The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds

Abstract

Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure – Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage‐gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on‐line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.

Ground truth explanation dataset for chemical property prediction on molecular graphs

“…A wide range of machine learning (ML) approaches allows for explaining the chemistry of molecules, attributing which parts of the molecules are responsible for the chemical property of interest [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] , and lessening the black box challenge of machine learning 20,21 . Typical explainable ML approaches that provide atomwise attribution include dummy atoms 22 , classification of atoms by chemical intuition 23 , regression models 24 , graph neural network (GNN) attributions [25][26][27][28] with gradients 29 , perturbations 30 , decompositions 31 , and surrogates 32 .…”

Ground truth explanation dataset for chemical property prediction on molecular graphs

“…A wide range of machine learning (ML) approaches allows for explaining the chemistry of molecules, attributing which parts of the molecules are responsible for the chemical property of interest [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] , and lessening the black box challenge of machine learning 20,21 . Typical explainable ML approaches that provide atomwise attribution include dummy atoms 22 , classification of atoms by chemical intuition 23 , regression models 24 , graph neural network (GNN) attributions [25][26][27][28] with gradients 29 , perturbations 30 , decompositions 31 , and surrogates 32 .…”

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

“…QSPR is a method for building machine learning models to mine the relationships between the properties and structure of a molecule. As a new field in the natural science, researches on QSPR include predicting the physicochemical behaviors and properties (boiling point [13], toxicity [14], heat capacity [15], sublimation enthalpy [16]) of a molecule based on structural information, identifying potential candidates with specified properties or functionalities [17]. Generally, a QSPR model uses numerical molecular structural characteristics as input, achieving efficient as well as accurate calculations and predictions of molecular properties based on various machine learning algorithms [17][18][19].…”