Search: onr:"swepub:oai:DiVA.org:oru-86638" >
Predicting Chemical...
-
Chavan, SwapnilSchool of Science and Technology, Örebro University, Örebro, Sweden
(author)
Predicting Chemical-Induced Liver Toxicity Using High-Content Imaging Phenotypes and Chemical Descriptors : A Random Forest Approach
- Article/chapterEnglish2020
Publisher, publication year, extent ...
-
2020-08-24
-
American Chemical Society (ACS),2020
-
printrdacarrier
Numbers
-
LIBRIS-ID:oai:DiVA.org:oru-86638
-
https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-86638URI
-
https://doi.org/10.1021/acs.chemrestox.9b00459DOI
Supplementary language notes
-
Language:English
-
Summary in:English
Part of subdatabase
Classification
-
Subject category:ref swepub-contenttype
-
Subject category:art swepub-publicationtype
Notes
-
Funding Agencies:EnForce project (Environmental Forensic Laboratory)Örebro University, Sweden
-
Hepatotoxicity is a major reason for the withdrawal or discontinuation of drugs from clinical trials. Thus, better tools are needed to filter potential hepatotoxic drugs early in drug discovery. Our study demonstrates utilization of HCI phenotypes, chemical descriptors, and both combined (hybrid) descriptors to construct random forest classifiers (RFCs) for the prediction of hepatotoxicity. HCI data published by Broad Institute provided HCI phenotypes for about 30 000 samples in multiple replicates. Phenotypes belonging to 346 chemicals, which were tested in up to eight replicates, were chosen as a basis for our analysis. We then constructed individual RFC models for HCI phenotypes, chemical descriptors, and hybrid (chemical and HCI) descriptors. The model that was constructed using selective hybrid descriptors showed high predictive performance with 5-fold cross validation (CV) balanced accuracy (BA) at 0.71, whereas within the given applicability domain (AD), independent test set and external test set prediction BAs were equal to 0.61 and 0.60, respectively. The model constructed using chemical descriptors showed a similar predictive performance with a 5-fold CV BA equal to 0.66, a test set prediction BA within the AD equal to 0.56, and an external test set prediction BA within the AD equal to 0.50. In conclusion, the hybrid and chemical descriptor-based models presented here should be considered as a new tool for filtering hepatotoxic molecules during compound prioritization in drug discovery.
Subject headings and genre
Added entries (persons, corporate bodies, meetings, titles ...)
-
Scherbak, Nikolai,1967-Örebro universitet,Institutionen för naturvetenskap och teknik(Swepub:oru)nsk
(author)
-
Engwall, Magnus,1965-Örebro universitet,Institutionen för naturvetenskap och teknik(Swepub:oru)mel
(author)
-
Repsilber, Dirk,1971-Örebro universitet,Institutionen för medicinska vetenskaper(Swepub:oru)drr
(author)
-
School of Science and Technology, Örebro University, Örebro, SwedenInstitutionen för naturvetenskap och teknik
(creator_code:org_t)
Related titles
-
In:Chemical Research in Toxicology: American Chemical Society (ACS)33:9, s. 2261-22750893-228X1520-5010
Internet link
Find in a library
To the university's database