Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks with Conjoint Fingerprints: Application in Predicting Skin-Sensitizing Agents in Natural Compounds

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Skin sensitization, or allergic contact dermatitis, represents a critical end point in toxicity assessment, with profound implications for drug safety and regulatory decision-making. This study aims to develop robust deep-learning-based quantitative structure-activity relationship framework accurately predicting skin sensitization toxicity, particularly the context of natural-product-derived compounds. To achieve this, we explored advanced recurrent neural network architectures, including long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated unit (GRU), GRU, model intricate structure-toxicity relationships inherent molecular We aim optimize improve predictive performance by training cohort 55 models diverse set fingerprints. Notably, BiLSTM model, which integrates SMILES tokens RDKit fingerprints, achieved superior performance, underscoring its capability effectively capture key determinants sensitization. An extensive applicability domain analysis coupled an in-depth evaluation feature importance provided new insights into attributes that influence propensity. further evaluated using natural product data set, where it demonstrated exceptional generalization capabilities. The accuracy 86.5%, Matthews correlation coefficient 75.2%, sensitivity 100%, area under curve 88%, specificity 75%, F1-score 88.8%. Remarkably, categorized products discriminating sensitizing from non-sensitizing agents across various subcategories. These results underscore potential BiLSTM-based as powerful silico tools modern discovery efforts assessments, especially field products.

Язык: Английский

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Modeling the complex spatio-temporal dynamics of ocean wave parameters: A hybrid PINN-LSTM approach for accurate wave forecasting

Measurement, Год журнала: 2025, Номер unknown, С. 117383 - 117383

Опубликована: Март 1, 2025

Язык: Английский

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Feature Engineering for Short Term Residential Load Forecasting Using RNN-Based Neural Networks

Advances in Science, Technology & Innovation/Advances in science, technology & innovation, Год журнала: 2025, Номер unknown, С. 83 - 90

Опубликована: Янв. 1, 2025

Язык: Английский

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Temperature field and curing degree prediction of large composite blades based on coupled finite element analysis and machine learning

Polymer Composites, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Abstract Since large composite blades are variable curvature and thickness components with dimensions, the temperature field analysis will produce a inhomogeneous field, which requires lot of time. In this paper, new method combining finite element machine learning is proposed. By constructing numerical model blade curing using zoned heating to optimize gradient in tongue groove region, maximum reduced by 74.18% degree 21.987% compared conventional profile curing. A long short‐term memory(LSTM) neural network was used predict variations, Grey Wolf algorithm parameters high prediction accuracy. The instructive for online monitoring control process customized hot press tanks. Highlights improves temperature‐field balance. tandem LSTM constructed as an agent model. Enabling be connected cure. Optimizing grey wolf algorithm.

Язык: Английский

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Optimization and prediction of mechanical properties of TPU-Based wrist hand orthosis using Bayesian and machine learning models

Measurement, Год журнала: 2025, Номер unknown, С. 117405 - 117405

Опубликована: Март 1, 2025

Язык: Английский

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