RT Book,_Section
T1 Multi-Target Spectral Moment QSAR vs. ANN for antiparasitic drugs against different parasite species
A1 Prado Prado, Francisco Javier
A1 García Mera, Xerardo
A1 González Díaz, Humberto
K1 Mt-QSAR
K1 Markov model
K1 Antiparasitic drugs
K1 Linear Discriminant Analysis
K1 Artificial Neural Network
AB There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial Neural Networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis
PB MDPI
SN 3-906980-24-3
YR 2010
FD 2010
LK http://hdl.handle.net/10347/26874
UL http://hdl.handle.net/10347/26874
LA eng
NO Proceedings of the 14th International Electronic Conference on Synthetic Organic Chemistry, 1–30 November 2010, MDPI: Basel, Switzerland, doi:10.3390/ecsoc-14-00466
NO The 14th International Electronic Conference on Synthetic Organic Chemistry session Computational Chemistry
NO F. acknowledges the financial support from program Angeles Albariño and González-Díaz, H. acknowledges financial support of the Programme Isidro Parga Pondal both funded by the Xunta de Galicia and European Social Fund (ESF)
DS Minerva
RD 30 abr 2026