Published June 21, 2022 | Version 1
Dataset Open

Machine Learning Models and New Computational Tool for the Discovery of Insect Repellents that Interfere with Olfaction

Creators

  • 1. Escuela de Ciencias Biológicas e Ingeniería, Universidad Yachay Tech, Hacienda San José, Proyecto Yachay, Urcuquí, Ecuador
  • 2. Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Av. Interoceánica Km 12 ½—Cumbayá, Quito 170157, Ecuador & Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Cumbayá, Quito, Ecuador.
  • 3. Universidad de Salamanca, Facultad de Farmacia, Departamento de Botánica, 4th Piso, Avenida Licenciado Méndez Nieto s/n, 37007 Salamanca, España
  • 4. Cátedras Conacyt – Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California, México
  • 5. Universidad Panamericana, Facultad de Ingeniería, Ciudad de México, México.

Description

  • SI1_Supporting Information file (docx) brings together detailed information on the outstanding models obtained for each dataset analyzed in this study such as statistical and training parameters and outliers. There can be found the responses in spikes/s of the mosquito Culex quinquefasciatus to the 50 IRs. Besides, there is presented a full table of the up-to-date studies related to QSAR and insect repellency.
  • SI2_EXP1_50IRs from Liu et al (2013) SDF file presents the structures of each of the 50 IRs analyzed.
  • SI3_EXP2_Datasets gathers the four datasets as SDF files from Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009) used for the repellency modeling in EXP2.
  • SI4_EXP3_Prospective analysis provides Malaria Box Library (400 compounds) as an SDF file, which were analyzed in our virtual screening to prospect potential virtual hits.
  • SI5_QuBiLS-MIDAS MDs lists contain three TXT lists of 3D molecular descriptors used in QuBiLS-MIDAS to describe the molecules used in the present study.
  • SI6_EXP1_Sensillar Modeling comprises two subfolders: Classification and Regression models for each of the six sensilla. Models built to predict the physiological interaction experimentally obtained from Liu et al. (2013). All of the models are implemented in the software SiLiS-PAPACS. Every single folder compiles a DOCX file with the detailed description of the model, an XLSX file with the output obtained from the training in Weka 3.9.4, an ARFF, and CSV files with the MDs for each molecule, and the SDF of the study dataset.
  • SI7_EXP2_Repellency Modeling encompasses the four datasets in the study: Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009). Inside the subfolders, there are three models per type of MDs (duplex, triple, generic, and mix) selected that best predict each dataset. As well as the SI6 folder, each model includes six files: DOCX, XLSX, ARFF, CSV, and an SDF.
  • SI8_Virtual Hits includes the cluster analysis results and physico-chemical properties of new IR virtual leads.

Files

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