Deep learning in drug discovery: an integrative review and future challenges

Askr, Heba; Elgeldawi, Enas; Ella, Heba Aboul; Elshaier, Yaseen A.M.M.; Gomaa, Mamdouh M.; Hassanien, Aboul Ella

doi:10.1007/s10462-022-10306-1

Cited by 96 publications

(52 citation statements)

References 257 publications

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“…On the other hand, neural networks can simultaneously learn the properties of many types of data. Thus, by combining deep learning with drug-protein(disease)-based networks, the drug selectivity or the protein promiscuity can be evaluated [ 72 ]. DTIs identify the interaction sites between drug compounds and protein targets [ 73 ].…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

Koutroumpa

Papavasileiou

Papadiamantis

et al. 2023

IJMS

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The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

Koutroumpa

Papavasileiou

Papadiamantis

et al. 2023

IJMS

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“…Explainable artificial intelligence (XAI) addresses the lack of transparency in traditional “black box” models, promoting trust and responsible use . Attention mechanisms, including self-attention in transformer-based models, show promise in enhancing interpretability, particularly in drug toxicity prediction. − Despite these advancements, challenges such as misleading interpretations and overfitting must be carefully considered when applying interpretability methods in drug toxicological studies.…”

Section: A Brief Account On Deep Learningmentioning

confidence: 99%

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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“…in non-coding DNA. [1][2][3][4][5][6][7][8][9] This review specifically focuses on the development and implementation of ML to interpret and understand biological phenomena and structures from microscopy images of biological samples. Particular emphasis is given to DL approaches due to recent advancements that have significantly impacted the field.…”

Section: Network Architecturementioning

confidence: 99%

From pixels to insights: Machine learning and deep learning for bioimage analysis

Jan,

Spangaro,

Lenartowicz

et al. 2023

BioEssays

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Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user‐friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.

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Deep learning in drug discovery: an integrative review and future challenges

Cited by 96 publications

References 257 publications

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

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

From pixels to insights: Machine learning and deep learning for bioimage analysis

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