| Revision as of 10:27, 20 November 2024 edit Kunalroyindia (talk | contribs)52 edits ←Created page with 'One of the most commonly used in silico approaches for assessing new molecules' activity/property/toxicity is the Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR), which generates predictive models for efficiently predicting query compounds . QSAR uses chemical information in the form of molecular descriptors and correlates these to the response using statistical techniques . While QSAR is essentially a similarity-based...'Tag: large unwikified new articleNext edit → |
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Revision as of 10:27, 20 November 2024
One of the most commonly used in silico approaches for assessing new molecules' activity/property/toxicity is the Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR), which generates predictive models for efficiently predicting query compounds . QSAR uses chemical information in the form of molecular descriptors and correlates these to the response using statistical techniques . While QSAR is essentially a similarity-based approach, the occurrence of activity/property cliffs may greatly reduce the predictive accuracy of the developed models . The novel Arithmetic Residuals in K-groups Analysis (ARKA) approach is a supervised dimensionality reduction technique that can easily identify activity cliffs in a data set . ARKA descriptors have also been used to develop classification-based and regression-based QSAR model development.
References Muratov et al., QSAR without borders. Chem. Soc. Rev., 2020,49, 3525-3564, https://doi.org/10.1039/D0CS00098A Cherkasov et al., QSAR Modeling: Where Have You Been? Where Are You Going To? J. Med. Chem. 2014, 57, 12, 4977–5010. https://doi.org/10.1021/jm4004285https://pubs.acs.org/doi/10.1021/jm4004285 Dablander, M., Hanser, T., Lambiotte, R. et al. Exploring QSAR models for activity-cliff prediction. J Cheminform 15, 47 (2023). https://doi.org/10.1186/s13321-023-00708-w Qin et al., Classification and Regression Machine Learning Models for Predicting the Combined Toxicity and Interactions of Antibiotics and Fungicides Mixtures. Environmental Pollution, Volume 360, 1 November 2024, 124565, https://doi.org/10.1016/j.envpol.2024.124565 Banerjee A, Roy K, ARKA: A framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data. Environ Sci: Process Impacts, 2024, https://doi.org/10.1039/D4EM00173G Sobanska et al., Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions—In Silico Studies of Drug-Likeness and Human Placental Transport. Int. J. Mol. Sci. 25(22), 12373 (2024), https://doi.org/10.3390/ijms252212373