We target the gatekeeper MET146 of c-Jun N-terminal kinase 3 (JNK3) to exemplify the applicability of X···S halogen bonds in molecular design using computational, synthetic, structural and biophysical techniques. In a designed series of aminopyrimidine-based inhibitors, we unexpectedly encounter a plateau of affinity. Compared to their QM-calculated interaction energies, particularly bromine and iodine fail to reach the full potential according to the size of their σ-hole. Instead, mutation of the gatekeeper residue into leucine, alanine, or threonine reveals that the heavier halides can significantly influence selectivity in the human kinome. Thus, we demonstrate that, although the choice of halogen may not always increase affinity, it can still be relevant for inducing selectivity. Determining the crystal structure of the iodine derivative in complex with JNK3 (4X21) reveals an unusual bivalent halogen/chalcogen bond donated by the ligand and the back-pocket residue MET115. Incipient repulsion from the too short halogen bond increases the flexibility of Cε of MET146, whereas the rest of the residue fails to adapt being fixed by the chalcogen bond. This effect can be useful to induce selectivity, as the necessary combination of methionine residues only occurs in 9.3% of human kinases, while methionine is the predominant gatekeeper (39%).

Halogen bonds have recently gained attention in life sciences and drug discovery. However, it can be difficult to harness their full potential, when newly introducing them into an established hit or lead structure by molecular design. A possible solution to overcome this problem is the use of halogen-enriched fragment libraries (HEFLibs), which consist of chemical probes that provide the opportunity to identify halogen bonds as one of the main features of the binding mode. Initially, we have suggested the HEFLibs concept when constructing a focused library for finding p53 mutant stabilizers. Herein, we broaden and extent this concept aiming for a general HEFLib comprising a huge diversity of binding motifs and, thus, increasing the applicability to various targets. Using the construction principle of feature trees, we represent each halogenated fragment by treating all simple to complex substituents as modifiers of the central (hetero)arylhalide. This approach allows us to focus on the proximal binding interface around the halogen bond and, thus, its integration into a network of interactions based on the fragment's binding motif. As a first illustrative example, we generated a library of 198 fragments that unifies a two-fold strategy: Besides achieving a diversity-optimized basis of the library, we have extended this “core” by structurally similar “satellite compounds” that exhibit quite different halogen bonding interfaces. Tuning effects, i.e., increasing the magnitude of the σ-hole, can have an essential influence on the strength of the halogen bond. We were able to implement this key feature into the diversity selection, based on the rapid and efficient prediction of the highest positive electrostatic potential on the electron isodensity surface, representing the σ-hole, by V max Pred.

Halogen bonds have become increasingly popular interactions in molecular design and drug discovery. One of the key features is the strong dependence of the size and magnitude of the halogen’s σ-hole on the chemical environment of the ligand. The term σ-hole refers to a region of lower electronic density opposite to a covalent bond, e.g., the C-X bond. It is typically (but not always) associated with a positive electrostatic potential in close proximity to the extension of the covalent bond. Herein, we use a variety of 30 nitrogen-bearing heterocycles, halogenated systematically by chlorine, bromine, or iodine, yielding 468 different ligands that are used to exemplify scaffold effects on halogen bonding strength. As a template interaction partner, we have chosen N-methylacetamide representing the ubiquitously present protein backbone. Adduct formation energies were obtained at a MP2/TZVPP level of theory. We used the local maximum of the electrostatic potential on the molecular surface in close proximity to the σ-hole, V S,max, as a descriptor for the magnitude of the positive electrostatic potential characterizing the tuning of the σ-hole. Free optimization of the complexes gave reasonable correlations with V S,max but was found to be of limited use because considerable numbers of chlorinated and brominated ligands lost their halogen bond or showed significant secondary interactions. Thus, starting from a close to optimal geometry of the halogen bond, we used distance scans to obtain the best adduct formation energy for each complex. This approach provided superior results for all complexes exhibiting correlations with R2 > 0.96 for each individual halogen. We evaluated the dependence of V S,max from the molecular surface onto which the positive electrostatic potential is projected, altering the isodensity values from 0.001 au to 0.050 au. Interestingly, the best overall fit using a third-order polynomial function (R2 = 0.99, RMSE = 0.562 kJ/mol) with rather smooth transitions between all halogens was obtained for V S,max calculated from an isodensity surface at 0.014 au.

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.

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