PMID- 31622216
OWN - NLM
STAT- MEDLINE
DCOM- 20200901
LR  - 20200901
IS  - 1875-5402 (Electronic)
IS  - 1386-2073 (Linking)
VI  - 22
IP  - 8
DP  - 2019
TI  - A Random Forest Model to Predict the Activity of a Large Set of Soluble Epoxide 
      Hydrolase Inhibitors Solely Based on a Set of Simple Fragmental Descriptors.
PG  - 555-569
LID - 10.2174/1386207322666191016110232 [doi]
AB  - BACKGROUND: The Soluble Epoxide Hydrolase (sEH) is a ubiquitously expressed 
      enzyme in various tissues. The inhibition of the sEH has shown promising results 
      to treat hypertension, alleviate pain and inflammation. OBJECTIVE: In this study, 
      the power of machine learning has been employed to develop a predictive QSAR 
      model for a large set of sEH inhibitors. METHODS: In this study, the random 
      forest method was employed to make a valid model for the prediction of sEH 
      inhibition. Besides, two new methods (Treeinterpreter python package and LIME, 
      Local Interpretable Model-agnostic Explanations) have been exploited to explain 
      and interpret the model. RESULTS: The performance metrics of the model were as 
      follows: R2=0.831, Q2=0.565, RMSE=0.552 and R2 pred=0.595. The model also 
      demonstrated good predictability on the two extra external test sets at least in 
      terms of ranking. The Spearman's rank correlation coefficients for external test 
      set 1 and 2 were 0.872 and 0.673, respectively. The external test set 2 was a 
      diverse one compared to the training set. Therefore, the model could be used for 
      virtual screening to enrich potential sEH inhibitors among a diverse compound 
      library. CONCLUSION: As the model was solely developed based on a set of simple 
      fragmental descriptors, the model was explained by two local interpretation 
      algorithms, and this could guide medicinal chemists to design new sEH inhibitors. 
      Moreover, the most important general descriptors (fragments) suggested by the 
      model were consistent with the available crystallographic data. The model is 
      available as an executable binary at http://www.pharm-sbg.com and 
      https://github.com/shamsaraj.
CI  - Copyright(c) Bentham Science Publishers; For any queries, please email at 
      epub@benthamscience.net.
FAU - Shamsara, Jamal
AU  - Shamsara J
AD  - Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad 
      University of Medical Sciences, Mashhad, Iran.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United Arab Emirates
TA  - Comb Chem High Throughput Screen
JT  - Combinatorial chemistry & high throughput screening
JID - 9810948
RN  - 0 (Enzyme Inhibitors)
RN  - EC 3.3.2.- (Epoxide Hydrolases)
SB  - IM
MH  - *Algorithms
MH  - Databases, Factual
MH  - Enzyme Inhibitors/*chemistry/pharmacology
MH  - Epoxide Hydrolases/antagonists & inhibitors/metabolism
MH  - High-Throughput Screening Assays
MH  - Machine Learning
MH  - Models, Molecular
MH  - Molecular Structure
MH  - Quantitative Structure-Activity Relationship
MH  - Solubility
OTO - NOTNLM
OT  - Cheminformatics
OT  - QSAR
OT  - machine learning
OT  - random forest
OT  - soluble epoxide hydrolase
OT  - virtual screening.
EDAT- 2019/10/18 06:00
MHDA- 2020/09/02 06:00
CRDT- 2019/10/18 06:00
PHST- 2019/07/08 00:00 [received]
PHST- 2019/08/02 00:00 [revised]
PHST- 2019/09/19 00:00 [accepted]
PHST- 2019/10/18 06:00 [pubmed]
PHST- 2020/09/02 06:00 [medline]
PHST- 2019/10/18 06:00 [entrez]
AID - CCHTS-EPUB-101514 [pii]
AID - 10.2174/1386207322666191016110232 [doi]
PST - ppublish
SO  - Comb Chem High Throughput Screen. 2019;22(8):555-569. doi: 
      10.2174/1386207322666191016110232.