EventsMOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Event submissions
Published
with-doi10.3390/mol2net-06-06867 (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
16 Jun, 2020
Citation
Jon Collados, Harbil Bediaga, LAGA: New software for new drug design using Perturbation Theory and Machine Learning techniques, 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-06-06867
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LAGA: New software for new drug design using Perturbation Theory and Machine Learning techniques

Jon Collados 1
Harbil Bediaga 2
1. Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
2. Department of Physical Chemistry, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
Abstract

The main objective of this project is the development of a useful computational tool for future preclinical trials. The implemented model will be a PTML-LFER model, for the prediction of the pharmacological activity of a certain molecule, or list of molecules, under multiple test conditions.

Keywords
anticancer
drug design
LFER
QSAR
software
Manuscript
LAGA.pdf
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