Deep Learning on Site‐Specific Drug Delivery System

Mishra, Prem Shankar; Mishra, Rakhi; Mazumder, Rupa

doi:10.1002/9781119857983.ch3

Cited by 1 publication

(2 citation statements)

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“…244 RBMs are stacked to form a DBN that was trained greedily. [244][245][246]38,39 The team used five-layers-deep DBN. Each adjacent two layers (excluding the last two layers, which form the logistic regression classifier) were made up of RBMs.…”

Section: Rbm and Dbmmentioning

confidence: 99%

“…Wen et al employed DBN to develop DeepDTIs, which precisely forecast new DTIs within FDA-approved drugs and targets . RBMs are stacked to form a DBN that was trained greedily. − ,, The team used five-layers-deep DBN. Each adjacent two layers (excluding the last two layers, which form the logistic regression classifier) were made up of RBMs.…”

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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