful for in silico multitarget prediction of other diseases.
authors.affiliation: Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050, PR China.
authors.forename: Jiansong
authors.initials: J
authors.lastname: Fang
authors.forename: Yongjie
authors.initials: Y
authors.lastname: Li
authors.forename: Rui
authors.initials: R
authors.lastname: Liu
authors.forename: Xiaocong
authors.initials: X
authors.lastname: Pang
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.forename: Ranyao
authors.initials: R
authors.lastname: Yang
authors.forename: Yangyang
authors.initials: Y
authors.lastname: He
authors.forename: Wenwen
authors.initials: W
authors.lastname: Lian
authors.forename: Ai-Lin
authors.initials: AL
authors.lastname: Liu
authors.forename: Guan-Hua
authors.initials: GH
authors.lastname: Du
mesh_terms: D000544:Alzheimer Disease / Q000188:drug therapy*; D000818:Animals; D001499:Bayes Theorem; D002800:Cholinesterase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D051360:Cyclin-Dependent Kinase 5 / Q000037:antagonists & inhibitors; D055808:Drug Discovery / Q000379:methods*; D004353:Drug Evaluation, Preclinical / Q000379:methods; D054828:Histamine H3 Antagonists / Q000737:chemistry / Q000494:pharmacology; D006801:Humans; D008024:Ligands; D058990:Molecular Targeted Therapy; D021281:Quantitative Structure-Activity Relationship; D012372:ROC Curve; D051381:Rats; D015203:Reproducibility of Results
chemical_list: D002800:Cholinesterase Inhibitors; D054828:Histamine H3 Antagonists; D008024:Ligands; D051360:Cyclin-Dependent Kinase 5; C495905:CDK5 protein, human