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

Abstract

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.

Critical Assessment of pH‐Dependent Lipophilicity Profiles of Small Molecules: Which One Should We Use and In Which Cases?

“…This affinity for water, at least for acidic compounds, was further demonstrated using the CH_strength descriptor (Figure 11b). This descriptor, calculated by Jazzy , predicts the hydrogen‐bond donor strength in carbon atoms [116] . The smaller CH_strength values indicate that for entries with d 3 <0.2, H‐bond donors are not primarily found on carbons.…”

“…The simplest regressions fit the distributions of data to a sigmoidal function, where the input values Table 1. List of the most influential structural descriptors [95,116,121,122] used for the Machine Learning classification models, their target molecules, and the divergence between the two populations from our dataset were determined using the WTT feature selection method by separating the populations with the conditional d 3 > 0.2.…”

“…This descriptor, calculated by Jazzy, predicts the hydrogen-bond donor strength in carbon atoms. [116] The smaller CH_strength values indicate that for entries with d 3 < 0.2, Hbond donors are not primarily found on carbons. Instead, they are found on other more electronegative heteroatoms.…”

“…We also added the free energies of hydration and hydrogen bond strengths computed using the new open-source tool 'Jazzy'. [116] The H-bond donor and acceptor strengths were obtained by calculating the partial charges of the hydrogen atoms and atoms with lone electron pairs, respectively, along with corrective terms. The free energy of hydration was calculated using the sum of the polar, apolar, and interaction terms.…”

“…The interaction term consists of a weighted sum of the amount of neighboring H-bond acceptor groups each atom has in a molecule. [116] We eliminated intercorrelated properties so that no descriptor had a correlation value of R 2 > 0.6 (Figure S1 and S2). After this filtration step, two different feature selection methods were tested to choose the best descriptors for the Machine Learning models.…”