Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

Quantitative structure-activity relationship model development for estimating the predicted No-effect concentration of petroleum hydrocarbon and derivatives in the ecological risk assessment

“…The autocorrelation descriptors that affected the PNECs of PHDs were mainly the mass (m), polarizability (p), van der Waals volume (v), first ionization potential (i), and state (s) weighting of Broto-Moreau (AST), Geary (GAT), and Moran (MATS) descriptors. The three autocorrelation descriptors characterized the structural conformation of chemical molecules [ 49 , 63 ], which has been found to be highly relevant to the aquatic toxicity of cosmetics and personal care additives [ 49 ]. These autocorrelation descriptors may affect the PNECs and toxicity by influencing the spatial conformation of PHDs.…”

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

“…They have stated that the q-RASAR model supersedes the QSAR model in terms of its overall performance as evident from the reported validation metrics. Yang et al 33 worked on the prediction of toxicity of organic chemicals against Gammarus species using QSAR and q-RASAR modeling algorithms, and they have reported that both these approaches have similar statistical performances. Sun et al 34 applied q-RASAR for the identification of structural features for the inhibition of O 6 -methylguanine-DNA methyltransferase with an objective to improve clinical outcomes in cancer treatment.…”