Drug-Target Interaction Prediction Based on Transformer

Liu, Junkai; Jiang, Tengsheng; Lu, Yaoyao; Wu, Hongjie

doi:10.1007/978-3-031-13829-4_25

Cited by 3 publications

(7 citation statements)

References 17 publications

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“…258 To improve the performance of the approach, the researchers conducted a lot of adjustment experiments and optimized the parameters to analyze and compare the experimental results. 258 Tests on two benchmark data sets demonstrated the effectiveness and accuracy of their method.…”

Section: Rbm and Dbmmentioning

confidence: 99%

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

“…258 The method uses only the sequence information on drugs and proteins and can effectively extract deep features through the attention mechanism in the transformer. 258 To improve the performance of the approach, the researchers conducted a lot of adjustment experiments and optimized the parameters to analyze and compare the experimental results. 258 Tests on two benchmark data sets demonstrated the effectiveness and accuracy of their method.…”

Section: Rbm and Dbmmentioning

confidence: 99%

“…To address the issue of traditional methods being unable to effectively extract features of complex drug and protein structures, Liu et al propose a novel transformer-based method for DTI prediction . The method uses only the sequence information on drugs and proteins and can effectively extract deep features through the attention mechanism in the transformer . To improve the performance of the approach, the researchers conducted a lot of adjustment experiments and optimized the parameters to analyze and compare the experimental results .…”

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

See 3 more Smart Citations

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: Rbm and Dbmmentioning

confidence: 99%

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

Section: Rbm and Dbmmentioning

confidence: 99%

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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“…By learning the sequence patterns of different protein families, PLMs can accurately classify proteins and predict their functions [17,23,30]. This capability not only streamlines the process of protein classification but also opens up new avenues for the discovery of therapeutic targets [31].…”

Section: Introduction Backgroundmentioning

confidence: 99%

Exploiting protein language models for the precise classification of ion channels and ion transporters

Ghazikhani

Butler

2023

Preprint

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This study presents TooT-PLM-ionCT, a composite framework consisting of three distinct systems, each with different architectures and trained on unique datasets. Each system within TooT-PLM-ionCT is dedicated to a specific task: segregating ion channels (ICs) and ion transporters (ITs) from other membrane proteins and differentiating ICs from ITs. These systems exploit the capabilities of six diverse Protein Language Models (PLMs) - ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters). As these proteins play a pivotal role in the regulation of ion movement across cellular membranes, they are integral to numerous biological processes and overall cellular vitality. To circumvent the costly and time-consuming nature of wet lab experiments, we harness the predictive prowess of PLMs, drawing parallels with techniques in natural language processing. Our strategy engages six classifiers, embracing both conventional methodologies and a deep learning model, for each of our defined tasks. Furthermore, we delve into critical factors influencing our tasks, including the implications of dataset balancing, the effect of frozen versus fine-tuned PLM representations, and the potential variance between half and full precision floating-point computations. Our empirical results showcase superior performance in distinguishing ITs from other membrane proteins and differentiating ICs from ITs, while the task of discriminating ICs from other membrane proteins exhibits results commensurate with the current state-of-the-art.

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Exploiting protein language models for the precise classification of ion channels and ion transporters

Ghazikhani,

Butler

2024

Proteins

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This study introduces TooT‐PLM‐ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion transporters (ITs) from MPs, and differentiating ICs from ITs. Drawing upon the strengths of six Protein Language Models (PLMs)—ProtBERT, ProtBERT‐BFD, ESM‐1b, ESM‐2 (650M parameters), and ESM‐2 (15B parameters), TooT‐PLM‐ionCT employs a combination of traditional classifiers and deep learning models for nuanced protein classification. Originally validated on an existing dataset by previous researchers, our systems demonstrated superior performance in identifying ITs from MPs and distinguishing ICs from ITs, with the IC‐MP discrimination achieving state‐of‐the‐art results. In light of recommendations for additional validation, we introduced a new dataset, significantly enhancing the robustness and generalization of our models across bioinformatics challenges. This new evaluation underscored the effectiveness of TooT‐PLM‐ionCT in adapting to novel data while maintaining high classification accuracy. Furthermore, this study explores critical factors affecting classification accuracy, such as dataset balancing, the impact of using frozen versus fine‐tuned PLM representations, and the variance between half and full precision in floating‐point computations. To facilitate broader application and accessibility, a web server (https://tootsuite.encs.concordia.ca/service/TooT-PLM-ionCT) has been developed, allowing users to evaluate unknown protein sequences through our specialized systems for IC‐MP, IT‐MP, and IC‐IT classification tasks.

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Drug-Target Interaction Prediction Based on Transformer

Cited by 3 publications

References 17 publications

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

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

Exploiting protein language models for the precise classification of ion channels and ion transporters

Exploiting protein language models for the precise classification of ion channels and ion transporters

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