Cargando…
Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification
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 screeni...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696588/ https://www.ncbi.nlm.nih.gov/pubmed/31357419 http://dx.doi.org/10.3390/molecules24152716 |
_version_ | 1783444288349143040 |
---|---|
author | Polash, Ahsan Habib Nakano, Takumi Takeda, Shunichi Brown, J.B. |
author_facet | Polash, Ahsan Habib Nakano, Takumi Takeda, Shunichi Brown, J.B. |
author_sort | Polash, Ahsan Habib |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6696588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66965882019-09-05 Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification Polash, Ahsan Habib Nakano, Takumi Takeda, Shunichi Brown, J.B. Molecules Article 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. MDPI 2019-07-26 /pmc/articles/PMC6696588/ /pubmed/31357419 http://dx.doi.org/10.3390/molecules24152716 Text en © 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/). |
spellingShingle | Article Polash, Ahsan Habib Nakano, Takumi Takeda, Shunichi Brown, J.B. Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification |
title | Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification |
title_full | Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification |
title_fullStr | Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification |
title_full_unstemmed | Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification |
title_short | Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification |
title_sort | applicability domain of active learning in chemical probe identification: convergence in learning from non-specific compounds and decision rule clarification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696588/ https://www.ncbi.nlm.nih.gov/pubmed/31357419 http://dx.doi.org/10.3390/molecules24152716 |
work_keys_str_mv | AT polashahsanhabib applicabilitydomainofactivelearninginchemicalprobeidentificationconvergenceinlearningfromnonspecificcompoundsanddecisionruleclarification AT nakanotakumi applicabilitydomainofactivelearninginchemicalprobeidentificationconvergenceinlearningfromnonspecificcompoundsanddecisionruleclarification AT takedashunichi applicabilitydomainofactivelearninginchemicalprobeidentificationconvergenceinlearningfromnonspecificcompoundsanddecisionruleclarification AT brownjb applicabilitydomainofactivelearninginchemicalprobeidentificationconvergenceinlearningfromnonspecificcompoundsanddecisionruleclarification |