Access count of this item: 15

Files in This Item:
File Description SizeFormat 
molecules24152716.pdf2.69 MBAdobe PDFView/Open
Title: Applicability domain of active learning in chemical probe identification: Convergence in learning from non-specific compounds and decision rule clarification
Authors: Polash, Ahsan Habib
Nakano, Takumi
Takeda, Shunichi
Brown, J. B.
Keywords: chemical probes
compound specificity
ligand-target interactions
chemogenomics
active learning
active projection
decision tree
molecular representation
Issue Date: 1-Aug-2019
Publisher: MDPI AG
Journal title: Molecules
Volume: 24
Issue: 15
Thesis number: 2716
Abstract: Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are being sought. The active learning virtual screening method has demonstrated the ability to efficiently converge on predictive models with reduced datasets, though its applicability domain to probe identification has yet to be determined. In this article, we challenge active learning’s ability to predict inhibitory bioactivity profiles of selective compounds when learning from chemogenomic features found in non-selective ligand-target pairs. Comparison of controls versus multiple molecule representations de-convolutes factors contributing to predictive capability. Experiments using the matrix metalloproteinase family demonstrate maximum probe bioactivity prediction achieved from only approximately 20% of non-probe bioactivity; this data volume is consistent with prior chemogenomic active learning studies despite the increased difficulty from chemical biology experimental settings used here. Feature weight analyses are combined with a custom visualization to unambiguously detail how active learning arrives at classification decisions, yielding clarified expectations for chemogenomic modeling. The results influence tactical decisions for computational probe design and discovery.
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
URI: http://hdl.handle.net/2433/243331
DOI(Published Version): 10.3390/molecules24152716
PubMed ID: 31357419
Appears in Collections:Journal Articles

Show full item record

Export to RefWorks


Export Format: 


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.