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      An AI Pipeline to Investigate the Binding Properties of Food Bioactive Small Molecules

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      conference-abstract
      Author(s):   1 , 2
      Publication date (Electronic, pub):
      Publisher: REPO4EU
      Conference name: RExPO23
      Conference date: 25-26 October 2023
      Keywords: Drug discovery, machine learning, topological shortcuts, network science, diet, food
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            Abstract

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            Author and article information

            Conference
            Publisher: REPO4EU
            Publication date (Electronic, pub): 14 September 2023
            Affiliations
            [1 ] Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA ( https://ror.org/04b6nzv94)
            [2 ] Network Science Institute, Northeastern University, Boston, MA, USA ( https://ror.org/04t5xt781)
            Author information
            https://orcid.org/0000-0001-5201-6774
            Article
            DOI: 10.58647/REXPO.23008
            SO-VID: 813b37b7-3363-4a03-b669-c03456e1d30c
            Copyright © Authors
            License:

            Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

            Conference name: RExPO23
            Conference number: 2
            Conference location: Stockholm, Sweden
            Conference date: 25-26 October 2023
            History
            Product

            REPO4EU

            Categories

            Data availability: Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine
            Keywords: Drug discovery,machine learning,topological shortcuts,network science,diet,food

            References

            1. Menichetti Giulia. An AI pipeline to investigate the binding properties of poorly annotated molecules. Nature Reviews Physics. Vol. 4(6)2022. Springer Science and Business Media LLC. [Cross Ref]

            2. Nasirian Farzaneh, Menichetti Giulia. Molecular Interaction Networks and Cardiovascular Disease Risk: The Role of Food Bioactive Small Molecules. Arteriosclerosis, Thrombosis, and Vascular Biology. Vol. 43(6):813–823. 2023. Ovid Technologies (Wolters Kluwer Health). [Cross Ref]

            3. Menichetti Giulia, Barabási Albert-László. Nutrient concentrations in food display universal behaviour. Nature Food. Vol. 3(5):375–382. 2022. Springer Science and Business Media LLC. [Cross Ref]

            4. Chatterjee Ayan, Walters Robin, Shafi Zohair, Ahmed Omair Shafi, Sebek Michael, Gysi Deisy, Yu Rose, Eliassi-Rad Tina, Barabási Albert-László, Menichetti Giulia. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nature Communications. Vol. 14(1)2023. Springer Science and Business Media LLC. [Cross Ref]

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