Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design

Bioisosteres are molecules that differ in substituents but still have very similar shapes. Bioisosteric replacements are ubiquitous in modern drug design, where they are used to alter metabolism, change bioavailability, or modify activity of the lead compound. Prediction of relative affinities of bioisosteres with computational methods is a long-standing task; however, the very shape closeness makes bioisosteric substitutions almost intractable for computational methods, which use standard force fields. Here, we design a quantum mechanical (QM)-cluster approach based on the GFN2-xTB semi-empirical quantum-chemical method and apply it to a set of H -> F bioisosteric replacements. The proposed methodology enables advanced prediction of biological activity change upon bioisosteric substitution of -H with -F, with the standard deviation of 0.60 kcal/mol, surpassing the ChemPLP scoring function (0.83 kcal/ mol), and making QM-based Delta Delta G estimation comparable to similar to 0.42 kcal/mol standard deviation of in vitro experiment. The speed of the method and lack of tunable parameters makes it affordable in current drug research.

Automated summary

Bioisosteres are molecules with different substituents but similar shapes. They are widely used in drug design to modify metabolism, bioavailability, and activity. However, predicting the affinity of bioisosteres with computational methods has been challenging due to their similarity to standard force fields. In this study, a quantum mechanical (QM)-cluster approach based on the GFN2-xTB method was developed and successfully applied to predict the biological activity change of H -> F bioisosteric replacements. The method showed superior accuracy compared to the ChemPLP scoring function and comparable to in vitro experiments, with a standard deviation of 0.60 kcal/mol.