Is conformation a fundamental descriptor in QSAR? A case for halogenated anesthetics

Discovery of novel anti-cyanobacterial allelochemicals by multi-conformational QSAR approach

Microcystis aeruginosa causes cyanobacterial harmful algal blooms (cHABs) in various freshwater environments. Due to global climate change, the cHABs have even spread to estuaries and coasts. Plant-derived flavones have been reported as allelochemicals that efficiently inhibit the growth of M. aeruginosa. Quantitative structure-activity relationship (QSAR) was applied to investigate the factors affecting the M. aeruginosa inhibitory activity of flavones, and to discover novel allelochemicals against M. aeruginosa. We constructed 2D and 3D-QSAR models based on the half maximum inhibitory concentration (IC₅₀) of 22 flavones against M. aeruginosa, using molecular descriptors from multiple stable conformations. Both models showed satisfactory performances (2D-QSAR: r²=0.899, q²=0.596, r_test²=0.801; 3D-QSAR: r²=0.810, q²=0.516, r_test²=0.897). The 2D-QSAR model indicates that the anti-cyanobacterial activity is positively correlated with minimum and maximum surface electrostatic potential, and negatively correlated with polarity index and polar surface area. Through the 3D-QSAR approach, electronegative hydroxyl groups in 5- and 4^'-position were favorable for the anti-cyanobacterial activity. In addition, we selected six untested flavones that fit the "activity-favorable" pattern of the visualized 3D-QSAR model. Five of the external flavones exhibited significant cyanobacterial inhibitory ability at their predicted IC₅₀ by the 3D-QSAR model. In particular, diosmetin achieved an inhibition rate of 70.50±4.74%, which was much higher than expected. The flavones screened by the 3D-QSAR model are novel cyanobacterial inhibitors and should be fully exploited to mitigate cHABs.

An examination of the relationship between molecular dipole moment and blood-gas partition for common anaesthetic gases

The solubility of inhalational anaesthetics in the bloodstream is related to the minimum alveolar concentration (MAC), which is an indicator of anaesthetic gas potency. The blood-gas partition coefficient (Kbg) is a measure of how much anaesthetics bind to plasma proteins in the blood compared to air. Just like the octanol-water partition coefficient, the Kbg may be related to the molecular dipole moment (μ), which can be modulated by the molecular conformation. Our quantum-chemical calculations demonstrated that subtle stereoelectronic interactions, namely those responsible for the anomeric and gauche effects, control the conformational equilibrium of some widely used volatile fluorinated anaesthetics and, consequently, of their molecular dipole moments. Since a remarkable correlation between empirical Kbg and calculated μ was found for these anaesthetics, the average molecular dipole moments may be used to predict the anaesthetic gas potency and other properties, such as lipid solubility, of inhalational anaesthetic analogs.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

The application of molecular topology for ulcerative colitis drug discovery

INTRODUCTION

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Is the bioconformation of 5-deoxy-5-fluoro-d-xylulose affected by intramolecular hydrogen bonds?

5-Deoxy-5-fluoro-d-xylulose (DFX) binds to the xylulokinase enzyme and, as a free ligand, it has preferential conformations governed by intramolecular interactions, such as hydrogen bonds and hyperconjugative interactions.