EventsMOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed.
Event submissions
Published
with-doi10.3390/mol2net-04-05427
This submission belongs to the session 06. CHEMBIOMOL-04: Chem. Biol. & Med. Chem. Workshop, Paraiba, Porto, Rostock, Germany-Galveston, Texas, USA, 2018 of the event MOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed.
Published date
05 Jul, 2018
Citation
Harbil Bediaga, Sonia Arrasate Gil, PTML Model Prediction of Preclinical Activity, in Proceedings of MOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed., 15 January 2018–20 January 2019, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-04-05427
Share
Email
Facebook
Twitter
LinkedIn

PTML Model Prediction of Preclinical Activity

Harbil Bediaga 1
Sonia Arrasate Gil 1
1. Department of Organic Chemistry II, Faculty of Science and Technology, University of Basque Country (UPV/EHU)
Abstract

ChEMBL-tik datu basea lortuta, perturbazio teoria (PT) eta Machine Learning (ML) teknikak erabilita PTML eredu bat eraiki da, zein sistema biomolekular konplexuetan erabili daitekeen perturbazioen efektua kuantifikatzeko.

Eredu hau erabilita konposatu berri batek erakusten dituen minbiziaren aurkako parametro klinikoen (ki, LD50, etab.) balioak aurresan ditzakegu.

After obtaining the database from ChEMBL we combine Perturbation Theory (PT) and Machine Learning (ML) to obtain PTML Model, which has been created to quantify the perturbations of complex bio molecular systems. The model can predict preclinical (ki, LD50, etc.) values of new anti-cancer compounds.

Keywords
ChEMBL
Perturbation Theory (PT)
Machine Learning
PTML
anti-cancer
Poster
Mol2Net Harbil Bediaga.pdf
Mol2Net Harbil Bediaga.pdf
Post-emergence herbicidal activity of Eucalyptus globulus Labill. essential oil
Phytochemical Profiling and Antioxidant Activity of Aqueous Extract of Aegle marmelos Fruit Shell