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Applying transfer learning techniques and ontologies to QSAR regression models

Grant number: 16/18840-3
Support type:Research Grants - Research Partnership for Technological Innovation - PITE
Duration: September 01, 2017 - September 30, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Cooperation agreement: IBM Brasil
Principal Investigator:Patrícia Rufino Oliveira
Grantee:Patrícia Rufino Oliveira
Home Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Company: IBM Brasil - Indústria, Máquinas e Serviços Ltda
City: São Paulo
Partner institutions: Universidade de São Paulo
Co-Principal Investigators:Kathia Maria Honorio
Associated scholarship(s):18/06680-7 - Application of ontologies in computational medicinal chemistry studies and relations between chemical structure and biological activity, BP.MS

Abstract

To develop a new medicine, investigators must analyze the biological targets of a given disease, discover and develop drug candidates for these targets, performing in parallel, biological laboratory tests to validate the drug effectiveness and side effects. The Quantitative Study of Activity-Structure Relationships (QSAR) involves building regression models that relate a set of descriptors of a chemical compound and its biological activity with respect to one or more targets in the human body. Datasets manipulated by researchers to QSAR analysis are generally characterized by a small number of instances and this makes it more complex to build accurate predictive models. In this context, transfer learning techniques that take information from other QSAR models to the same biological target would be desirable, reducing the effort and cost for generating new chemical compounds descriptors. This project aims at applying two learning transfer methods, based on instances and on parameters, to build QSAR regression models via Support Vectors (SV). In order to implement the transfer learning techniques, a new approach for selecting related chemical datasets, which is based on the integration of different ontologies, will be developed. The regression results obtained by the use of the transfer learning approaches will be compared with those obtained by classical methods regarding the Mean Squared Error (MSE) measure. (AU)

Scientific publications
(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)
LIPINSKI, CELIO F.; MALTAROLLO, VINICIUS G.; OLIVEIRA, PATRICIA R.; DA SILVA, ALBERICO B. F.; HONORIO, KATHIA MARIA. Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery. FRONTIERS IN ROBOTICS AND AI, v. 6, NOV 5 2019. Web of Science Citations: 0.
MALTAROLLO, VINICIUS GONCALVES; KRONENBERGER, THALES; ESPINOZA, GABRIEL ZARZANA; OLIVEIRA, PATRICIA RUFINO; HONORIO, KATHIA MARIA. Advances with support vector machines for novel drug discovery. EXPERT OPINION ON DRUG DISCOVERY, v. 14, n. 1, p. 23-33, JAN 2 2019. Web of Science Citations: 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 DEC 2018. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.