EventsMOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Event submissions
Published
with-doi10.3390/mol2net-06-06863 (registering DOI)
This submission belongs to the session 09. USEDAT-06: USA-Europe Data Analysis Training Program Workshop, Bilbao, Spain-Cambridge, UK-Miami, USA, 2020 of the event MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Published date
13 Jun, 2020
Citation
Ricardo Santana, Robin Zuluaga, Piedad Gañán, Sonia Arrasate, Enrique Onieva, Humbert González-Díaz, Designing nano-systems for anticancer purposes by applying Perturbation Theory Machine Learning (PTML) models, in Proceedings of MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed., 30 January 2020–30 January 2021, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-06-06863
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Designing nano-systems for anticancer purposes by applying Perturbation Theory Machine Learning (PTML) models

Ricardo Santana 1
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Robin Zuluaga 2
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Piedad Gañán 2
Sonia Arrasate 3
Enrique Onieva 4
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Humbert González-Díaz 5
1. University of Deusto / Universidad Pontificia Bolivariana
2. Universidad Pontificia Bolivariana
3. University of Basque Country
4. University of Deusto
5. Department of Organic Chemistry II and Basque Center for Biophysics (CSIC-UPVEHU), University of Basque Country (UPVEHU), Basque Country, Spain
Abstract

The number of possible designs of nano-systems is elevated. The design depends on the function we need to develop. Among these systems we highlight Nanoparticle Drug Delivery Systems (DDNS) of high interest not only for Nanotechnology but also for Biomaterials science.1–3

In this work we fusion the following information: 1) Drug-vitamin release nano-systems (DVRNs). This data set was collected from literature. 2) Vitamin derivatives data set extracted from ChEMBL database. Both data sets contain different assay conditions and molecular descriptors. Once we fusion the information, we apply Perturbation Theory Machine Learning (PTML) method in order to build the model. Once built with Perturbation Theory Operators (PT Operators), it presents both Specificity and Sensibility higher than 80%.

Until the best of our knowledge, we developed the first multi-label PTML model useful to design DVRNs for optimal biological activity.

Keywords
Nanotechnology
DVRN
Drug Delivery
Machine Learning
PTML
Manuscript
USEDAT-2020.pdf
PTML-LDA model applied to allosteric modulators
Alternative therapies for Mexican Leishmania.