How to correctly develop q-RASAR models for predictive cheminformatics

[1] Banerjee A., 2024, q-RASAR. A Path to Predictive Cheminformatics, DOI [10.1007/978-3-031-52057-0, DOI 10.1007/978-3-031-52057-0]

[2] 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 [J]. ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, 2023, 25 (10) : 1626 - 1644

[3] Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure-Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients [J]. CHEMICAL RESEARCH IN TOXICOLOGY, 2023, 36 (09) : 1518 - 1531

[4] Machine-learning-based similarity meets traditional QSAR: "q-RASAR" for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2023, 237

[5] Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach [J]. NANOTOXICOLOGY, 2023, 17 (01) : 78 - 93

[6] On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points [J]. CHEMICAL RESEARCH IN TOXICOLOGY, 2023, 36 (03) : 446 - 464

[7] A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites [J]. MOLECULAR INFORMATICS, 2023, 42 (04)

[8] First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability [J]. MOLECULAR DIVERSITY, 2022, 26 (05) : 2847 - 2862

[9] In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report [J]. TOXICOLOGY RESEARCH, 2024, 13 (01)

[10] Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees [J]. JOURNAL OF HAZARDOUS MATERIALS, 2023, 460

[1] Banerjee A., 2024, q-RASAR. A Path to Predictive Cheminformatics, DOI [10.1007/978-3-031-52057-0, DOI 10.1007/978-3-031-52057-0]

[2] 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 [J]. ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, 2023, 25 (10) : 1626 - 1644

[3] Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure-Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients [J]. CHEMICAL RESEARCH IN TOXICOLOGY, 2023, 36 (09) : 1518 - 1531

[4] Machine-learning-based similarity meets traditional QSAR: "q-RASAR" for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2023, 237

[5] Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach [J]. NANOTOXICOLOGY, 2023, 17 (01) : 78 - 93

[6] On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points [J]. CHEMICAL RESEARCH IN TOXICOLOGY, 2023, 36 (03) : 446 - 464

[7] A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites [J]. MOLECULAR INFORMATICS, 2023, 42 (04)

[8] First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability [J]. MOLECULAR DIVERSITY, 2022, 26 (05) : 2847 - 2862

[9] In silico soil degradation and ecotoxicity analysis of veterinary pharmaceuticals on terrestrial species: first report [J]. TOXICOLOGY RESEARCH, 2024, 13 (01)

[10] Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees [J]. JOURNAL OF HAZARDOUS MATERIALS, 2023, 460