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. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

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. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

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 Mycobacterium tuberculosis bacilli exhibiting phenotypic tolerance to most antibiotics in the standard treatment regimen. Confounding this problem is the increasing incidence of heritable multidrug-resistant M. tuberculosis 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 M. tuberculosis Both families of compounds depleted M. tuberculosis of intrabacterial magnesium. Complete or partial resistance to both chemotypes arose from mutations in the putative mycobacterial Mg2+/Co2+ ion channel, CorA. Excess extracellular Mg2+, but not other divalent cations, diminished the compounds' cidality against replicating M. tuberculosis These findings establish depletion of intrabacterial magnesium as an antimicrobial mechanism of action and show that M. tuberculosis magnesium homeostasis is vulnerable to disruption by structurally diverse, nonchelating, drug-like compounds.IMPORTANCE Antimycobacterial agents might shorten the course of treatment by reducing the number of phenotypically tolerant bacteria if they could kill M. tuberculosis in diverse metabolic states. Here we report two chemically disparate classes of agents that kill M. tuberculosis both when it is replicating and when it is not. Under replicating conditions, the tricyclic 4-hydroxyquinolines and a barbituric acid analogue deplete intrabacterial magnesium as a mechanism of action, and for both compounds, mutations in CorA, a putative Mg2+/Co2+ transporter, conferred resistance to the compounds when M. tuberculosis was under replicating conditions but not under nonreplicating conditions, illustrating that a given compound can kill M. tuberculosis in different metabolic states by disparate mechanisms. Targeting magnesium metallostasis represents a previously undescribed antimycobacterial mode of action that might cripple M. tuberculosis in a Mg2+-deficient intraphagosomal environment of macrophages.