Banca de DEFESA: JACKSON GOMES DE SOUZA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : JACKSON GOMES DE SOUZA
DATE: 10/12/2024
TIME: 08:30
LOCAL: meet.google.com/dms-gjcv-nsu
TITLE:

A Novel Deep Neural Network Technique for Drug-Target Interaction prediction

KEY WORDS:

Drug-Target Interaction; DTI prediction; Deep-learning; Convolutional Neural Network.

PAGES: 128
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUMMARY:

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. Prediction of drug-target interaction is an essential part of the DD process because it can accelerate it and reduce required costs. DTI prediction performed \textit{in silico} have used approaches based on molecular docking simulation, similarity-based and network and graph based. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for representing/encoding molecule and protein sequences into images; and the definition of a deep-learning approach based on a convolutional neural-network in order to create a new method for DTI prediction. The results of this research indicate that the image-based representation of molecule and protein sequences is a viable alternative to the NLP-based approaches and, as such, does not adopt an embedding layer
in the neural network. The training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. Regarding the Davis dataset, the results of the experiments indicate a concordance index (CI) of 0.876 and a MSE of 0.276; with the KIBA dataset, 0.836 and 0.226, respectively. Finally, the experimental results utilizing the BindingDB dataset and six core proteins of SARS-CoV-2 suggest that MPS2IT-DTI performs comparably with state-of-the-art methodologies for the repurposing of clinically approved antiviral agents in the context of COVID-19 treatment

COMMITTEE MEMBERS:
Presidente - 1837240 - MARCELO AUGUSTO COSTA FERNANDES
Externo ao Programa - 1669545 - DANIEL SABINO AMORIM DE ARAUJO - UFRNExterna à Instituição - FATIMA GARCÍA VILLÉN - UGR
Externo à Instituição - GUILHERME DE ALENCAR BARRETO - UFC
Externa à Instituição - RAQUEL DE MELO BARBOSA - US