Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction.

The estrogenic potential (expressed as a score composite of 18 high throughput screening bioassays) of 1528 compounds from the ToxCast database was modeled by a 3-dimensional spectral data activity relationship approach (3D-SDAR). Due to a lack of 17 O nuclear magnetic resonance (NMR) simulation software, the most informative carbon-carbon 3D-SDAR fingerprints were augmented with indicator variables representing oxygen atoms from carbonyl and carboxamide, ester, sulfonyl, nitro, aliphatic hydroxyl, and phenolic hydroxyl groups. To evaluate the true predictive performance of the authors' model the United States Environmental Protection Agency provided them with a blind test set consisting of 2008 compounds. Of these, 543 had available literature data-their binding affinity served to estimate the external classification accuracy of the developed model: predictive accuracy of 0.62, sensitivity of 0.71, and specificity of 0.53 were obtained. Compared with alternative modeling techniques, the authors' model displayed very little reduction in performance between the modeling and the prediction set. A 3D-SDAR mapping technique allowed identification of structural features essential for estrogenicity: 1) the presence of a phenolic OH group or cyclohexenone, 2) a second aromatic or phenolic ring at a distance of 6 Što 8 Šfrom the oxygen of the first phenol ring, 3) the presence of a methyl group approximately 6 Šaway from the centroid of a phenol ring, and 4) a carbonyl group in close proximity (∼4 Šmeasured to the centroid) to 1 of the phenol rings. Environ Toxicol Chem 2017;36:823-830. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks.

BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. RESULTS: An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. CONCLUSION: A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.

Alignment-independent technique for 3D QSAR analysis.

Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test (2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test (2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test (2) = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.

Computational identification of a phospholipidosis toxicophore using (13)C and (15)N NMR-distance based fingerprints.

Modified 3D-SDAR fingerprints combining (13)C and (15)N NMR chemical shifts augmented with inter-atomic distances were used to model the potential of chemicals to induce phospholipidosis (PLD). A curated dataset of 328 compounds (some of which were cationic amphiphilic drugs) was used to generate 3D-QSDAR models based on tessellations of the 3D-SDAR space with grids of different density. Composite PLS models averaging the aggregated predictions from 100 fully randomized individual models were generated. On each of the 100 runs, the activities of an external blind test set comprised of 294 proprietary chemicals were predicted and averaged to provide composite estimates of their PLD-inducing potentials (PLD+ if PLD is observed, otherwise PLD-). The best performing 3D-QSDAR model utilized a grid with a density of 8ppm×8ppm in the C-C region, 8ppm×20ppm in the C-N region and 20ppm×20ppm in the N-N region. The classification predictive performance parameters of this model evaluated on the basis of the external test set were as follows: accuracy=0.70, sensitivity=0.73 and specificity=0.66. A projection of the most frequently occurring bins on the standard coordinate space suggested a toxicophore composed of an aromatic ring with a centroid 3.5-7.5Å distant from an amino-group. The presence of a second aromatic ring separated by a 4-5Å spacer from the first ring and at a distance of between 5.5Å and 7Å from the amino-group was also associated with a PLD+ effect. These models provide comparable predictive performance to previously reported models for PLD with the added benefit of being based entirely on non-confidential, publicly available training data and with good predictive performance when tested in a rigorous, external validation exercise.

Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data-activity relationship consensus modeling of acute toxicity.

A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test = 0.801) offered an improvement of about 7.5% (R(2) test consensus = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.