There is a need for improved and generally applicable scoring functions for fragment-based approaches to ligand design. Here, we evaluate the performance of a computationally efficient model for inhibitory activity estimation, which is composed only of multipole electrostatic energy and dispersion energy terms that approximate long-range ab initio quantum mechanical interaction energies. We find that computed energies correlate well with inhibitory activity for a compound series with varying substituents targeting two subpockets of the binding site of Trypanosoma brucei pteridine reductase 1. For one subpocket, we find that the model is more predictive for inhibitory activity than the ab initio interaction energy calculated at the MP2 level. Furthermore, the model is found to outperform a commonly used empirical scoring method. Finally, we show that the results for the two subpockets can be combined, which suggests that this simple nonempirical scoring function could be applied in fragment–based drug design.

A dispersion function D_as in the form of a damped atom–atom asymptotic expansion fitted to ab initio dispersion energies from symmetry-adapted perturbation theory was improved and extended to systems containing heavier halogen atoms. To illustrate its performance, the revised D_as function was implemented in the multipole first-order electrostatic and second-order dispersion (MED) scoring model. The extension has allowed applications to a much larger set of biocomplexes than it was possible with the original D_as. A reasonable correlation between MED and experimentally determined inhibitory activities was achieved in a number of test cases, including structures featuring nonphysically shortened intermonomer distances, which constitute a particular challenge for binding strength predictions. Since the MED model is also computationally efficient, it can be used for reliable and rapid assessment of the ligand affinity or multidimensional scanning of amino acid side-chain conformations in the process of rational design of novel drugs or biocatalysts.

Development and binding affinity predictions of inhibitors targeting protein–protein interactions (PPI) still represent a major challenge in drug discovery efforts. This work reports application of a predictive non-empirical model of inhibitory activity for PPI inhibitors, exemplified here for small molecules targeting the menin–mixed lineage leukemia (MLL) interaction. Systematic ab initio analysis of menin–inhibitor complexes was performed, revealing the physical nature of these interactions. Notably, the non-empirical protein–ligand interaction energy comprising electrostatic multipole and approximate dispersion terms (E⁽¹⁰⁾ _El,MTP + E_Das) produced a remarkable correlation with experimentally measured inhibitory activities and enabled accurate activity prediction for new menin–MLL inhibitors. Importantly, this relatively simple and computationally affordable non-empirical interaction energy model outperformed binding affinity predictions derived from commonly used empirical scoring functions. This study demonstrates high relevance of the non-empirical model we developed for binding affinity prediction of inhibitors targeting protein–protein interactions that are difficult to predict using empirical scoring functions.

Halogenated ligands are nowadays commonly designed in order to increase their potency against protein targets. Although novel computational methods of evaluating the affinity of such halogenated inhibitors have emerged, they still lack the sufficient accuracy, which is especially noticeable in the case of empirical scoring functions, being the method of choice in the drug design process. Here, we evaluated a series of halogenated inhibitors of phosphodiesterase type 5 with ab initio methods, revealing the physical nature of ligand binding and determining the components of interaction energy that are essential for proper inhibitor ranking. In particular, a nonempirical scoring model combining long-range contributions to the interaction energy provided a significant correlation with experimental binding potency, outperforming a number of commonly used empirical scoring functions. Considering the low computational cost associated with remarkable predictive abilities of the aforementioned model, it could be used for rapid assessment of the ligand affinity in the process of rational design of novel halogenated compounds.

This work aims at the theoretical description of EphA2-ephrin A1 inhibition by small molecules. Recently proposed ab initio-based scoring models, comprising long-range components of interaction energy, is tested on lithocholic acid class inhibitors of this protein–protein interaction (PPI) against common empirical descriptors. We show that, although limited to compounds with similar solvation energy, the ab initio model is able to rank the set of selected inhibitors more effectively than empirical scoring functions, aiding the design of novel compounds.

Numerous empirical scoring schemes are arbitrary, yielding contradictory results for different protein inhibitor families. This study is aimed to validate the predictive potential of alternative nonempirical model of inhibitory activity based on first principle quantum chemical results for new class of small molecules targeting the menin-mixed lineage leukemia (MLL) interaction. Presented nonempirical interaction energy model comprising long-range electrostatic multipole and approximate dispersion terms is universal and computationally affordable, outperforming a number of commonly used empirical scoring functions, especially when combined with analysis of solvation energy.