Application of ontologies in computational medicinal chemistry studies and relations between chemical structure and biological activity

• Grant number: 18/06680-7
• Support Opportunities: Scholarships in Brazil - Master
• Start date: June 01, 2018
• End date: May 31, 2019
• Field of knowledge: Biological Sciences - Biophysics - Molecular Biophysics
• Agreement: IBM Brasil
• Principal Investigator: Kathia Maria Honorio
• Grantee: Rafaela Molina de Angelo
• Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Company: Universidade de São Paulo (USP). Escola de Artes, Ciências e Humanidades (EACH)
• Associated research grant: 16/18840-3 - Applying transfer learning techniques and ontologies to QSAR regression models, AP.PITE

Abstract

The quantitative study of structure-activity relationship (QSAR) involves the construction of regression models that relate properties (descriptors) of a set containing chemical substances and biological activity data in relation to one or more biological targets in the body. Data sets manipulated by researchers using QSAR techniques are usually characterized by a small number of instances (samples / compounds) and this makes complex predictive modeling more complex. In this context, inhibitors of the biological target ALK-5 will be analyzed and properties (descriptors) will be calculated for this set of inhibitors so that it is possible to develop an ontology that helps the construction and the obtaining of statistical quality QSAR models. This interest in the use of ontologies is based on the fact that the use of a structuring (organization) of the knowledge of a given domain is of great value for the development of more flexible and reliable computational systems for prediction, making the molecular information of the compounds studied for this target are unified and standardized. For the application of this ontology, an approach will be necessary for the selection of chemical data sets related to the target set through the use of already existing ontologies. (AU)

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE ANGELO, RAFAELA MOLINA; ALMEIDA, MICHELL DE OLIVEIRA; DE PAULA, HEBERTH; HONORIO, KATHIA MARIA. Studies on the Dual Activity of EGFR and HER-2 Inhibitors Using Structure-Based Drug Design Techniques. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, v. 19, n. 12, DEC 2018. (16/24524-7, 18/06680-7, 16/18840-3)

CHIARI, LAISE P. A.; DA SILVA, ALDINEIA P.; DE OLIVEIRA, ALINE A.; LIPINSKI, CELIO F.; HONORIO, KATHIA M.; DA SILVA, ALBERICO B. F.. Drug design of new sigma-1 antagonists against neuropathic pain: A QSAR study using partial least squares and artificial neural networks. Journal of Molecular Structure, v. 1223, JAN 5 2021. (16/24524-7, 18/06680-7, 13/07375-0, 17/10118-0)

DE ANGELO, RAFAELA MOLINA; ALMEIDA, MICHELL DE OLIVEIRA; DE PAULA, HEBERTH; HONORIO, KATHIA MARIA. Studies on the Dual Activity of EGFR and HER-2 Inhibitors Using Structure-Based Drug Design Techniques. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, v. 19, n. 12, p. 16-pg., 2018-12-01. (16/24524-7, 16/18840-3, 18/06680-7)

DA SILVA, ALDINEIA P.; DE ANGELO, RAFAELA M.; DE PAULA, HEBERTH; HONORIO, KATHIA M.; DA SILVA, ALBERICO B. F.. Drug design of new 5-HT6 antagonists: a QSAR study of arylsulfonamide derivatives. STRUCTURAL CHEMISTRY, v. 31, n. 4, MAR 2020. (18/06680-7, 13/07375-0)

SANTOS, GENISSON R.; CHIARI, LAISE P. A.; DA SILVA, ALDINEIA P.; LIPINSKI, CELIO F.; OLIVEIRA, ALINE A.; HONORIO, KATHIA M.; DE SOUSA, ALEXSANDRO GAMA; DA SILVA, ALBERICO B. F.. A partial least squares and artificial neural network study for a series of arylpiperazines as antidepressant agents. Journal of Molecular Modeling, v. 27, n. 10, OCT 2021. (17/10118-0, 18/06680-7, 13/07375-0, 16/24524-7)

CHIARI, LAISE P. A.; DA SILVA, ALDINEIA P.; HONORIO, KATHIA M.; DA SILVA, ALBERICO B. F.. A PLS study on the psychotropic activity for a series of cannabinoid compounds. Journal of Molecular Modeling, v. 29, n. 2, p. 23-pg., 2023-02-01. (16/24524-7, 17/10118-0, 13/07375-0, 18/06680-7)