RT Book,_Section
T1 QSAR & Network-based multi-species activity models for antifungals
A1 Prado Prado, Francisco Javier
A1 González Díaz, Humberto
A1 Santana Penín, María Lourdes
A1 Uriarte Villares, Eugenio
K1 Molecular descriptor
K1 Markov model
K1 Networks
K1 QSAR
K1 Co-expression network
K1 Probability
K1 Antimicrobials
K1 Antifungals
AB There are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model, which outputs were the inputs of the above-mentioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extent model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power-law network with few mechanisms (network hubs)
PB MDPI
SN 3-906980-19-7
YR 2007
FD 2007
LK http://hdl.handle.net/10347/26827
UL http://hdl.handle.net/10347/26827
LA eng
NO Prado-Prado, F.J.; Gonzàlez-Dìaz, H.; Santana, L.; Uriarte, E. QSAR & Network-based multi-species activity models for antifungals, in Proceedings of the 11th International Electronic Conference on Synthetic Organic Chemistry, 1–30 November 2007, MDPI: Basel, Switzerland, doi:10.3390/ecsoc-11-01372
NO The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational Chemistry
NO Gonzalez-Díaz H. acknowledges contract/grant sponsorship from the Program Isidro Parga Pondal of the “Dirección Xeral de Investigación y Desenvolvemento” of “Xunta de Galicia”. This author also acknowledges two contracts as guest professor in the Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Spain in 2006. The authors thank the Xunta de Galicia (projects PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (project PI061457) for partial financial support
DS Minerva
RD 24 abr 2026