The Experts below are selected from a list of 84 Experts worldwide ranked by ideXlab platform

Nuria Sotomayor - One of the best experts on this subject based on the ideXlab platform.

  • Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.
    Journal of chemical information and modeling, 2018
    Co-Authors: Lorena Simon-vidal, Esther Lete, Nuria Sotomayor, Sonia Arrasate, Oihane Garcia-calvo, Uxue Oteo, Humberto González-díaz
    Abstract:

    Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham Cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all...

  • Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies
    2018
    Co-Authors: Lorena Simón-vidal, Esther Lete, Nuria Sotomayor, Sonia Arrasate, Uxue Oteo, Oihane García-calvo, Humberto González-díaz
    Abstract:

    Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham Cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0–98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1–0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported

  • Brønsted Acid Catalyzed Enantioselective α-Amidoalkylation in the Synthesis of Isoindoloisoquinolines
    The Journal of organic chemistry, 2012
    Co-Authors: Eider Aranzamendi, Nuria Sotomayor, Esther Lete
    Abstract:

    The Parham Cyclization–intermolecular α-amidoalkylation sequence results in the facile enantioselective synthesis of 12b-substituted isoindoloisoquinolines (ee up to 95%) using BINOL-derived Bronsted acids. α-Amidoalkylation of indole occurs through the formation of a chiral conjugate base/bicyclic quaternary N-acyliminium ion pair.

  • Brønsted Acid Catalyzed Enantioselective α-Amidoalkylation in the Synthesis of Isoindoloisoquinolines
    2012
    Co-Authors: Eider Aranzamendi, Nuria Sotomayor, Esther Lete
    Abstract:

    The Parham Cyclization–intermolecular α-amidoalkylation sequence results in the facile enantioselective synthesis of 12b-substituted isoindoloisoquinolines (ee up to 95%) using BINOL-derived Brønsted acids. α-Amidoalkylation of indole occurs through the formation of a chiral conjugate base/bicyclic quaternary N-acyliminium ion pair

  • Synthesis of Pyrrolo[1,2-b]isoquinolines through Mesityllithium-Mediated IntramolecularCarbolithiation
    Synlett, 2008
    Co-Authors: Sergio Lage, Nuria Sotomayor, Irune Villaluenga, Esther Lete
    Abstract:

    Mesityllithium has proven to be an effective iodine-lithiumexchange reagent. Thus, carbolithiation reactions on 2-alkenyl-substituted N-( O-iodobenzyl)pyrroleshave been accomplished avoiding side reactions to afford pyrroloisoquinolinesin high yields (80-92%), improving the resultsobtained with T-BuLi. The carbolithiationreaction requires the use of electron-deficient alkenes. Mesityllithiumhas also been studied as an alternative to T-BuLiin Parham Cyclization with other internal electrophiles (aldehyde,ketone, ester, amide), proving to be more selective and efficientthan T-BuLi.

Esther Lete - One of the best experts on this subject based on the ideXlab platform.

  • Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.
    Journal of chemical information and modeling, 2018
    Co-Authors: Lorena Simon-vidal, Esther Lete, Nuria Sotomayor, Sonia Arrasate, Oihane Garcia-calvo, Uxue Oteo, Humberto González-díaz
    Abstract:

    Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham Cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all...

  • Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies
    2018
    Co-Authors: Lorena Simón-vidal, Esther Lete, Nuria Sotomayor, Sonia Arrasate, Uxue Oteo, Oihane García-calvo, Humberto González-díaz
    Abstract:

    Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham Cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0–98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1–0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported

  • Brønsted Acid Catalyzed Enantioselective α-Amidoalkylation in the Synthesis of Isoindoloisoquinolines
    The Journal of organic chemistry, 2012
    Co-Authors: Eider Aranzamendi, Nuria Sotomayor, Esther Lete
    Abstract:

    The Parham Cyclization–intermolecular α-amidoalkylation sequence results in the facile enantioselective synthesis of 12b-substituted isoindoloisoquinolines (ee up to 95%) using BINOL-derived Bronsted acids. α-Amidoalkylation of indole occurs through the formation of a chiral conjugate base/bicyclic quaternary N-acyliminium ion pair.

  • Brønsted Acid Catalyzed Enantioselective α-Amidoalkylation in the Synthesis of Isoindoloisoquinolines
    2012
    Co-Authors: Eider Aranzamendi, Nuria Sotomayor, Esther Lete
    Abstract:

    The Parham Cyclization–intermolecular α-amidoalkylation sequence results in the facile enantioselective synthesis of 12b-substituted isoindoloisoquinolines (ee up to 95%) using BINOL-derived Brønsted acids. α-Amidoalkylation of indole occurs through the formation of a chiral conjugate base/bicyclic quaternary N-acyliminium ion pair

  • Synthesis of Pyrrolo[1,2-b]isoquinolines through Mesityllithium-Mediated IntramolecularCarbolithiation
    Synlett, 2008
    Co-Authors: Sergio Lage, Nuria Sotomayor, Irune Villaluenga, Esther Lete
    Abstract:

    Mesityllithium has proven to be an effective iodine-lithiumexchange reagent. Thus, carbolithiation reactions on 2-alkenyl-substituted N-( O-iodobenzyl)pyrroleshave been accomplished avoiding side reactions to afford pyrroloisoquinolinesin high yields (80-92%), improving the resultsobtained with T-BuLi. The carbolithiationreaction requires the use of electron-deficient alkenes. Mesityllithiumhas also been studied as an alternative to T-BuLiin Parham Cyclization with other internal electrophiles (aldehyde,ketone, ester, amide), proving to be more selective and efficientthan T-BuLi.

David A. Hunt - One of the best experts on this subject based on the ideXlab platform.

Humberto González-díaz - One of the best experts on this subject based on the ideXlab platform.

  • Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.
    Journal of chemical information and modeling, 2018
    Co-Authors: Lorena Simon-vidal, Esther Lete, Nuria Sotomayor, Sonia Arrasate, Oihane Garcia-calvo, Uxue Oteo, Humberto González-díaz
    Abstract:

    Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham Cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all...

Melanie C. Skilbeck - One of the best experts on this subject based on the ideXlab platform.