Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 474, P. 134865 - 134865

Published: June 12, 2024

Language: Английский

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Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure–activity relationship (q-RASAR) with the application of machine learning

Critical Reviews in Toxicology, Journal Year: 2024, Volume and Issue: 54(9), P. 659 - 684

Published: Sept. 3, 2024

This article aims to provide a comprehensive critical, yet readable, review of general interest the chemistry community on molecular similarity as applied chemical informatics and predictive modeling with special focus read-across (RA) structure-activity relationships (RASAR). Molecular similarity-based computational tools, such quantitative (QSARs) RA, are routinely used fill data gaps for wide range properties including toxicity endpoints regulatory purposes. will explore background RA starting from how structural information has been through other contexts physicochemical, absorption, distribution, metabolism, elimination (ADME) properties, biological aspects being characterized. More recent developments RA's integration QSAR have resulted in emergence novel models ToxRead, generalized (GenRA), RASAR (q-RASAR). Conventional techniques excluded this except where necessary context.

Language: Английский

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How to correctly develop q-RASAR models for predictive cheminformatics

Expert Opinion on Drug Discovery, Journal Year: 2024, Volume and Issue: 19(9), P. 1017 - 1022

Published: July 5, 2024

Language: Английский

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Accurate Prediction of Rat Acute Oral Toxicity and Reference Dose for Thousands of Polycyclic Aromatic Hydrocarbon Derivatives Based on Chemometric QSAR and Machine Learning

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 13, 2024

Acute oral toxicity is currently not available for most polycyclic aromatic hydrocarbons (PAHs), especially their derivatives, because it cost-prohibitive to experimentally determine all of them. Here, quantitative structure–activity relationship (QSAR) models using machine learning (ML) predicting the PAH derivatives were developed, based on data points 788 individual substances rats. Both ML algorithm gradient boosting regression trees (GBRT) and stacking (extreme + GBRT random forest regression) provided best prediction results with satisfactory determination coefficients both cross-validation test set. It was found that those fewer polar hydrogens, more large-sized atoms, branches, lower polarizability have higher toxicity. Software optimal ML-QSAR model successfully developed expand application potential model, obtaining reliable pLD50 values reference doses 6893 external derivatives. Among these chemicals, 472 identified as moderately or highly toxic; 10 out them had clear environment detection use records. The findings provide valuable insights into PAHs offering a standard platform effectively evaluating chemical models.

Language: Английский

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Tracking the biogeochemical behavior of tire wear particles in the environment – A review

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 480, P. 136184 - 136184

Published: Oct. 17, 2024

Language: Английский

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Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 4, 2025

We have adopted the classification Read-Across Structure–Activity Relationship (c-RASAR) approach in present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential orally active drugs. initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" subsequent sections manuscript) and MACCS fingerprints "fingerprints" manuscript), thus generating 18 QSAR models. Using chemical spaces defined by modeling fingerprints, similarity error-based RASAR were computed, most discriminating used develop another set c-RASAR All 36 cross-validated 20 times with fivefold cross-validation strategy, their predictivity was checked test data. A multi-criteria decision-making strategy – Sum Ranking Differences (SRD) approach—was identify best-performing based robustness external validation parameters. This statistical analysis suggested that had an overall good performance, while also (LDA derived descriptors, MCC values 0.229 0.431 training sets, respectively). screen true data prepared known nephrotoxic compounds DrugBankDB, demonstrating predictivity.

Language: Английский

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Degradation of the novel herbicide tiafenacil in aqueous solution: Kinetics, various influencing factors, hydrolysis products identification, and toxicity assessment

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 952, P. 175937 - 175937

Published: Aug. 30, 2024

Language: Английский

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Machine learning-assisted c-RASAR modeling of a curated set of orally active nephrotoxic drugs: Similarity-based predictions from close source neighbors

Published: Aug. 22, 2024

Cheminformatics and Machine Learning (ML) have seen exponential progress in the last decade, field of chemical risk assessment, due to their efficiency, accuracy, reliability. The constant evolution New Approach Methodologies (NAM) has inspired researchers around globe deviate from conventional approaches adopt or develop new, “unconventional” methods. classification Read-Across Structure-Activity Relationship (c-RASAR) is an unconventional approach that utilizes similarity error-based information nearest neighboring compounds into a modeling framework, resulting enhanced predictivity. Although this technique so far been applied molecular descriptors, we present study on fingerprints along with descriptors for ML-based model development recently reported highly curated set orally active nephrotoxic drugs. We initially developed ML models using nine different linear non-linear algorithms separately MACCS fingerprints, thus generating 18 QSAR models. Using spaces defined by RASAR were computed, most discriminating used another c-RASAR All 36 cross-validated 20 times 5-fold cross-validation strategy, predictivity was checked test data. A multi-criteria decision-making strategy – Sum Ranking Differences (SRD) - adopted identify best-performing based robustness external validation parameters. This statistical analysis suggested had overall good performance, while also model. screen true data prepared known DrugBankDB. These results showed our efficiently identifies compounds. t-SNE analyses descriptor inferred encode information, as evident tight distinct clustering points. Additionally, corresponding potential activity cliffs ARKA framework.

Language: Английский

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Toxicological mechanisms and molecular impacts of tire particles and antibiotics on zebrafish

Environmental Pollution, Journal Year: 2024, Volume and Issue: 362, P. 124912 - 124912

Published: Sept. 7, 2024

Language: Английский

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Network pharmacology and transcriptomics reveal androgen receptor as a potential protein target for 6PPD-quinone

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 957, P. 177678 - 177678

Published: Nov. 29, 2024

Language: Английский

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