Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Polypharmacology Modelling Using Proteochemometrics (PCM): Recent Methodological Developments, Applications to Target Families, and Future Prospects

Peer reviewe

Development of Covalent Ligands for G Protein-Coupled Receptors: A Case for the Human Adenosine A3 Receptor

The development of covalent ligands for G protein-coupled receptors (GPCRs) is not a trivial process. Here, we report a streamlined workflow thereto from synthesis to validation, exemplified by the discovery of a covalent antagonist for the human adenosine A3 receptor (hA3AR). Based on the 1H,3H-pyrido[2,1-f]purine-2,4-dione scaffold, a series of ligands bearing a fluorosulfonyl warhead and a varying linker was synthesized. This series was subjected to an affinity screen, revealing compound 17b as the most potent antagonist. In addition, a nonreactive methylsulfonyl derivative 19 was developed as a reversible control compound. A series of assays, comprising time-dependent affinity determination, washout experiments, and [35S]GTPγS binding assays, then validated 17b as the covalent antagonist. A combined in silico hA3AR-homology model and site-directed mutagenesis study was performed to demonstrate that amino acid residue Y2657.36 was the unique anchor point of the covalent interaction. This workflow might be applied to other GPCRs to guide the discovery of covalent ligands.Medicinal Chemistr

Synthesis and Pharmacological Evaluation of Triazolopyrimidinone Derivatives as Noncompetitive, Intracellular Antagonists for CC Chemokine Receptors 2 and 5

CC chemokine receptors 2 (CCR2) and 5 (CCR5) are involved in many inflammatory diseases; however, most CCR2 and CCR5 clinical candidates have been unsuccessful. (Pre)clinical evidence suggests that dual CCR2/CCR5 inhibition might be more effective in the treatment of such multifactorial diseases. In this regard, the highly conserved intracellular binding site in chemokine receptors provides a new avenue for the design of multitarget ligands. In this study, we synthesized and evaluated the biological activity of a series of triazolopyrimidinone derivatives in CCR2 and CCR5. Radioligand binding assays first showed that they bind to the intracellular site of CCR2, and in combination with functional assays on CCR5, we explored structure−affinity/activity relationships in both receptors. Although most compounds were CCR2-selective, 39 and 43 inhibited β-arrestin recruitment in CCR5 with high potency. Moreover, these compounds displayed an insurmountable mechanism of inhibition in both receptors, which holds promise for improved efficacy in inflammatory diseases.Medicinal Chemistr

Kinetics of human cannabinoid 1 (CB1) receptor antagonists: Structure-kinetics relationships (SKR) and implications for insurmountable antagonism

While equilibrium binding affinities and in vitro functional antagonism of CB1 receptor antagonists have been studied in detail, little is known on the kinetics of their receptor interaction. In this study, we therefore conducted kinetic assays for nine 1-(4,5-diarylthiophene-2-carbonyl)-4-phenylpiperidine-4-carboxamide derivatives and included the CB1 antagonist rimonabant as a comparison. For this we newly developed a dual-point competition association assay with [3H]CP55940 as the radioligand. This assay yielded Kinetic Rate Index (KRI) values from which structure-kinetics relationships (SKR) of hCB1 receptor antagonists could be established. The fast dissociating antagonist 6 had a similar receptor residence time (RT) as rimonabant, i.e. 19 and 14 min, respectively, while the slowest dissociating antagonist (9) had a very long RT of 2222 min, i.e. pseudo-irreversible dissociation kinetics. In functional assays, 9 displayed insurmountable antagonism, while the effects of the shortest RT antagonist 6 and rimonabant were surmountable. Taken together, this study shows that hCB1 receptor antagonists can have very divergent RTs, which are not correlated to their equilibrium affinities. Furthermore, their RTs appear to define their mode of functional antagonism, i.e. surmountable vs. insurmountable. Finally, based on the recently resolved hCB1 receptor crystal structure, we propose that the differences in RT can be explained by a different binding mode of antagonist 9 from short RT antagonists that is able to displace unfavorable water molecules. Taken together, these findings are of importance for future design and evaluation of potent and safe hCB1 receptor antagonists.Medicinal Chemistr

The development of the advanced web shop based on purchase history

The goal of thesis is to develop a typical web shop application with some additional functionality. This functionality enables web shop customers to browse products in a more efficient way and thus makes shop more profitable. For this purpose, we developed a specific mechanism that handles product presentation in customer adapted way. First we describe technologies used for development. Programing language C# is presented shortly as well as some other frameworks (ASP.net, Entity framework,), libraries (LINQ) and other web technologies (HTML, CSS, AJAX). For storing and manipulating data a database with tables in MS SQL database is created. Furthermore we take a look at requirements, idea and logic of solution. We present solution design and present how specific functionality behaves in case of different user types. We present a solution analysis where a comparison with other similar solutions and user tests are shown. Finally we discuss problems during the development and possibilities about the future improvements

Successive statistical and structure-based modeling to identify chemically novel kinase inhibitors

Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of 50 value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well.Toxicolog

Artificial intelligence in biological activity prediction

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.This study was supported by the European Commission through project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408), and by the Portuguese FCT under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020.info:eu-repo/semantics/publishedVersio

A Cross-Site Analysis of Neotropical Bird Hunting Profiles

© The Author(s) 2017. Subsistence hunting of neotropical birds is common and widespread in the tropical forests of Latin America. Although its sustainability under different scenarios is subject to debate, hunting has already contributed to the decline and local extirpation of several taxa and is considered to be a significant threat to a range of large-bodied species. Gaining a better understanding of the variability of hunting patterns, as well as the factors that can potentially be used to predict them, is important if we are to develop conservation strategies that target the species most likely to be experiencing declines. In this article, we examine the avian hunting profiles of 65 communities in the neotropics. We describe their variability and look at the relationship between a hunting profile and (a) its geographical location, (b) the community’s age, (c) the community’s population size, and (d) the year in which the survey was carried out. We find that there is a significant but weak relationship between a community’s geographic location and the composition of its bird hunting profile, and that prey profiles can be considerably different even among close neighbors. We found no relationship between a community’s age or size and the mean biomass of bird prey hunted. Our results challenge the assumption that older and larger settlements have a predictable impact upon avian prey communities and suggest that cultural preferences or the starting availability of prey species can change rapidly over short distances