EventsMOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
Event submissions
Published
with-doi10.3390/MOL2NET-1-e008
This submission belongs to the session 03. USEDAT-01: USA-Europe Data Analysis Training Congress, Cambridge, UK-Bilbao, Spain-Miami, USA, 2015 of the event MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
Published date
04 Dec, 2015
Citation
Gerardo Maikel Casañola-Martin, Huong Le-Thi-Thu, Facundo Perez-Gimenez, Concepción Abad, Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction , in Proceedings of MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed., 5 December–15 December 2015, MDPI: Basel, Switzerland, doi: 10.3390/MOL2NET-1-e008
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Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction

Gerardo Maikel Casañola-Martin 1,2,3
Huong Le-Thi-Thu 4
Facundo Perez-Gimenez 2
Concepción Abad 3
1. Universidad Estatal Amazónica, Facultad de Ingeniería Ambiental, Paso lateral km 2 1/2 via Napo, Puyo, Ecuador
2. Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Spain
3. Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100 Burjassot, Spain
4. School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam
Abstract

The atom-quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures.

Keywords
Atom-based quadratic index
classification and regression model
machine learning
proteasome inhibition
QSAR
TOMOCOMD-CARDD software
Manuscript
5-MOL2NET_template_GerardoMCM.pdf
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