TL;DR
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.
Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.
Molecular docking is an important step in drug discovery, enabling the evaluation of receptor-ligand affinity while reducing experimental costs and increasing the number of possible tests. However, the high computational cost associated with molecular docking remains a limiting factor that can restrict both the experimental precision and the scale of the problems being addressed. To improve the future applicability of molecular docking, recent works have proposed the use of quantum algorithms based on Gaussian Boson Sampling quantum computers and also gate-based quantum computers. In this work, we propose the use of Quantum Circuit Evolution (QCE) for solving the molecular docking problem, a gate based and gradient-free quantum evolutionary method whose evolution is driven by the random application of unitary operations to a quantum circuit. The proposed algorithm demonstrated the ability to find the best solution to the problem in fewer steps than the methods presented in previous studies, exhibiting fast and stable convergence.
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.
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.
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.