Creating semi-Quanta multi-layer synthetic CNT images using CycleGAN

Safavigerdini, Kaveh; Surya, Ramakrishna; Reinhard, Andrew; Quinlan, Zach; Bunyak, Filiz; Maschmann, Matthew R.; Palaniappan, Kannappan

doi:10.1109/aipr60534.2023.10440711

Cited by 1 publication

(1 citation statement)

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“…Moreover, a linear kernel requires less memory and fewer parameters than others, making it a practical choice for our data set. Another algorithm employed in this study is RF, an ensemble technique that builds multiple decision trees and combines their predictions to produce a final result. , RF is a robust algorithm for regression problems, as it can capture nonlinear relationships between the feature and target variable. , These algorithms were selected due to their extensive use and reliable performance in cheminformatics and machine learning. ,,,− The run-time parameters of these ML models can be found in Table S3 of the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

Daghighi,

Casanola-Martin,

Iduoku

et al. 2024

Environ. Sci. Technol.

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In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure–Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R 2 train = 0.93, R 2 ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S–P fragments, ionization potential, and presence of C–N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

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