Predicting the Magnitude of σ-Holes Using VmaxPred, a Fast and Efficient Tool Supporting the Application of Halogen Bonds in Drug Discovery

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 (V max), 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 V max 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.

Halogen Bonding Propensity in Solution: Direct Observation and Computational Prediction

“…The implementation of non-covalent interactions, such as hydrogen bonding, into supramolecular complex design in chemistry and biology is an attractive approach for molecular recognition. [1][2][3] Toward this pursuit, various avenues are being investigated to establish robust strategies that can encompass a wide variety of molecular design processes. Halogen bonding, a directional non-covalent interaction between a halogen atom (X) and a Lewis base (LB), [4][5][6][7] has been observed to facilitate strong and directional interactions in the solid state, solution, and gas phase and is well described with computational modeling.…”

Embracing the Diversity of Halogen Bonding Motifs in Fragment-Based Drug Discovery—Construction of a Diversity-Optimized Halogen-Enriched Fragment Library

“…The strong correlation of V max and the potential adduct formation energy (Politzer et al, 2013; Heidrich et al, 2018; Lange et al, 2019) and its demonstrated usefulness as descriptor for the magnitude of the σ-hole, is the basis for our choice of V max as an important parameter for our diversity assessment, which is centered on the XB interface. As an indicator for the diversity of our showcase HEFLib, we use the distribution of calculated V max values at the classical definition of the electron isodensity level of 0.001 au while including all possible tautomeric states with a net charge of zero.…”

“…Next evolutionary steps of the library are focused on three-dimensionality and even higher tuning values of V max . Since approximation of V max values can be efficiently performed using V max Pred (Heidrich et al, 2018), we plan to enrich fragments with greater σ-hole magnitudes by custom synthesis using carefully selected building blocks. Based on an in silico synthesis approach, vast numbers of potential halogenated fragments can be efficiently proposed and used as a starting point for a selection process as outlined herein.…”

“…Thereby defined nodes contain property values of atom count (Todeschini and Consonni, 2009), lone-pair electrostatic interaction (Cheng and Yuan, 2006; Todeschini and Consonni, 2009), information index on proton-neutron composition (Bonchev et al, 1976; Todeschini and Consonni, 2009), kappa shape index-related flexibility index (Kier, 1989; Hall and Kier, 2007; Todeschini and Consonni, 2009), edge connectivity index (Cash, 1995; Estrada, 1995; Todeschini and Consonni, 2009), and group electronegativity (Zhou et al, 2007; Todeschini and Consonni, 2009). Moreover, each node that encodes an aromatic halogen atom contains the information of the predicted V max value at the electron isodensity level of 0.020 au (Heidrich et al, 2018), which was derived by a machine-learned SVM model. Furthermore, atom types and topologies of the central aromatic ring are taken into consideration.…”

“…By treating each aromatic ring system with a connected heavier halogen, i.e., chlorine, bromine, or iodine, as the central element for molecular similarity assessment and all of its substituents as modifiers, we created a similarity measure that is tailor-made for emphasizing the influence of the local environment of the respective halogen on the possible interaction pattern of this “binding motif.” At the same time, the influence of more halogen-distant molecular features is decreased. This construction principle of a rooted tree, based on the abstraction of the molecular graph, was successfully implemented to predict V max (Heidrich et al, 2018) and is herein utilized for the diversity assessment of halogenated fragments. As a first showcase application, we compiled a library of 198 fragments that consists of a diversity-optimized “core” which is extended by structurally similar, but with regard to their halogen bonding interface quite different, “satellite compounds.” When applying this diversity-optimized HEFLib to a variety of different target proteins, we propose that the experimental results can inspire a better understanding of geometric requirements and tuning effects of halogen bonds.…”