Do electrostatic interactions make a difference in physics-based AutoDock4 scoring function?

Physics-based scoring function AutoDock4 is one of the most successfully applied tools in the area of structure-based drug design. However, current scoring functions are still far from being perfect. In a recent work highlighting the strengths and deficiencies of current scoring functions, we discovered that the residual error of ΔG predictions made by AutoDock4 is highly correlated to the presence of formally charged fragments in a ligand.

On hidden anisotropy of formally charged fragments.

The proper and precise reproduction of the molecular electrostatic potential (MEP) is crucial to describe correctly electrostatic interactions in molecular modeling. Most of the classical molecular mechanics force fields for biomolecules and drug-like molecules use the atom-centered point charges to describe MEP. However, it has been systematically pointed out in literature that such an approximation is not always enough, and some groups, like amino group or heavy halogens, require the use of anisotropic model for better description of their MEP.

Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features.

Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have been formulated.

Is an Inductive Effect Explicit Account Required for Atomic Charges Aimed at Use within the Force Fields?

Polarization and inductive effects are the concepts that have been widely used in qualitative and even quantitative descriptions of experimentally observed properties in chemistry. The polarization effect has proven to be important in cases of biomolecular modeling though still the vast majority of molecular simulations use the classical non-polarizable force fields. In the last few decades, a lot of effort has been put into promoting the polarization effect and incorporating it into modern force fields and charge calculation methods.

Iterative Solvers for Empirical Partial Atomic Charges: Breaking the Curse of Cubic Numerical Complexity.

Rational drug design involves a vast amount of computations to get thermodynamically reliable results and often relies on atomic charges as a means to model electrostatic interactions within the system. Computational inefficiency often hampers the development of new and wider dissemination of the known methods; thus, any source to speed up the calculations without a sacrifice in quality is warranted. At the heart of many empirical methods of calculating atomic charges is the solution of a system of linear algebraic equations (SLAE).