Non-active Molecules Research Articles

Transmission of mobility via cooperative mechanisms in soft active matter

When supercooled, liquid's viscosity increases dramatically as the glass transition temperature is approached. Although the physical origin of this behavior is still not understood, it is now well established that the addition of a few activated particles is able to reverse that increase in viscosity. Here we further raise the question of a limit in that fluidization process and of the differences between the fluidized liquid and its viscous counterpart. The results show that a few percent active molecules are enough to trigger a phase transition leading to diffusion coefficients typical of liquids while the medium retains cooperative properties of the viscous phase. The similarity between cooperative properties of the active and non-active molecules suggests that the mobility of active molecules is transmitted to inactive ones via the medium's cooperative mechanisms, a result in agreement with facilitation theories. This result is then confirmed by the compared behavior of the distinct Van Hove correlation functions of most mobile active and non-active molecules. Interestingly enough, in our simulations, the cooperative mechanisms are not induced or related to a decrease in the excitation concentration.

Predicting active compounds for lung cancer based on quantitative structure-activity relationships

<span lang="EN-US">Recently, advancements in computational and artificial intelligence (AI) methods have contributed in improving research results in the field of drug discovery. In fact, machine learning techniques have proven to be especially effective in this regard, aiding in the development of new drug variants and enabling more precise targeting of specific disease mechanisms. In this paper, we propose to use a quantitative structure-activity relationship-based approach for predicting active compounds related to non-small cell lung cancer. Our approach uses a neural network classifier that learns from sequential structures and chemical properties of molecules, as well as a gradient boosting tree classifier to conduct comparative analysis. To evaluate the contribution of each feature, we employ Shapley additive explanations (SHAP) summary plots to perform features selection. Our approach involves a dataset of active and non-active molecules collected from ChEMBL database. Our results show the effectiveness of the proposed approach when it comes to predicting accurately active compounds for lung cancer. Furthermore, our comparative analysis reveals important chemical structures that contribute to the effectiveness of the compounds. Thus, the proposed approach can greatly enhance the drug discovery pipeline and may lead to the development of new and effective treatments for lung cancer.</span>

Decoys Selection in Benchmarking Datasets: Overview and Perspectives.

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

Common features of antibacterial compounds: an analysis of 104 compounds library

Antibacterial compounds are one of the essential classes of clinically important drugs. High throughput screening allowed revealing potential antibiotics active towards any molecular target in bacterial cell. We used a library of 9820 organic compounds with highly diversified structures to screen for antibacterial activity. As the result of automated screening, 103 compounds were found to possess antibacterial activity against Escherichia coli. The properties of these compounds were compared with those of initial library. Non-linear Kohonen mapping was used to analyze the differences between non-active molecules from initial library, identified antibacterial hits and compounds with reported antibacterial activity. It was found that identified antibacterial compounds are located in the separated area of chemical space. It can be therefore suggested that these molecules belong to novel classes of antibacterial compounds and could be studied further.

Investigation of Structure-Activity Relationship Between Chemical Structure and CCR5 Anti HIV-1 Activity in a Class of 1-[N-(Methyl)-N- (Phenylsulfonyl)Amino]-2-(Phenyl)-4-[4-(Substituted)Piperidin-1-yl]Butane Derivatives: The Electronic-Topological Approach

The relationship between chemical structure and CCR5 anti HIV-1 activity was investigated in the series of 1-[N-(Methyl)-N-(phenylsulfonyl)amino]-2-(phenyl)-4-[4-(substituted) piperidin-1-yl]butanes derivatives including 114 molecules by using the Electron-Topological Method (ETM). In the frameworks of this approach, its input data were taken as the results of conformational and quantum-mechanical calculations. Conformational analysis and quantum-chemical calculations were carried out for each molecule. Then molecular fragments being specific for active molecules and non-active molecules were revealed by using ETM. The result of testing showed the high ability of ETM in predicting the activity and inactivity investigated series. In order to classify and to develop a model for those molecules, cluster and discriminant analyses are conducted. First, cluster analysis is implemented in order to classify similar molecules into the groups. Then, discriminant analysis is used to construct models including descriptors. By doing so, two obtained discriminant functions segregate those molecules into three different groups by using the descriptors called EHOMO, Dipole Moment and SEZPE.

Toward a Better Pharmacophore Description of P-Glycoprotein Modulators, Based on Macrocyclic Diterpenes from Euphorbia Species

Multidrug resistance related to the increased expression of P-glycoprotein (P-gp) by cancer cells is the major contributor for the failure of chemotherapeutic treatments. Starting from pharmacophores and data already published and in macrocyclic diterpenes isolated from Euphorbia species, a comprehensive study of pharmacophore definitions of features was performed in order to obtain a new improved four-point pharmacophore able to detect literature and in-house modulators and simultaneously specific enough to avoid the detection of most nonactive molecules in a universe of 152 (literature), 74 (in-house), and 46 (inactive) molecules. This pharmacophore detects 84.2% of the molecules described in the literature, along with 100% detection of in-house isolated compounds and 19.5% of false positives. The importance of the hydrophobic and electron acceptor moieties as essential features for recognition of different molecules by the P-gp drug-binding site is clarified. The best combination of acceptor, donor, hydrophobic, and aromatic characteristics that contribute for the increased selectivity shown by the described pharmacophore is evaluated, and the protonation state of the molecules is also addressed.

Intracellular Processing of Proteins Mediated by Biodegradable Polyelectrolyte Capsules

Multilayer polyelectrolyte capsules made by layer-by-layer assembly of oppositely charged biodegradable polyelectrolytes were filled with a model of a nonactive prodrug, a self-quenched fluorescence-labeled protein. After capsule uptake by living cells, the walls of the capsules were actively degraded and digested by intracellular proteases. Upon capsule wall degradation, intracellular proteases could reach the protein cargo in the cavity of the capsules. Enzymatic fragmentation of the self-quenched fluorescence-labeled protein by proteases led to individual fluorescence-labeled peptides and thus revoked self-quenching of the dye. In this way nonactive (nonfluorescent) molecules were converted into active (fluorescent) molecules. The data demonstrates that biodegradable capsules are able to convert nonactive molecules (prodrugs) to active molecules (drugs) specifically only inside cells where appropriate enzymes are at hand. In this way only cargo inside the capsules reaching cells is activated, but not the cargo in capsules which remain extracellular. The peptide fragments undergo further processing inside the cells, leading ultimately to exocytosis.

Possible accumulation of non-active molecules of catalase and superoxide dismutase in S. cerevisiae cells under hydrogen peroxide induced stress

Abstract The effect of hydrogen peroxide on the activities of catalase and superoxide dismutase (SOD) in S. cerevisiae has been studied under different experimental conditions: various H2O2 concentrations, time exposures, yeast cell densities and media for stress induction. The yeast treatment with 0.25–0.50 mM H2O2 led to an increase in catalase activity by 2–3-fold. At the same time, hydrogen peroxide caused an elevation by 1.6-fold or no increase in SOD activity dependently on conditions used. This effect was cancelled by cycloheximide, an inhibitor of protein synthesis in eukaryotes. Weak elevation of catalase and SOD activities in cells treated with 0.25–0.50 mM H2O2 found in this study does not correspond to high level of synthesis of the respective enzyme molecules observed earlier by others. It is well known that exposure of microorganisms to low sublethal concentrations of hydrogen peroxide leads to the acquisition of cellular resistance to a subsequent lethal oxidative stress. Hence, it makes possible to suggest that S. cerevisiae cells treated with low sublethal doses of hydrogen peroxide accumulate non-active stress-protectant molecules of catalase and SOD to survive further lethal oxidant concentrations.

Computational detection of the binding-site hot spot at the remodeled human growth hormone-receptor interface.

A hierarchical computational approach is used to identify the engineered binding-site cavity at the remodeled intermolecular interface between the mutants of human growth hormone (hGH) and the extracellular domain of its receptor (hGHbp). Multiple docking simulations are conducted with the remodeled hGH-hGHbp complex for a panel of potent benzimidazole-containing inhibitors that can restore the binding affinity of the wild-type complex, and for a set of known nonactive small molecules that contain different heterocyclic motifs. Structural clustering of ligand-bound conformations and binding free-energy calculations, using the AMBER force field and a continuum solvation model, can rapidly locate and screen numerous ligand-binding modes on the protein surface and detect the binding-site hot spot at the intermolecular interface. Structural orientation of the benzimidazole motif in the binding-site cavity closely mimics the position of the hot spot residue W104 in the crystal structure of the wild-type complex, which is recognized as an important structural requirement for restoring binding affinity. Despite numerous pockets on the protein surface of the mutant hGH-hGHbp complex, the binding-site cavity presents the energetically favorable hot spot for the benzimidazole-containing inhibitors, whereas for a set of nonactive molecules, the lowest energy ligand conformations do not necessarily bind in the engineered cavity. The results reveal a dominant role of the intermolecular van der Waals interactions in providing favorable ligand-protein energetics in the redesigned interface, in agreement with the experimental and computational alanine scanning of the hGH-hGHbp complex.

Parity-violating effects of weak interactions in molecules

Effects of neutral-current parity-violating weak interactions in molecules are investigated. Asymmetries in the spectra of optically active mirror systems are estimated. The activity induced by weak interactions in rotational and vibrational transitions of nonactive molecules is computed.