EventsMOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
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This submission belongs to the session 02. CHEMBIO.MOL-09: Org. Chem., Med. Chem., Mol. Biol., & Pharm. Industry Congress, Paris, France-Fargo, USA, 2023. of the event MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published date
15 Jun, 2023
Academic Editor
author-avatarMOL2NET Team
Citation
Humbert Gonzalez-Diaz, Begoña Bilbao, Sonia Arrasate, Shan He, AIMOFGIFT: Towards AI-Driven Metal Organic Framework Drug Delivery Systems Design, in Proceedings of MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed., 25 December–31 December 2023, MDPI: Basel, Switzerland
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AIMOFGIFT: Towards AI-Driven Metal Organic Framework Drug Delivery Systems Design

Begoña Bilbao 1
Shan He 2
Sonia Arrasate 3
image
Humbert Gonzalez-Diaz 4,5
1. Department of Organic Chemistry II, Faculty of Science and technology, University of the Basque Country UPV/EHU, 48940, Leioa, Spain.
2. Department of Organic and Inorganic Chemistry, Faculty of Science and technology, University of the Basque Country UPV/EHU, 48940, Leioa, Spain.
3. Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940, Bilbao, Spain.
4. Department of Organic Chemistry II, University of the Basque Country (UPV/EHU), Biscay, Spain, Spain
5. IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain.
Abstract

Metal Organic Framework (MOF) drug delivery systems are interesting for Gastrointestinal tract (GIT) inflammatory, parasitic, cancer, and other diseases therapy. Artificial Intelligence - Machine Learning (AI/ML) can be used for a more rational design of these systems. However, the low abundance of data difficult these studies. In this communication we presented preliminary results of the project AIMOFGIFT funded by the SPRI Group Elkratek program in this area. A new database of MOF-Drug systems was created from public sources. In addition, different AI/ML preliminary models were developed to predict new MOF-Drug systems.

Keywords
MOF
AI
ML
Cheminformatics
Gastrointestinal tract
Cancer
Manuscript
AIMOFGIFT MOL2NET conference.pdf
Poster
AIMOFGIFT MOL2NET conference.pdf
AIMOFGIFT MOL2NET conference.pdf
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