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AIMOFGIFT: Towards AI-Driven Metal Organic Framework Drug Delivery Systems Design
Begoña Bilbao
1
,
Shan He
2
,
3
,
*
4, 5
Academic Editor: MOL2NET Team
Published:
15 June 2023
by MDPI
in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
congress CHEMBIO.MOL-09: Org. Chem., Med. Chem., Mol. Biol., & Pharm. Industry Congress, Paris, France-Fargo, USA, 2023.
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
Comments on this paper
Bernabe Ortega-Tenezaca
31 December 2023
Dear Researchers
I would like to know if the calculation software IFPTML Non-linear models developed in python language is available for the scientific community or for private use of the research group.
I would like to know if the calculation software IFPTML Non-linear models developed in python language is available for the scientific community or for private use of the research group.
Maider Baltasar Marchueta
4 January 2024
Dear authors thank you for your support to the conference.
Now we closed the publication phase and launched the post-publication phase of the conference. REVIEWWWERS Brainstorming Workshop is now open until January 25th. MOL2NET committee, authors, and social media followers worldwide are invited to post questions/answers and comments about papers. Please kindly post your public Answers (A) to the following questions in order to promote interchange of scientific ideas.
My Questions (Q) to you:
Q1. Could you elaborate on the key findings or insights obtained from the preliminary AI/ML models developed in the AIMOFGIFT project for predicting new MOF-Drug systems,
Q2. and how these models may pave the way for advancements in therapeutic applications for gastrointestinal tract diseases and beyond?
Dear author thanks in advance for your kind support answering the questions.
Now, the conference publication phase is close but you can answer here directly as a post-publication comment.
Last but not least, please become a verified REVIEWWWER of our conference by making questions to other papers in different Mol2Net congresses. Commenting steps: login, go to papers list, select paper, write comment, click post comment.
Happy new year 2024.
Thanks and kind regards,
CHEMBIOMOL Committee Assistant, Maider Baltasar Marchueta.
Now we closed the publication phase and launched the post-publication phase of the conference. REVIEWWWERS Brainstorming Workshop is now open until January 25th. MOL2NET committee, authors, and social media followers worldwide are invited to post questions/answers and comments about papers. Please kindly post your public Answers (A) to the following questions in order to promote interchange of scientific ideas.
My Questions (Q) to you:
Q1. Could you elaborate on the key findings or insights obtained from the preliminary AI/ML models developed in the AIMOFGIFT project for predicting new MOF-Drug systems,
Q2. and how these models may pave the way for advancements in therapeutic applications for gastrointestinal tract diseases and beyond?
Dear author thanks in advance for your kind support answering the questions.
Now, the conference publication phase is close but you can answer here directly as a post-publication comment.
Last but not least, please become a verified REVIEWWWER of our conference by making questions to other papers in different Mol2Net congresses. Commenting steps: login, go to papers list, select paper, write comment, click post comment.
Happy new year 2024.
Thanks and kind regards,
CHEMBIOMOL Committee Assistant, Maider Baltasar Marchueta.
6 January 2024
Thank you for your question and interest in our publication.
Q1. Key findings from AIMOFGIFT's AI/ML models: Created a database of MOF-Drug systems from public sources and developed predictive models for suggesting new combinations, offering insights into potential drug-MOF interactions and system designs.
Q2. Advancements in therapeutic applications: The models pave the way for tailored drug delivery systems, potentially accelerating GIT disease therapy by optimizing drug release, enhancing treatment efficacy, and expediting drug development processes.
Q1. Key findings from AIMOFGIFT's AI/ML models: Created a database of MOF-Drug systems from public sources and developed predictive models for suggesting new combinations, offering insights into potential drug-MOF interactions and system designs.
Q2. Advancements in therapeutic applications: The models pave the way for tailored drug delivery systems, potentially accelerating GIT disease therapy by optimizing drug release, enhancing treatment efficacy, and expediting drug development processes.
