PMID- 28155089
OWN - NLM
STAT- MEDLINE
DCOM- 20170614
LR  - 20181113
IS  - 1573-4951 (Electronic)
IS  - 0920-654X (Linking)
VI  - 31
IP  - 4
DP  - 2017 Apr
TI  - Predicting DPP-IV inhibitors with machine learning approaches.
PG  - 393-402
LID - 10.1007/s10822-017-0009-6 [doi]
AB  - Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) 
      drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 
      (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without 
      weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors 
      have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, 
      skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV 
      inhibitors with minimal adverse effects. The scaffolds of existing DPP-IV 
      inhibitors are structurally diversified. This makes it difficult to build virtual 
      screening models based upon the known DPP-IV inhibitor libraries using 
      conventional QSAR approaches. In this paper, we report a new strategy to predict 
      DPP-IV inhibitors with machine learning approaches involving naive Bayesian (NB) 
      and recursive partitioning (RP) methods. We built 247 machine learning models 
      based on 1307 known DPP-IV inhibitors with optimized molecular properties and 
      topological fingerprints as descriptors. The overall predictive accuracies of the 
      optimized models were greater than 80%. An external test set, composed of 65 
      recently reported compounds, was employed to validate the optimized models. The 
      results demonstrated that both NB and RP models have a good predictive ability 
      based on different combinations of descriptors. Twenty "good" and twenty "bad" 
      structural fragments for DPP-IV inhibitors can also be derived from these models 
      for inspiring the new DPP-IV inhibitor scaffold design.
FAU - Cai, Jie
AU  - Cai J
AD  - Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun 
      Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
FAU - Li, Chanjuan
AU  - Li C
AD  - Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun 
      Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
FAU - Liu, Zhihong
AU  - Liu Z
AD  - Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun 
      Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
FAU - Du, Jiewen
AU  - Du J
AD  - Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun 
      Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
FAU - Ye, Jiming
AU  - Ye J
AD  - Lipid Biology and Metabolic Disease Health Innovations Research Institute, RMIT 
      University, PO Box 71, Melbourne, VIC, 3083, Australia.
FAU - Gu, Qiong
AU  - Gu Q
AD  - Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun 
      Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
FAU - Xu, Jun
AU  - Xu J
AUID- ORCID: 0000-0002-1075-0337
AD  - Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun 
      Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China. 
      junxu@biochemomes.com.
LA  - eng
PT  - Journal Article
DEP - 20170202
PL  - Netherlands
TA  - J Comput Aided Mol Des
JT  - Journal of computer-aided molecular design
JID - 8710425
RN  - 0 (Dipeptidyl-Peptidase IV Inhibitors)
RN  - 0 (Small Molecule Libraries)
RN  - EC 3.4.14.5 (Dipeptidyl Peptidase 4)
SB  - IM
MH  - Bayes Theorem
MH  - *Computer-Aided Design
MH  - Diabetes Mellitus, Type 2/drug therapy
MH  - Dipeptidyl Peptidase 4/chemistry/metabolism
MH  - Dipeptidyl-Peptidase IV Inhibitors/*chemistry/*pharmacology
MH  - *Drug Design
MH  - Humans
MH  - *Machine Learning
MH  - Molecular Docking Simulation
MH  - Small Molecule Libraries/chemistry/pharmacology
OTO - NOTNLM
OT  - Cheminformatics
OT  - DPP-IV
OT  - Naive Bayesian learning
OT  - Recursive partitioning
OT  - Virtual screening
EDAT- 2017/02/06 06:00
MHDA- 2017/06/15 06:00
CRDT- 2017/02/04 06:00
PHST- 2016/09/30 00:00 [received]
PHST- 2017/01/04 00:00 [accepted]
PHST- 2017/02/06 06:00 [pubmed]
PHST- 2017/06/15 06:00 [medline]
PHST- 2017/02/04 06:00 [entrez]
AID - 10.1007/s10822-017-0009-6 [pii]
AID - 10.1007/s10822-017-0009-6 [doi]
PST - ppublish
SO  - J Comput Aided Mol Des. 2017 Apr;31(4):393-402. doi: 10.1007/s10822-017-0009-6. 
      Epub 2017 Feb 2.