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PTML Model Prediction of Preclinical Activity
Published:
05 July 2018
by MDPI
in MOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed.
congress CHEMBIOMOL-04: Chem. Biol. & Med. Chem. Workshop, Paraiba, Porto, Rostock, Germany-Galveston, Texas, USA, 2018
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
