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

Abstract

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.

Alignment-independent technique for 3D QSAR analysis

“…In the last 2 years our group has been using this rather stringent 20 %-Hold-Out and 100 random partitions validation process as standard procedure for quality assurance [ 1 , 3 , 4 , 22 ]. This standard has recently been evaluated by others and confirmed as both necessary and appropriate for estimating QSAR model predictive accuracy of external data [ 21 ].…”

“…We built and modeled 2D > 3D conformations for 130 estrogens and 154 acute toxicity compounds previously modeled by 3D-QSDAR [ 1 , 4 ]. Predictive accuracy, R Test 2 , was compared for these endpoints to estimate the circumstances under which one might effectively take advantage of the 2D > 3D shortcut and save 93–97 % of modeling time.…”

“…In this project, information about the presence of atoms other than carbon was not explicitly included. We have found in several of our previous SDAR modeling projects that the high sensitivity of 13 C δs to their environment is often sufficient for useful reflection of chemical structure in the vicinity, including the presence of nearby heteroatoms [ 1 , 3 , 4 ]. A tessellation of the 3D-SDAR space into regular grids (“binning”) is further used to convert the information contained in a fingerprint into a set of 3D-SDAR descriptors.…”

“…Our earlier studies indicated that 3D-QSDAR models based on lowest energy conformations perform well [ 1 , 3 , 4 ]. However, we hypothesized that use of substrates internally aligned with respect to molecular template molecules rather than energy-minimized conformations might prove beneficial.…”

Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna

“…The assumption is that the strengths of one model will counterbalance the weaknesses of the others and vice versa. Previous studies have already shown the beneficial effects of consensus modelling when dealing with ecotoxicological endpoints [39][40][41][42].…”