Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed Heck–Heck cascade reactions

Blázquez-Barbadillo, C.; Aranzamendi, Eider; Coya, Estíbaliz; Lete, Esther; Sotomayor, Nuria; González-Dı́az, Humberto

doi:10.1039/c6ra08751e

Cited by 23 publications

(23 citation statements)

References 59 publications

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“…These PT operators resemble the Box-Jenkins MA operators. (18,28) We used both linear and nonlinear ML algorithms called linear discriminant analysis (LDA) and artificial neural network (ANN) to seek alternative linear and nonlinear models. In addition, several ML methods from Weka (53) have been used: Bayes Nets(54), RandomTree(55), Random Forest, and Decision Table.…”

Section: L-[(3s5r)-35-bis(azidomethyl)]prolyl-l-valyl-l-alanine Hydro...mentioning

confidence: 99%

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Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

Costa

Schwartz

Caamaño

et al. 2018

ACS Chem. Neurosci.

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Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC, EC, K, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

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Section: L-[(3s5r)-35-bis(azidomethyl)]prolyl-l-valyl-l-alanine Hydro...mentioning

confidence: 99%

“…Specifically, PTML models combine PT and ML methods to study biomolecular systems. (18,28) In fact, PTML models may have applications in medicinal chemistry, nanotechnology, etc. to study large data sets with multiple experimental conditions or other parameters.…”

Section: Introductionmentioning

confidence: 99%

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

Costa

Schwartz

Caamaño

et al. 2018

ACS Chem. Neurosci.

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“…The method has also been extended to predict the enantioselectivity and/or yield of intramolecularc arbolithiation [51] and Heck-Heckc ascade reactions. [52] In some cases, the developedP T-QSRR models use trace operators, like spectral moments, or eigenvalues of chemical structure matrices, like bond adjacency matrix,a st he inputs. [53,51] Now,w ei ntend to use this correlative PT-QSRR approach to predict the effect of the structures of the substrate, nucleophile, and catalyst, as well as the experimental conditions, on the enantioselectivity of intermolecular a-amidoalkylation reactions.…”

Section: Predictive Studymentioning

confidence: 99%

Chiral Brønsted Acid‐Catalyzed Enantioselective α‐Amidoalkylation Reactions: A Joint Experimental and Predictive Study

et al. 2016

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Enamides with a free NH group have been evaluated as nucleophiles in chiral Brønsted acid‐catalyzed enantioselective α‐amidoalkylation reactions of bicyclic hydroxylactams for the generation of quaternary stereocenters. A quantitative structure–reactivity relationship (QSRR) method has been developed to find a useful tool to rationalize the enantioselectivity in this and related processes and to orient the catalyst choice. This correlative perturbation theory (PT)‐QSRR approach has been used to predict the effect of the structure of the substrate, nucleophile, and catalyst, as well as the experimental conditions, on the enantioselectivity. In this way, trends to improve the experimental results could be found without engaging in a long‐term empirical investigation.

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“…Furthermore, the combination of perturbation theory (PT) and machine learning techniques can be applied to solve this uncertainty of compound activity. This is because PTML (perturbation theory machine learning) models have been applied to different areas, such as medicinal proteomics, chemistry, and nanotechnology. − This model is especially adequate for databases with similar big data characteristics and combinatorial information. Nevertheless, research about PTML models for vitamins and vitamin derivatives including multiple biological activity data have not been reported.…”

Section: Introductionmentioning

confidence: 99%

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives

et al. 2020

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Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (n ij ) with a considerable combination of assay conditions (c j ). Indeed, the ChEMBL platform contains a database that includes results from 36 220 different biological activity bioassays of 21 240 different vitamins and vitamin derivatives. These assays present are heterogeneous in terms of assay combinations of c j . They are focused on >500 different biological activity parameters (c 0), >340 different targets (c 1), >6200 types of cell (c 2), >120 organisms of assay (c 3), and >60 assay strains (c 4). It includes a total of >1850 niacin assays, >1580 tretinoin assays, >1580 retinol assays, 857 ascorbic acid assays, etc. Given the complexity of this combinatorial data in terms of being assimilated by researchers, we propose to build a model by combining perturbation theory (PT) and machine learning (ML). Through this study, we propose a PTML (PT + ML) combinatorial model for ChEMBL results on biological activity of vitamins and vitamins derivatives. The linear discriminant analysis (LDA) model presented the following results for training subset a: specificity (%) = 90.38, sensitivity (%) = 87.51, and accuracy (%) = 89.89. The model showed the following results for the external validation subset: specificity (%) = 90.58, sensitivity (%) = 87.72, and accuracy (%) = 90.09. Different types of linear and nonlinear PTML models, such as logistic regression (LR), classification tree (CT), näive Bayes (NB), and random Forest (RF), were applied to contrast the capacity of prediction. The PTML-LDA model predicts with more accuracy by applying combinatorial descriptors. In addition, a PCA experiment with chemical structure descriptors allowed us to characterize the high structural diversity of the chemical space studied. In any case, PTML models using chemical structure descriptors do not improve the performance of the PTML-LDA model based on ALOGP and PSA. We can conclude that the three variable PTML-LDA model is a simplified and adaptable tool for the prediction, for different experiment combinations, the biological activity of derivative vitamins.

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Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed Heck–Heck cascade reactions

Abstract: A new multi-output PT-QSRR model to correlate and predict the enantioselectivity and yield of Heck–Heck cascade reactions has been developed.

Cited by 23 publications

References 59 publications

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

Chiral Brønsted Acid‐Catalyzed Enantioselective α‐Amidoalkylation Reactions: A Joint Experimental and Predictive Study

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives

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