Predictive Structure-Based Toxicology Approaches To
Assess the Androgenic Potential of Chemicals
Daniela Trisciuzzi
Domenico Alberga
Kamel Mansouri
Richard Judson
Ettore Novellino
Giuseppe Felice Mangiatordi
Orazio Nicolotti
10.1021/acs.jcim.7b00420.s001
https://acs.figshare.com/articles/journal_contribution/Predictive_Structure-Based_Toxicology_Approaches_To_Assess_the_Androgenic_Potential_of_Chemicals/5544202
We
present a practical and easy-to-run <i>in silico</i> workflow
exploiting a structure-based strategy making use of docking
simulations to derive highly predictive classification models of the
androgenic potential of chemicals. Models were trained on a high-quality
chemical collection comprising 1689 curated compounds made available
within the CoMPARA consortium from the US Environmental Protection
Agency and were integrated with a two-step applicability domain whose
implementation had the effect of improving both the confidence in
prediction and statistics by reducing the number of false negatives.
Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from
the Protein Data Bank) was associated with the best performing structure-based
classification model. Three validation sets comprising each 2590 compounds
extracted by the DUD-E collection were used to challenge model performance
and the effectiveness of Applicability Domain implementation. Next,
the 2PNU model
was applied to screen and prioritize two collections of chemicals.
The first is a small pool of 12 representative androgenic compounds
that were accurately classified based on outstanding rationale at
the molecular level. The second is a large external blind set of 55450
chemicals with potential for human exposure. We show how the use of
molecular docking provides highly interpretable models and can represent
a real-life option as an alternative nontesting method for predictive
toxicology.
2017-10-12 00:00:00
DUD-E collection
applicability domain
Applicability Domain implementation
androgen receptor X-ray
validation sets
docking simulations
chemical collection
entry code
Predictive Structure-Based Toxicology Approaches
classification models
silico workflow
crystal 2 PNU
structure-based strategy
structure-based classification model
Environmental Protection Agency
55450 chemicals
1689 curated compounds
CoMPARA consortium
12 representative androgenic compounds
alternative nontesting method
2590 compounds
2 PNU model
Protein Data Bank
challenge model performance