Protein hydration and druggability

Quantum Methods in Drug Discovery: An Introduction to the Theoceptor (Theoretical Receptor) Approach.

Theoceptor method can be applied to X-ray crystallography- or cryo-electron microscopy-derived structures, can be applied equally to covalent and noncovalent binders, and can provide excellent predictions for the changes in activity between matched molecular pairs.

Protein-Protein Interactions with the Fragment Molecular Orbital Method.

Benchmark Study for Calculations of pK a Values of Metal Ligands in Proteins

We have compared the performance of 64 different computational methods, based on combined quantum mechanical (QM) and molecular mechanical (QM/MM) or QM-cluster calculations in a continuum solvent, to estimate the acid constant (pK a) of metal-bound ligands in proteins. As a calibration set, we use 12 experimental pK a values from six different proteins that involve Zn2+, Fe3+, or Fe4+. We employ two different density functional theory (DFT) methods (TPSS and B3LYP), two basis sets (def2-SV(P) and def2-TZVPD), QM regions of three different sizes (∼40, ∼100, and ∼350 atoms), relaxed or fixed surroundings, and three different values of the dielectric constant of the continuum-solvation model (ε = 4, 20, or 80). The results clearly show that QM-cluster+continuum-solvation is much better than QM/MM. In general, the most accurate results are obtained with ε = 80 and the minimal QM region. The two DFT methods, the two basis sets, and relaxing or fixing the surroundings give similar results. The best-performing method is TPSS with the minimal QM region, def2-TZVPD, relaxed surroundings, and ε = 80, yielding a mean absolute deviation (after removal of a systematic error of 11.6 pK a units, pu) of 2.0 pu and a maximum deviation of 5.0 pu. The coefficient of determination (R 2) and Kendall’s τ with respect to the experimental pK a values are both 0.64, while Spearman’s rank correlation coefficient is 0.78. This level of accuracy should be sufficient to reliably determine the protonation states of metal-bound ligands in QM-based studies of enzymatic reaction mechanisms.

Adventures in Chemical Space: From Targets to Drugs to Design Principles.

This manuscript traces my journey through computational medicinal chemistry, showing how mechanistic and structural insights and physicochemical reasoning enable the translation of challenging targets into drugs and general design principles. The central theme, conformational analysis, links on-target potency via pre-organization of the bioactive conformation with physics-based physicochemical property prediction. This strategy can unlock dramatic gains in lipophilic efficiency and pharmacokinetic properties through the judicious addition of single atoms. This principle is extended to proteins, particularly kinases, where discrete conformational states can explain binding modes and kinetics. Binding to inactive conformations is linked to slow-on/ slow-off kinetics and can be engineered through ligand design or protein mutations. The manuscript summarizes principles of oral bioavailability in beyond-Rule-of-5 (bRo5) space, highlighting neutral polarity as a key determinant of permeability and exposure. Marketed oral bRo5 drugs and the lead optimization campaigns of first in class representatives converge on a polarity-lipophilicity sweet spot. Finally, nonclassical zwitterions represent a general design strategy to reconcile low lipophilicity with high permeability, supported by strong agreement between computation and experiment.

Open-Sourced In Silico Drug Screening.

This chapter describes a structure-based computational approach to perform high-throughput ligand screens of chemical libraries using open-source software programs and illustrates this workflow with the enzymatic molecular target NAD(P)H:quinone oxidoreductase1 (NQO1), which is overexpressed in a number of human solid tumors.

Drugs solubility parameter prediction using modified Cubic Plus Chain equation of state.

Accurate prediction of drug solubility parameter is essential for understanding drug-solvent interactions and guiding formulation design. Experimental determination is often limited by low solubility and measurement complexity; thus, reliable predictive methods are required. In this work, the main objective is to estimate drug solubility parameter using a modified Cubic-Plus-Chain (CPC) equation of state (EoS). This work emphasizes that a simple cubic-based EoS can effectively model complex pharmaceutical systems and can be readily implemented in commercial simulation software. Accordingly, the CPC EoS was coupled with the Two-State Theory (TST) to enhance the description of complex molecular interactions, such as association in drug systems. The primary motivation for employing the TST instead of Wertheim's theory lies in the difficulty of defining the number and types of association sites in large and complex drug molecules. In contrast to Wertheim's approach, TST does not require explicit specification of association sites, which greatly simplifies the modeling of associating components. Moreover, the proposed CPC-TST model retains analytical solvability comparable to conventional cubic EoS, further enhancing its practicality for implementation in commercial simulation tools. The model parameters were determined using available experimental solubility data for selected drug-solvent systems. The solubility of drugs in various pure and mixed solvents was then estimated with the CPC-TST model. The analysis shows that accounting for a binary interaction parameter that varies with temperature leads to a notable improvement in the model's capability, yielding the average RMSD of 0.034 and AAD% of 0.168. The CPC-TST model was employed to predict the solubility parameter of several drug compounds. The predicted values were compared with those obtained from regression-based, group-contribution methods, and PC-SAFT EoS. The CPC-TST model demonstrated good accuracy in predicting solubility parameter, particularly for strongly associating systems, while maintaining low computational complexity. These results highlight the potential of the CPC-TST model as a robust and efficient alternative for modeling solubility behavior in pharmaceutical systems.