Glycoinformatics in the Artificial Intelligence Era

Bojar, Daniel; Lisacek, Frédérique

doi:10.1021/acs.chemrev.2c00110

Cited by 35 publications

(26 citation statements)

References 167 publications

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“…In addition to genomics and imaging, proteomics technologies have been extensively explored to identify biomarkers for the effectiveness of tumor immunotherapy ( 46 ). An AI-based serum proteomics test model has been developed to predict response to ICIs in patients with metastatic melanoma ( 48 ). The application of multi-omics-based AI models to predict tumor immunotherapy responses is also promising.…”

Section: The Existing Approaches To Predicting Immunotherapy Outcomesmentioning

confidence: 99%

Advances in artificial intelligence to predict cancer immunotherapy efficacy

Xie

Luo²,

Deng³

et al. 2023

Front. Immunol.

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Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.

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Section: The Existing Approaches To Predicting Immunotherapy Outcomesmentioning

confidence: 99%

Advances in artificial intelligence to predict cancer immunotherapy efficacy

Xie

Luo²,

Deng³

et al. 2023

Front. Immunol.

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“…Glycan microarrays have also proven to be extremely useful tools in deciphering glycan-protein interactions [ 114 , 115 , 116 ]. Artificial intelligence (AI) methods are also now being used in glycan structure-function studies and in drug design and are well suited to the analysis of large data sets [ 117 , 118 , 119 , 120 ]. A number of glycomic databases and glycomic tools are available to assist in such studies [ 118 , 119 , 120 , 121 , 122 ].…”

Section: Analysis Of Glycan Structure and Functionmentioning

confidence: 99%

“…AI provides a powerful analytic tool for analysis of large data sets [ 151 ]. AI methods integrated with prediction software in glycoinformatics approaches are emerging to provide further improvement in glycomics analysis [ 117 ].…”

Section: Analysis Of Glycan Structure and Functionmentioning

confidence: 99%

HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease

Hayes

Melrose

2023

IJMS

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Heparan sulfate is a ubiquitous, variably sulfated interactive glycosaminoglycan that consists of repeating disaccharides of glucuronic acid and glucosamine that are subject to a number of modifications (acetylation, de-acetylation, epimerization, sulfation). Variable heparan sulfate chain lengths and sequences within the heparan sulfate chains provide structural diversity generating interactive oligosaccharide binding motifs with a diverse range of extracellular ligands and cellular receptors providing instructional cues over cellular behaviour and tissue homeostasis through the regulation of essential physiological processes in development, health, and disease. heparan sulfate and heparan sulfate-PGs are integral components of the specialized glycocalyx surrounding cells. Heparan sulfate is the most heterogeneous glycosaminoglycan, in terms of its sequence and biosynthetic modifications making it a difficult molecule to fully characterize, multiple ligands also make an elucidation of heparan sulfate functional properties complicated. Spatio-temporal presentation of heparan sulfate sulfate groups is an important functional determinant in tissue development and in cellular control of wound healing and extracellular remodelling in pathological tissues. The regulatory properties of heparan sulfate are mediated via interactions with chemokines, chemokine receptors, growth factors and morphogens in cell proliferation, differentiation, development, tissue remodelling, wound healing, immune regulation, inflammation, and tumour development. A greater understanding of these HS interactive processes will improve therapeutic procedures and prognoses. Advances in glycosaminoglycan synthesis and sequencing, computational analytical carbohydrate algorithms and advanced software for the evaluation of molecular docking of heparan sulfate with its molecular partners are now available. These advanced analytic techniques and artificial intelligence offer predictive capability in the elucidation of heparan sulfate conformational effects on heparan sulfate-ligand interactions significantly aiding heparan sulfate therapeutics development.

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“…We have limited our focus to OSS that has been released alongside a peer-reviewed paper (i.e., work originating from a preprint manuscript was not included), thereby helping to ensure a higher academic standard of the work. Due to its limited in-depth coverage, the reader is referred to reviews written within the past 3 years that cover the use of ML in specific computational chemistry subdomains for additional information. − Given the numerous review articles and vastness of the topic, we believe that the information herein can serve as an introductory point into the application of ML in computational chemistry.…”

Section: Introductionmentioning

confidence: 99%

Open-Source Machine Learning in Computational Chemistry

Hagg

2023

J. Chem. Inf. Model.

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The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

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Glycoinformatics in the Artificial Intelligence Era

Cited by 35 publications

References 167 publications

Advances in artificial intelligence to predict cancer immunotherapy efficacy

Advances in artificial intelligence to predict cancer immunotherapy efficacy

HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease

Open-Source Machine Learning in Computational Chemistry

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