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Predicting the Magnitude of σ-Holes Using VmaxPred, a Fast and Efficient Tool Supporting the Application of Halogen Bonds in Drug Discovery.
Heidrich, Johannes; Exner, Thomas E; Boeckler, Frank M.
Afiliação
  • Heidrich J; Molecular Design and Pharmaceutical Biophysics, Institute of Pharmaceutical Sciences , Eberhard Karls University Tübingen , Auf der Morgenstelle 8 , 72076 Tübingen , Germany.
  • Exner TE; Molecular Design and Pharmaceutical Biophysics, Institute of Pharmaceutical Sciences , Eberhard Karls University Tübingen , Auf der Morgenstelle 8 , 72076 Tübingen , Germany.
  • Boeckler FM; Center for Bioinformatics Tübingen (ZBIT) , Eberhard Karls University Tübingen , Sand 1 , 72076 Tübingen , Germany.
J Chem Inf Model ; 59(2): 636-643, 2019 02 25.
Article em En | MEDLINE | ID: mdl-30582814
ABSTRACT
Halogen bonding as a modern molecular interaction has received increasing attention not only in materials sciences but also in biological systems and drug discovery. Thus, there is a growing demand for fast, efficient, and easily applicable tailor-made tools supporting the use of halogen bonds in molecular design and medicinal chemistry. The potential strength of a halogen bond is dependent on several properties of the σ-hole donor, e.g., a (hetero)aryl halide, and the σ-hole acceptor, a nucleophile with n or π electron density. Besides the influence of the interaction geometry and the type of acceptor, significant tuning effects on the magnitude of the σ-hole can be observed, caused by different (hetero)aromatic scaffolds and their substitution patterns. The most positive electrostatic potential on the isodensity surface ( Vmax), representing the σ-hole, has been widely used as the standard descriptor for the magnitude of the σ-hole and the strength of the halogen bond. Calculation of Vmax using quantum-mechanical methods at a reasonable level of theory is time-consuming and thus not applicable for larger numbers of compounds in drug discovery projects. Herein we present a tool for the prediction of this descriptor based on a machine-learned model with a speedup of 5 to 6 orders of magnitude relative to MP2 quantum-mechanical calculations. According to the test set, the squared correlation coefficient is greater than 0.94.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Teoria Quântica / Descoberta de Drogas / Halogênios Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Teoria Quântica / Descoberta de Drogas / Halogênios Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article