Molecular Dynamics and Other HPC Simulations for Drug Discovery.

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs.

Fine tuning for success in structure-based virtual screening.

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site.

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time.

User-Friendly Quantum Mechanics: Applications for Drug Discovery.

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields.

Bactericidal Disruption of Magnesium Metallostasis in Mycobacterium tuberculosis Is Counteracted by Mutations in the Metal Ion Transporter CorA.

A defining characteristic of treating tuberculosis is the need for prolonged administration of multiple drugs. This may be due in part to subpopulations of slowly replicating or nonreplicating bacilli exhibiting phenotypic tolerance to most antibiotics in the standard treatment regimen. Confounding this problem is the increasing incidence of heritable multidrug-resistant A search for new antimycobacterial chemical scaffolds that can kill phenotypically drug-tolerant mycobacteria uncovered tricyclic 4-hydroxyquinolines and a barbituric acid derivative with mycobactericidal activity against both replicating and nonreplicating Both families of compounds depleted of intrabacterial magnesium.