The evolution of drug design at Merck Research Laboratories

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

This manuscript provides an overview of the current state of the art in terms of the molecular modelling of the thermophysical properties of fluids. It is intended to manage the expectations and serve as guidance to practising physical chemists, chemical physicists and engineers in terms of the scope and accuracy of the more commonly available intermolecular potentials along with the peculiarities of the software and methods employed in molecular simulations while providing insights on the gaps and opportunities available in this field. The discussion is focused around case studies which showcase both the precision and the limitations of frequently used workflows.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

A Reduced Generalized Force Field for Biological Halogen Bonds

The halogen bond (or X-bond) is a noncovalent interaction that is increasingly recognized as an important design tool for engineering protein-ligand interactions and controlling the structures of proteins and nucleic acids. In the past decade, there have been significant efforts to characterize the structure-energy relationships of this interaction in macromolecules. Progress in the computational modeling of X-bonds in biological molecules, however, has lagged behind these experimental studies, with most molecular mechanics/dynamics-based simulation methods not properly treating the properties of the X-bond. We had previously derived a force field for biological X-bonds (ffBXB) based on a set of potential energy functions that describe the anisotropic electrostatic and shape properties of halogens participating in X-bonds. Although fairly accurate for reproducing the energies within biomolecular systems, including X-bonds engineered into a DNA junction, the ffBXB with its seven variable parameters was considered to be too unwieldy for general applications. In the current study, we have generalized the ffBXB by reducing the number of variables to just one for each halogen type and show that this remaining electrostatic variable can be estimated for any new halogenated molecule through a standard restricted electrostatic potential calculation of atomic charges. In addition, we have generalized the ffBXB for both nucleic acids and proteins. As a proof of principle, we have parameterized this reduced and more general ffBXB against the AMBER force field. The resulting parameter set was shown to accurately recapitulate the quantum mechanical landscape and experimental interaction energies of X-bonds incorporated into DNA junction and T4 lysozyme model systems. Thus, this reduced and generalized ffBXB is more readily adaptable for incorporation into classical molecular mechanics/dynamics algorithms, including those commonly used to design inhibitors against therapeutic targets in medicinal chemistry and materials in biomolecular engineering.

SAMPL7: Host-guest binding prediction by molecular dynamics and quantum mechanics

Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges provide routes to compare chemical quantities determined using computational chemistry approaches to experimental measurements that are shared after the competition. For this effort, several computational methods have been used to calculate the binding energies of Octa Acid (OA) and exo-Octa Acid (exoOA) host-guest systems for SAMPL7. The initial poses for molecular dynamics (MD) were generated by molecular docking. Binding free energy calculations were performed using molecular mechanics combined with Poisson-Boltzmann or generalized Born surface area solvation (MMPBSA/MMGBSA) approaches. The factors that affect the utility of the MMPBSA/MMGBSA approaches including solvation, partial charge, and solute entropy models were also analyzed. In addition to MD calculations, quantum mechanics (QM) calculations were performed using several different density functional theory (DFT) approaches. From SAMPL6 results, B3PW91-D3 was found to overestimate binding energies though it was effective for geometry optimizations, so it was considered for the DFT geometry optimizations in the current study, with single-point energy calculations carried out with B2PLYP-D3 with double-, triple-, and quadruple-ζ level basis sets. Accounting for dispersion effects, and solvation models was deemed essential for the predictions. MMGBSA and MMPBSA correlated better to experiment when used in conjunction with an empirical/linear correction.

Tailored therapeutics based on 1,2,3-1H-triazoles: a mini review

Contemporary drug discovery approaches rely on library synthesis coupled with combinatorial methods and high-throughput screening to identify leads. However, due to the multitude of components involved, a majority of optimization techniques face persistent challenges related to the efficiency of synthetic processes and the purity of compound libraries. These methods have recently found an upgradation as fragment-based approaches for target-guided synthesis of lead molecules with active involvement of their biological target. The click chemistry approach serves as a promising tool for tailoring the therapeutically relevant biomolecules of interest, improving their bioavailability and bioactivity and redirecting them as efficacious drugs. 1,2,3-1H-Triazole nucleus, being a planar and biologically acceptable scaffold, plays a crucial role in the design of biomolecular mimetics and tailor-made molecules with therapeutic relevance. This versatile scaffold also forms an integral part of the current fragment-based approaches for drug design, kinetic target guided synthesis and bioorthogonal methodologies.

CHEMGENIE: integration of chemogenomics data for applications in chemical biology

Increasing amounts of biological data are accumulating in the pharmaceutical industry and academic institutions. However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of chemogenomics. We are integrating complementary data from both internal and external data sources into one chemogenomics database (Chemical Genetic Interaction Enterprise; CHEMGENIE). Here, we demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building.