Distributed and multicore QuBiLS‐MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra

García-Jacas, César R.; Marrero-Ponce, Yovani; Vivas‐Reyes, Ricardo; Suárez-Lezcano, José; Martínez-Rios, Félix; Terán, Julio E.; Aguilera‐Mendoza, Longendri

doi:10.1002/jcc.26167

Cited by 10 publications

(15 citation statements)

References 81 publications

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“…When executing main.py , the software will detect the file and generate 3D structures by optimizing each molecule’s geometry with 2000 iterations, using the UFF level of theory. These optimized UFF structures will be used for calculating the 2D and 3D descriptors that are present in GM using QuBiLs-MIDAS . If a topographic descriptor cannot be calculated, the molecule will be instantly OUT of the AD of GM and its E value will not be predicted.…”

Section: Resultsmentioning

confidence: 99%

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ElectroPredictor: An Application to Predict Mayr’s Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms

Cuesta

Moreno

López

et al. 2023

J. Chem. Inf. Model.

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Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr’s database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model’s development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q 5‑fold 2 of 0.909 and a Q ext 2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr’s electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.

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Section: Resultsmentioning

confidence: 99%

“…These optimized UFF structures will be used for calculating the 2D and 3D descriptors that are present in GM using QuBiLs-MIDAS. 73 If a topographic descriptor cannot be calculated, the molecule will be instantly OUT of the AD of GM and its E value will not be predicted.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

ElectroPredictor: An Application to Predict Mayr’s Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms

Cuesta

Moreno

López

et al. 2023

J. Chem. Inf. Model.

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“…Topographic 3D molecular descriptors suggested for cheminformatics studies such as QSAR were calculated using QuBiLs-MIDAS [33]. A total of 3031 3D molecular descriptors were estimated based on many algebraic operations that consider linear, bilinear, and quadratic indexes for all molecules in the dataset [34]. Next, two subsets were obtained for the modelling process.…”

Section: Datasetmentioning

confidence: 99%

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study

et al. 2022

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Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic β-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure–activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms. The best model (M2) meets the Tropsha’s test requirements and has the statistics parameters R2 = 0.843, Q2CV = 0.785, and Q2ext = 0.855. Also, coverage of 100% of the test set based on the applicability domain analysis was obtained. Furthermore, a deep analysis based on the ADME predictions, molecular docking, and molecular dynamics simulations was performed. The lipophilicity and the residue interactions were used as relevant criteria for selecting a candidate from the screening of the DiaNat and DrugBank databases. Finally, the FDA-approved drugs bilastine, bromfenac, and fenofibric acid are suggested as potential and lead FFA1 agonists.

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“…To define or generate the M -descriptors and define the chemical space using open-source and freely available software, there are several tools that have been available in the public domain for several years now. Typical examples include MayaChemTools (chemistry toolkit) [ 51 ], PaDEL-Descriptors [ 52 ], and the 3D descriptors implemented in QuBiLs-MIDAS [ 53 ], which was updated recently [ 54 ]. Additional free resources recently developed are briefly commented on hereunder.…”

Section: Open Resources To Expand and Describe The Chemical Spacementioning

confidence: 99%

Progress on open chemoinformatic tools for expanding and exploring the chemical space

Medina-Franco

Sánchez-Cruz

López-López

et al. 2021

J Comput Aided Mol Des

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The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure–property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure–property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

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Distributed and multicore QuBiLS‐MIDAS software v2.0: Computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra

Cited by 10 publications

References 81 publications

ElectroPredictor: An Application to Predict Mayr’s Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms

ElectroPredictor: An Application to Predict Mayr’s Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study

Progress on open chemoinformatic tools for expanding and exploring the chemical space

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