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 ΔΔG estimation comparable 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.