Toxicity Detection in Drug Candidates using Simplified Molecular-Input Line-Entry System

Nath, Mriganka; Goswami, Subhasish

doi:10.5120/ijca2020920695

Cited by 1 publication

(4 citation statements)

References 7 publications

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“…207 Experiments with different batch sizes show an increase in training accuracy, but testing accuracy and AUROC remain almost constant. 207 Promising results are observed in the study with LSTM, but better-balanced data is needed for accuracy improvement.…”

Section: Dnnmentioning

confidence: 94%

“…207 The team used simplified molecular-input line-entry system (SMILES) as a parameter for developing LSTM-based models to examine new drug toxicity. 207 However, it is cautioned that imbalanced data may affect the reliability of the model's predictions, and thus sampling is adapted to balance the data. 207 Experiments with different batch sizes show an increase in training accuracy, but testing accuracy and AUROC remain almost constant.…”

Section: Dnnmentioning

confidence: 99%

“…207 However, it is cautioned that imbalanced data may affect the reliability of the model's predictions, and thus sampling is adapted to balance the data. 207 Experiments with different batch sizes show an increase in training accuracy, but testing accuracy and AUROC remain almost constant. 207 Promising results are observed in the study with LSTM, but better-balanced data is needed for accuracy improvement.…”

Section: Dnnmentioning

confidence: 99%

“…Though CNNs are being successfully used to predict molecular toxicity, Nath and Goswami showed an efficient and comparatively fast method for detecting toxicity in new drug candidates by using LSTM . The team used simplified molecular-input line-entry system (SMILES) as a parameter for developing LSTM-based models to examine new drug toxicity . However, it is cautioned that imbalanced data may affect the reliability of the model’s predictions, and thus sampling is adapted to balance the data .…”

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

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Section: Dnnmentioning

confidence: 94%