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Diéguez-Santanaa K, Puris A, Rivera-Borroto OM, Casanola-Marting GM, Rasulev B, González-Díaz H. A Fuzzy System Classification Approach for QSAR Modeling of α-Amylase and α-Glucosidase Inhibitors. Curr Comput Aided Drug Des 2022; 18:469-479. [DOI: 10.2174/1573409918666220929124820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 07/07/2022] [Accepted: 08/09/2022] [Indexed: 11/22/2022]
Abstract
Introduction:
This report proposes the application of a new Machine Learning algorithm called Fuzzy Unordered Rules Induction Algorithm (FURIA)-C in the classification of drug-like compounds with antidiabetic inhibitory ability toward the main two pharmacological targets: α-amylase and α-glucosidase.
Methods:
The two obtained QSAR models were tested for classification capability, achieving satisfactory accuracy scores of 94.5% and 96.5%, respectively. Another important outcome was to achieve various α-amylase and α-glucosidase fuzzy rules with high Certainty Factor values. Fuzzy-Rules derived from the training series and active classification rules were interpreted. An important external validation step, comparing our method with those previously reported, was also included.
Results:
The Holm’s test comparison showed significant differences (p-value<0.05) between FURIA-C, Linear Discriminating Analysis (LDA), and Bayesian Networks, the former beating the two latter ones according to the relative ranking score of the Holm’s test.
Conclusion:
From these results, the FURIA-C algorithm could be used as a cutting-edge technique to predict (classify or screen) the α-amylase and α-glucosidase inhibitory activity of new compounds and hence speed up the discovery of new potent multi-target antidiabetic agents.
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Affiliation(s)
| | - Amilkar Puris
- Facultad de Ciencias de La Ingeniería, Universidad Técnica Estatal de Quevedo, Ecuador
| | - Oscar M. Rivera-Borroto
- Department of Mathematics, Houston Community College-West Loop Campus, Houston TX, 77081, USA
- Department of Mathematics, Lone Star College-CyFair Campus, Houston, TX, 77433, USA
- Departamento de Química Física Aplicada, Facultad de Ciencias,
Universidad Autonoma de Madrid, 28049 Madrid, Spain
| | - Gerardo M. Casanola-Marting
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, North Dakota, 58102, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials,
North Dakota State University, Fargo, ND, 58102, USA
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940, Leioa, Spain
- Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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Diéguez-Santana K, Rivera-Borroto OM, Puris A, Pham-The H, Le-Thi-Thu H, Rasulev B, Casañola-Martin GM. Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies. Chem Biol Drug Des 2019; 94:1414-1421. [PMID: 30908888 DOI: 10.1111/cbdd.13518] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 02/17/2019] [Accepted: 03/03/2019] [Indexed: 12/17/2022]
Abstract
In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.
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Affiliation(s)
| | - Oscar M Rivera-Borroto
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain
| | - Amilkar Puris
- Facultad de Ciencias de La Ingeniería, Universidad Técnica Estatal de Quevedo, Quevedo, Ecuador
| | | | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota
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Dieguez-Santana K, Pham-The H, Rivera-Borroto OM, Puris A, Le-Thi-Thu H, Casanola-Martin GM. A Two QSAR Way for Antidiabetic Agents Targeting Using α-Amylase and α-Glucosidase Inhibitors: Model Parameters Settings in Artificial Intelligence Techniques. LETT DRUG DES DISCOV 2017. [DOI: 10.2174/1570180814666161128121142] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Karel Dieguez-Santana
- Faculty of Life Sciences, Amazonian State University, Paso Lateral km 2½ via Tena, Puyo, Pastaza, Ecuador
| | - Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Oscar M. Rivera-Borroto
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Amilkar Puris
- Facultad de Ciencias de La Ingeniería, Universidad Técnica Estatal de Quevedo, Ecuador
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam
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Martínez-Santiago O, Marrero-Ponce Y, Vivas-Reyes R, Rivera-Borroto OM, Hurtado E, Treto-Suarez MA, Ramos Y, Vergara-Murillo F, Orozco-Ugarriza ME, Martínez-López Y. Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets. SAR QSAR Environ Res 2017; 28:367-389. [PMID: 28590848 DOI: 10.1080/1062936x.2017.1326403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 04/27/2017] [Indexed: 06/07/2023]
Abstract
Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q2ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q2ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.
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Affiliation(s)
- O Martínez-Santiago
- a Department of Chemical Sciences , Central University 'Martha Abreu' of Las Villas , Santa Clara , Cuba
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- c Group of Quantum and Theoretical Chemistry , University of Cartagena , Cartagena de Indias , Colombia
- d Facultad de Ingeniería , Grupo CipTec, Fundación Universitaria Tecnológico Comfenalco , Cartagena de Indias , Colombia
| | - Y Marrero-Ponce
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- e Escuela de Medicina, Edificio de Especialidades Médicas , Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA) , Av. Interoceánica Km 12 ½, Cumbayá , Ecuador
- f Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica , Quito , Ecuador
- g Grupo de Investigación Ambiental (GIA) , Fundación Universitaria Tecnológico de Comfenalco , Cartagena de Indias , Colombia
| | - R Vivas-Reyes
- c Group of Quantum and Theoretical Chemistry , University of Cartagena , Cartagena de Indias , Colombia
- d Facultad de Ingeniería , Grupo CipTec, Fundación Universitaria Tecnológico Comfenalco , Cartagena de Indias , Colombia
| | - O M Rivera-Borroto
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- h Departamento de Química Física Aplicada , Universidad Autónoma de Madrid (UAM) , Madrid , España
| | - E Hurtado
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
| | - M A Treto-Suarez
- i Center of Applied Nanosciences (CENAP), Andres Bello University , Chile
| | - Y Ramos
- j Department of Economic Sciences , University of Camagüey , Camagüey , Cuba
| | - F Vergara-Murillo
- c Group of Quantum and Theoretical Chemistry , University of Cartagena , Cartagena de Indias , Colombia
- d Facultad de Ingeniería , Grupo CipTec, Fundación Universitaria Tecnológico Comfenalco , Cartagena de Indias , Colombia
| | - M E Orozco-Ugarriza
- k Seccional Cartagena y Grupo de Investigación Traslacional en Biomedicina & Biotecnología - GITB&B , Universidad del Sinú - Elías Bechara Zainúm , Cartagena de Indias , Colombia
| | - Y Martínez-López
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- l Grupo de Investigación de Inteligencia Artificial (AIRES) , Universidad de Camagüey , Camagüey , Cuba
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Meneses-Marcel A, Rivera-Borroto OM, Marrero-Ponce Y, Montero A, Machado Tugores Y, Escario JA, Gómez Barrio A, Montero Pereira D, Nogal JJ, Kouznetsov VV, Ochoa Puentes C, Bohórquez AR, Grau R, Torrens F, Ibarra-Velarde F, Arán VJ. New antitrichomonal drug-like chemicals selected by bond (edge)-based TOMOCOMD-CARDD descriptors. ACTA ACUST UNITED AC 2008; 13:785-94. [PMID: 18753687 DOI: 10.1177/1087057108323122] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel lead trichomonacidals. The obtained LDA-based quantitative structure-activity relationships (QSAR) models, using nonstochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews correlation coefficients of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. Later, both models were applied to the virtual screening of 21 chemicals to find new lead antitrichomonal agents. Predictions agreed with experimental results to a great extent because a correct classification for both models of 95.24% (20 of 21) of the chemicals was obtained. Of the 21 compounds that were screened and synthesized, 2 molecules (chemicals G-1, UC-245) showed high to moderate cytocidal activity at the concentration of 10 microg/ml, another 2 compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100 microg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153, except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10 microg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promising results as a trichomonacidal drug-like compound.
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Affiliation(s)
- Alfredo Meneses-Marcel
- Unit of Computer-Aided Molecular Biosilico Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Central University of Las Villas, Villa Clara, Cuba
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Marrero-Ponce Y, Meneses-Marcel A, Rivera-Borroto OM, García-Domenech R, De Julián-Ortiz JV, Montero A, Escario JA, Barrio AG, Pereira DM, Nogal JJ, Grau R, Torrens F, Vogel C, Arán VJ. Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds. J Comput Aided Mol Des 2008; 22:523-40. [DOI: 10.1007/s10822-008-9171-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2006] [Accepted: 01/05/2008] [Indexed: 10/22/2022]
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