How to Model Inter- and Intramolecular Hydrogen Bond Strengths with Quantum Chemistry

Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules

Hydrogen bonding is an interaction of great importance in drug discovery and development as it may significantly affect chemical and biological processes including the interaction of small molecules with other molecules, proteins, and membranes. In particular, hydrogen bonding can impact drug-like properties such as target affinity and oral availability which are critical to developing effective pharmaceuticals, and therefore, numerous methods for the calculation of properties such as hydrogen-bond strengths, free energy of hydration, or water solubility have been proposed over time. However, the accessibility to efficient methods for the predictions of such properties is still limited. Here, we present the development of Jazzy, an open-source tool for the prediction of hydrogen-bond strengths and free energies of hydration of small molecules. Jazzy also allows the visualisation of hydrogen-bond strengths with atomistic resolution to support the design of compounds with desired properties and the interpretation of existing data. The tool is described in its implementation, parameter fitting, and validation against two data sets of experimental hydration free energies. Jazzy is also applied against two chemical series of bioactive compounds to show that hydrogen-bond strengths can be used to understand their structure-activity relationships. Results from the validations highlight the strengths and limitations of Jazzy, and suggest its suitability for interactive design, screening, and machine-learning featurisation.

A Quantum Mechanics-Based Method to Predict Intramolecular Hydrogen Bond Formation Reflecting P-glycoprotein Recognition

Passive membrane permeability and an active transport process are key determinants for penetrating the blood-brain barrier. P-glycoprotein (P-gp), a well-known transporter, serves as the primary gatekeeper, having broad substrate specificity. A strategy to increase passive permeability and impair P-gp recognition is intramolecular hydrogen bonding (IMHB). 3 is a potent brain penetrant BACE1 inhibitor with high permeability and low P-gp recognition, although slight modifications to its tail amide group significantly affect P-gp efflux. We hypothesized that the difference in the propensity to form IMHB could impact P-gp recognition. Single-bond rotation at the tail group enables both IMHB forming and unforming conformations. We developed a quantum-mechanics-based method to predict IMHB formation ratios (IMHBRs). In a given data set, IMHBRs accounted for the corresponding temperature coefficients measured in NMR experiments, correlating with P-gp efflux ratios. Furthermore, the method was applied in hNK2 receptor antagonists, demonstrating that the IMHBR could be applied to other drug targets involving IMHB.

Dihydrogen Bonding-Seen through the Eyes of Vibrational Spectroscopy

In this work, we analyzed five groups of different dihydrogen bonding interactions and hydrogen clusters with an H3+ kernel utilizing the local vibrational mode theory, developed by our group, complemented with the Quantum Theory of Atoms-in-Molecules analysis to assess the strength and nature of the dihydrogen bonds in these systems. We could show that the intrinsic strength of the dihydrogen bonds investigated is primarily related to the protonic bond as opposed to the hydridic bond; thus, this should be the region of focus when designing dihydrogen bonded complexes with a particular strength. We could also show that the popular discussion of the blue/red shifts of dihydrogen bonding based on the normal mode frequencies is hampered from mode-mode coupling and that a blue/red shift discussion based on local mode frequencies is more meaningful. Based on the bond analysis of the H3+(H2)n systems, we conclude that the bond strength in these crystal-like structures makes them interesting for potential hydrogen storage applications.

Assessment of Bioactivity-Modulating Pseudo-Ring Formation in Psilocin and Related Tryptamines

Psilocybin (1) is the major alkaloid found in psychedelic mushrooms and acts as a prodrug to psilocin (2, 4‐hydroxy‐N,N‐dimethyltryptamine), a potent psychedelic that exerts remarkable alteration of human consciousness. In contrast, the positional isomer bufotenin (7, 5‐hydroxy‐N,N‐dimethyltryptamine) differs significantly in its reported pharmacology. A series of experiments was designed to explore chemical differences between 2 and 7 and specifically to test the hypothesis that the C‐4 hydroxy group of 2 significantly influences the observed physical and chemical properties through pseudo‐ring formation via an intramolecular hydrogen bond (IMHB). NMR spectroscopy, accompanied by quantum chemical calculations, was employed to compare hydrogen bond behavior in 4‐ and 5‐hydroxylated tryptamines. The results provide evidence for a pseudo‐ring in 2 and that sidechain/hydroxyl interactions in 4‐hydroxytryptamines influence their oxidation kinetics. We conclude that the propensity to form IMHBs leads to a higher number of uncharged species that easily cross the blood‐brain barrier, compared to 7 and other 5‐hydroxytryptamines, which cannot form IMHBs. Our work helps understand a fundamental aspect of the pharmacology of 2 and should support efforts to introduce it (via the prodrug 1) as an urgently needed therapeutic against major depressive disorder.

Bayer's in silico ADMET platform: a journey of machine learning over the past two decades

Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here. we discuss the development of machine-learning (ML) approaches with special emphasis on data, descriptors, and algorithms. We show that high company internal data quality and tailored descriptors, as well as a thorough understanding of the experimental endpoints, are essential to the utility of our models. We discuss the recent impact of deep neural networks and show selected application examples.