RT Journal Article
T1 Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models
A1 Munteanu, Cristian R.
A1 González-Díaz, Humberto
A1 Garcia, Rafael
A1 Loza García, María Isabel
A1 Pazos, Alejandro
K1 Molecular information
K1 Machine learning
K1 Protein graphs
K1 Python scripts
K1 QSAR models
K1 Web tools
AB The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.
PB Bentham Science
YR 2015
FD 2015-09-04
LK https://hdl.handle.net/10347/44904
UL https://hdl.handle.net/10347/44904
LA eng
NO Munteanu, C.R., González-Díaz, H., García, R., Loza, M., Pazos, A. (2015). Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models. Combinatorial Chemistry & High Throughput Screening, 18(8), 735-750
NO The authors acknowledge the support provided by the Galician Network of Drugs R+D REGID (Xunta de Galicia R2014/025) and by the "Collaborative Project on Medical Informatics (CIMED)" PI 13/00280 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Fund / GAIN (FEDER - CONECTAPEME - INTERCONECTA). This work was partially supported by the Galician Network for Colorectal Cancer Research (Red Gallega de Cáncer Colorrectal - REGICC, Ref.: CN 20121217), Institute for Biomedical Informatics of A Coruña (INIBIC), and Center for Research of Information and Communication Technologies (CITIC).
DS Minerva
RD 28 abr 2026