Field Descriptors Research Articles

Magnetocaloric effect modeling of dysprosium-transition metal based intermetallic alloys for magnetic refrigeration application using hybrid genetic algorithm based support vector regression intelligent method.

Intermetallic alloy containing rare earth dysprosium ions with the associated unfilled 4f shell electrons and sub-lattice of 3d-transition metal, results into fascinating magnetic properties which are useful for green refrigeration technological application. Magnetocaloric effect remains the fundamental principle upon which magnetic refrigeration technology is based while this cooling technology has advantages of cost effectiveness, high efficiency and environmental friendliness as compared with the existing conventional gas compression systems. Maximum magnetic entropy change (which controls the hugeness of magnetocaloric effect) of intermetallic alloy Dy-T-X (where T = transition metal and X = any other metal or nonmetal) is modeled in this work using hybrid genetic algorithm based support vector regression (GSVR) computational intelligent method with applied magnetic field, ionic concentration and ionic radii descriptors. The developed GSVR-G model with kernel Gaussian function outperforms GSVR-P model with polynomial function with improvement of 85.23%, 78.82% and 78.67% on the basis of the computed correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) on testing sample, respectively. The developed model further investigates the influence of applied external magnetic field on magnetocaloric effect of DyCuAl intermetallic alloy. The developed models in this work circumvent experimental challenges of magnetocaloric effect determination while the recorded precision of the developed model further opens doors for possible exploration of these intermetallic compounds for addressing environmental challenges associated with the present system of cooling.

Exploration and quantification of magnetocaloric effect in EuTiO3 perovskite using extreme learning machine intelligent computational method

Perovskite EuTiO3 is a multi-ferroic titanate oxide material that exhibits huge magnetocaloric effect at low magnetic field without associated hysteresis loss due to the magnetic field and thermal energy. These unique features coupled with the presence of strong coupling existing between spin and lattice as well as the possibility to switch the magnetic ground state between ferromagnetism and anti-ferromagnetism, foster the potentials of this material for magnetocaloric effect based cooling technology application. Maximum magnetic entropy change of perovskite EuTiO3 determines the strength of magnetocaloric effect in the material and ultimately influences the usability of this material for cooling technology. However, experimental measurement of maximum magnetic entropy change is challenging and consumes appreciable resources and time. Maximum magnetic entropy change of intrinsic and doped EuTiO3 perovskite is modeled in this work using extreme learning machine intelligent algorithm (ELMIA) with the ionic radii, elemental concentration and applied magnetic field descriptors. The developed ELMIA-SINE with sine activation function performs better than ELMIA-SIG model with sigmoid activation function with performance superiority of 42.46%, 44.58% and 1.76% using root mean square error, mean absolute error and correlation coefficient performance yardstick, respectively. The developed ELMIA-SINE model further outperforms the existing particle swarm based support vector regression model with polynomial (PSVR-P) and Gaussian (PSVR-G) function with performance improvement of 37.03% and 14.11%, respectively. The dependence of applied magnetic field and dopant concentration on hugeness of magnetocaloric effect was established using the developed model. The precision and the ease of implementation of the developed ELMIA model as compared with the existing models would definitely facilitate exploration of magnetocaloric effect based system of refrigeration for addressing the present energy crisis.

A Light Field FDL-HCGH Feature in Scale-Disparity Space

Many computer vision applications rely on feature detection and description, hence the need for computationally efficient and robust 4D light field (LF) feature detectors and descriptors. In this paper, we propose a novel light field feature descriptor based on the Fourier disparity layer representation, for light field imaging applications. After the Harris feature detection in a scale-disparity space, the proposed feature descriptor is then extracted using a circular neighborhood rather than a square neighborhood. It is shown to yield more accurate feature matching, compared with the LiFF LF feature, with a lower computational complexity. In order to evaluate the feature matching performance with the proposed descriptor, we generated a synthetic stereo LF dataset with ground truth matching points. Experimental results with synthetic and real-world dataset show that our solution outperforms existing methods in terms of both feature detection robustness and feature matching accuracy.

DRFD-Net: Using Dual Receptive Field Descriptors for Multitemporal Optical Remote Sensing Image Registration

Multitemporal optical remote sensing image registration is still a challenging problem for current feature-based image registration algorithms due to the complex nonlinear discrepancies arising from diverse factors, including illumination, weather, and surface condition changes. To address the issue, this article attempts to combine the dual receptive field descriptors (DRFDs) constructed by a novel deep convolutional network. In addition, a novel inner loss function (ILF) that imposes constraints on the intermediate descriptors is adopted in order to consolidate the distinguishability of the descriptors when the overlapping areas of the input image patches are large. Subsequently, the dual feature distance maps (DFDMs) are built on the basis of the DRFDs and combined with features from accelerated segment test (FAST) key points for efficient and accurate correspondence establishment across the source image and the target image. Eventually, an iterative algorithm is proposed to remove the possible outliers. Experiments show that the combination of DRFDs trained with the ILF performs better than current learnable local descriptors, such as L2-Net, HardNet, and SOSNet. The image registration results using our method are more accurate than the methods based on learnable descriptors, such as L2-Net, HardNet, and SOSNet, and handcrafted descriptors, such as scale-invariant feature transform (SIFT), SURF, and ORB.

New Insights to Understand the CoMFA Analysis within the Density Functional Theory Framework

The Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) models now have a wide range of applications; however, new methodologies are required due to the complexity in understanding their results. This research presents a generalized version of quantum similarity field and chemical reactivity descriptors within the density functional theory framework. By taking reference compounds, this generalized methodology can be used to understand the biological activity of a molecular set. In this sense, this methodology allows to study of the CoMFA in quantum similarity and chemical reactivity. It is feasible to investigate steric and electrostatic effects on local substitutions using this method. They were considering that how these methodologies could be used when the receptor is known or unknown.

Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.

Multiple-phase-field modeling for fracture of composite materials

Fracture behavior of composite materials is a challenge in numerical simulations due to the heterogeneity introduced by reinforcements. Based on the phase-field fracture model, a multiple-phase-field ductile fracture model is developed by using different phase field descriptors to characterize multiple cracking behaviors, involving different fracture mechanism such as brittle fracture and ductile fracture for composite materials. A novel degradation function is introduced to couple different fracture mechanism, i.e., brittle damage and ductile damage. We also extended the proposed multiple-phase-field ductile fracture model to a general formulation which can incorporate with different constitutive models. The main findings of this article are three folds: (1) accuracy and robustness of the proposed model are guaranteed by comparing the present results and previous work; (2) plastic localization and accumulation is the main reason for crack initiation and propagation; (3) enhanced load carrying ability and ductility are obtained in composite structures with hard/tough inclusions.

Leishmanicidal and cytotoxic activities and 4D-QSAR of 2-arylidene indan-1,3-diones.

The indan-1,3-dione and its derivatives are important building blocks in organic synthesis and present important biological activities. Herein, the leishmanicidal and cytotoxicity evaluation of 16 2-arylidene indan-1,3-diones is described. The compounds were evaluated against the leukemia cell lines HL60 and Nalm6, and the most effective ones were 2-(4-nitrobenzylidene)-1H-indene-1,3(2H)-dione (4) and 4-[(1,3-dioxo-1H-inden-2(3H)-ylidene)methyl]benzonitrile (10), presenting IC50 values of around 30 µmol/L against Nalm6. The leishmanicidal activity was assessed on Leishmania amazonensis, with derivative 4 (IC50 = 16.6 µmol/L) being the most active. A four-dimensional quantitative structure-activity analysis (4D-QSAR) was applied to the indandione derivatives, through partial least-squares regression. The statistics presented by the regression models built with the selected field descriptors of Coulomb (C) and Lennard-Jones (L) nature, considering the activities against L. amazonensis, HL60, and Nalm6 leukemia cells, were, respectively, R2 = 0.88, 0.92, and 0.98; Q2 = 0.83, 0.88, and 0.97. The presence of positive Coulomb descriptors near the carbonyl groups indicates that these polar groups are related to the activities. Besides, the presence of positive Lennard-Jones descriptors close to substituents R3 or R1 indicates that bulky nonpolar substituents in these positions tend to increase the activities. This study provides useful insights into the mode of action of indandione derivatives for each biological activity involved.

On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities.

For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC50 predictions can screen molecules that have desirable activity before assessment by docking studies.

Synthesis, biological activity, and four-dimensional quantitative structure–activity analysis of 2-arylidene indan-1,3-dione derivatives tested against Daphnia magna

ABSTRACT A series of 18 2-arylidene indan-1,3-dione derivatives was synthesized and tested against Daphnia magna to assess the environmental toxicity of these compounds. Aiming to investigate the toxicity mechanism for this series of compounds, a four-dimensional quantitative structure–activity analysis (4D-QSAR) was performed through the partial least square regression (PLS). The best PLS model was built with two factors and the selected field descriptors, of Coulomb (C) and Lennard-Jones (L) nature, describing 77.43% of variance and presenting the following statistics: r 2 = 0.89; SEC = 0.30; Q 2 = 0.81; SEV = 0.36. According to the literature, the bioactivity of α,β-unsaturated ketones, a functionality present in the series of compounds under investigation, is related to the conjugated double bond with the carbonyl group. The presence of a positive Coulomb descriptor nearby the carbonyl moieties, obtained as a result of the regression model, indicates that these polar groups are also related to the toxicity on D. magna. From the PLS regression model, the toxicity EC50-48 h values increases with the positive Coulomb descriptor and diminishes with the negative Lennard-Jones descriptors. It could be concluded that the presence of small polar groups in the aromatic ring of the arylidene moiety tends to increase the toxicity, while bulkier apolar substituents lead to a decrease of the toxicity.

Graph-based feature extraction: A new proposal to study the classification of music signals outside the time-frequency domain.

Most feature extraction algorithms for music audio signals use Fourier transforms to obtain coefficients that describe specific aspects of music information within the sound spectrum, such as the timbral texture, tonal texture and rhythmic activity. In this paper, we introduce a new method for extracting features related to the rhythmic activity of music signals using the topological properties of a graph constructed from an audio signal. We map the local standard deviation of a music signal to a visibility graph and calculate the modularity (Q), the number of communities (Nc), the average degree (〈k〉), and the density (Δ) of this graph. By applying this procedure to each signal in a database of various musical genres, we detected the existence of a hierarchy of rhythmic self-similarities between musical styles given by these four network properties. Using Q, Nc, 〈k〉 and Δ as input attributes in a classification experiment based on supervised artificial neural networks, we obtained an accuracy higher than or equal to the beat histogram in 70% of the musical genre pairs, using only four features from the networks. Finally, when performing the attribute selection test with Q, Nc, 〈k〉 and Δ, along with the main signal processing field descriptors, we found that the four network properties were among the top-ranking positions given by this test.

Multi-phase field modeling for various fracture mechanisms in composites

Composite possesses wide spectrum of mechanical responses along with remarkable fracture strength and toughness. Such properties evolve due to combination of different failure mechanisms across multiple phases. Understanding the contribution of these mechanisms on overall behavior is necessary to fabricate the composite with desired fracture properties. Although numerous studies have been performed to correlate the fracture response with crack propagation characteristics, they fail to account several underlying micro-mechanical events. Therefore, in the present study, a multi-phase field fracture model is proposed. We consider that constituents are homogeneously distributed within the matrix, while macro-scale phase field descriptors are utilized to represent the failure of its constituents. Further, the reinforced elements are assumed to be anisotropic in nature with preferential fracture direction. Therefore, by employing the rule of mixture in free energies and applying thermodynamic principles, the governing equations together with evolution laws of phase field parameters are derived. Using multi-field approach and extracting the weak form for governing equations, we develop finite element based numerical framework. Coupled sets of linear equations are solved using staggered scheme in parallel computing architecture. Proposed scheme is verified with analytical solution of a composite where separate evolutions of damage within phases are correlated with emergent stress–strain response. Numerical simulations are performed to predict the mechanical response by interpreting the competitive involvement of fracture mechanisms within matrix and inclusion phases. The present findings and future scopes highlight the predictive capability of the proposed scheme for analyzing fracture behavior of composites.

Microwave-assisted synthesis, biological assessment, and molecular modeling of aza-heterocycles: Potential inhibitory capacity of cholinergic enzymes to Alzheimer's disease

A highly regioselective solvent-free microwave-assisted synthesis of pyrazoles and tetrahydroindazoles based on the condensation of 1,3-diketones with arylhydrazines is described. Compounds were evaluated as cholinesterase inhibitors in order to identify an alternative treatment for Alzheimer's disease. All compounds displayed moderated acetylcholinesterase inhibitory activity and most of the compounds displayed remarkable butyrylcholinesterase inhibitory activity and selectivity. The compounds 3y and 3i with IC50 of 1.65 and 3.59 µM, respectively, were the most active and selective compounds as butyrylcholinesterase inhibitors. Likewise, the compounds were tested as antioxidants agents, results showed that they have the ability to trap free-radicals. Molecular Docking studies showed a key π-π stacking interaction of most of the compounds with residue Trp82 within of butyrylcholinesterase active site. Molecular quantum similarity field, global and local reactivity descriptors, and the Fukui functions were calculated in the Density Functional Theory framework to analyze the reactivity patterns along with the molecular set.

Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data

Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models. However, the fundamental limitation of machine learning methods is their correlative nature, leading to extreme susceptibility to confounding factors. Here, we implement the workflow for causal analysis of structural scanning transmission electron microscopy (STEM) data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions. The combinatorial library of the Sm-doped BiFeO3 is grown to cover the composition range from pure ferroelectric BFO to orthorhombic 20% Sm-doped BFO. Atomically resolved STEM images are acquired for selected compositions and are used to create a set of local compositional, structural, and polarization field descriptors. The information-geometric causal inference (IGCI) and additive noise model (ANM) analysis are used to establish the pairwise causal directions between the descriptors, ordering the data set in the causal direction. The causal chain for IGCI and ANM across the composition is compared and suggests the presence of common causal mechanisms across the composition series. Ultimately, we believe that the causal analysis of the multimodal data will allow exploring the causal links between multiple competing mechanisms that control the emergence of unique functionalities of morphotropic materials and ferroelectric relaxors.

Assessing impact of inpatient rotations on resident interest in hematology-oncology.

11031 Background: Working as a hematologist-oncologist is rewarding yet challenging. As cancer cases increase, there is a growing need to recruit more oncologists. It has been hypothesized that earlier exposure to oncology may improve resident interest in the field. This is often achieved by rotations on inpatient oncology wards, which represents only a small subset of the clinical work typical in hematology-oncology. We looked to assess whether early exposure to an inpatient oncology rotation impacts pursuit of this career path. Methods: Categorical interns in Internal Medicine at the University of Florida during year 2017-2018 were surveyed anonymously before and after their inpatient oncology rotation. Confidential identifiers were used to pair surveys. Similar surveys were used in the cardiology and gastroenterology rotations to allow for comparison. The survey included descriptor words to choose when describing the field. These words were categorized as either positive or negative. Results: Thirty-three interns were surveyed. Seventeen surveys were able to be paired (52% response rate) in oncology. Cardiology had 15 paired survey responses and gastroenterology had 13 paired responses. Using a 4-point Likert scale, interest in hematology-oncology decreased overall from 3.12 to 2.82 (p = 0.059) while interest in pursuing a fellowship remained the same. Stress levels were increased slightly from 2.30 to 2.05 (p = 0.564). Cardiology also saw a decrease in interest in the field from 3.53 to 2.93 (p = 0.007) but also an increase in pursuing fellowship from 2.20 to 2.60 (p = 0.014) and decreased stress from 3.13 to 2.67 (p = 0.020). Gastroenterology saw a decrease in interest in the field as well from 3.46 to 2.92 (p = 0.020). Interest in pursuing a fellowship and stress levels were not significantly different for gastroenterology. Field descriptors including “challenging,” “scared,” and “excited” decreased the most while “confident,” “inspired,” and “motivated” increased the most when describing hematology-oncology. Conclusions: While interest in this field was negatively impacted, stress levels associated with the field decreased. Furthermore, field descriptors were generally more positive and optimistic in the post-rotation setting. Though not statistically significant, these findings suggest that interns see oncology as less stressful and feel more capable after inpatient exposure during intern year. As much of hematology-oncology practice is outpatient, increasing intern exposure in this area may further improve perception and recruitment to the field.

Characterization of Cover Crop Rooting Types from Integration of Rhizobox Imaging and Root Atlas Information.

Plant root systems are essential for sustainable agriculture, conveying resource-efficient genotypes and species with benefits to soil ecosystem functions. Targeted selection of species/genotypes depends on available root system information. Currently there is no standardized approach for comprehensive root system characterization, suggesting the need for data integration across methods and sources. Here, we combine field measured root descriptors from the classical Root Atlas series with traits from controlled-environment root imaging for 10 cover crop species to (i) detect descriptors scaling between distant experimental methods, (ii) provide traits for species classification, and (iii) discuss implications for cover crop ecosystem functions. Results revealed relation of single axes measures from root imaging (convex hull, primary-lateral length ratio) to Root Atlas field descriptors (depth, branching order). Using composite root variables (principal components) for branching, morphology, and assimilate investment traits, cover crops were classified into species with (i) topsoil-allocated large diameter rooting type, (ii) low-branched primary/shoot-born axes-dominated rooting type, and (iii) highly branched dense rooting type, with classification trait-dependent distinction according to depth distribution. Data integration facilitated identification of root classification variables to derive root-related cover crop distinction, indicating their agro-ecological functions.

Morphometric and colourimetric tools to dissect morphological diversity: an application in sweet potato [Ipomoea batatas (L.) Lam.

The genetic diversity of sweet potato [Ipomoea batatas (L.) Lam.] and its wild relatives has been collected and conserved in germplasm collections worldwide and explored employing several tools. The characterization of crops diversity through morphological tools produce useful information. However, the use of conventional morphological descriptions exhibits limitations due to the use of subjective and categorical parameters that affect phenotypic description and diversity estimation. In order to increase the efficiency to discriminate different phenotypes not detected by conventional morphological descriptors, new phenomic approaches were used. Seventy sweet potato accessions collected in the northern coast of Colombia were characterized by forty-nine parameters from conventional sweet potato descriptors and data obtained by RGB imaging and colourimetry. Field descriptions, RGB imaging-colourimetry and both databases integrated were analysed using Gower’s general similarity coefficient for clustering. Estimation of genotype similarity was significantly improved when quantitative data obtained by RGB imaging and colourimetry analysis were included. Variations in traits such as flesh and periderm colour in roots, leaves, vein colour and leaf shape that were not detected by field descriptors, were efficiently discriminated by measuring pixel values from images, estimation of shape descriptors (circularity, solidity, area) and colourimetry data. Expected high correlations were found for field parameters (number of lobes, lobe type, and central lobe shape) and image data (circularity, roundness and solidity). The combination of RGB imaging and colourimetry benefits the quality of morphological characterizations, resulting in a cost-effective process that is able to identify polymorphisms and target traits for diversity estimation and breeding.

Four‐dimensional quantitative structure‐activity analysis of 1,4‐naphthoquinone derivatives tested against HL‐60 human promyelocytic leukemia cells

AbstractFour‐dimensional quantitative structure‐activity relationship (4D‐QSAR) models were developed to predict biological activity of 1,4‐naphthoquinones derivatives tested against human HL‐60 leukemic cells, in order to better investigate the action mode of these compounds. Quinones can generate reactive oxygen species (ROS) through the activation by the cytochrome P450 and P450 reductase enzymes acting as anticancer agents. Molecular dynamics (MD) simulations were performed in the 3D optimized geometries, and the field descriptors were calculated. Partial least squares (PLS) regression method was applied to build the QSAR model, which presented the following statistics, with two factors and explaining 51.11% of total variance: R2 = 0.83; SEC = 0.28; Q2 = 0.77; SEV = 0.31. For external validation, the results were R2pred = 0.76 and SEP = 0.30. Among the nine Coulomb (C) and Lennard‐Jones (LJ) descriptors selected by the model, one of them, C13838, is located close to quinone oxygens involved in the production of radical anions (O2−·) and to hydroxyl in position 5 that may stabilize catalytically important water molecules. The negative LJ descriptors around R1 and R2 substituents might indicate that apolar substituents in these regions are unfavorable to the activity. Coulomb descriptors located at the vicinities of the substituent R2 gave information about the bioactive conformation.

Insights into the key structural features of triazolothienopyrimidines as anti-HIV agents using QSAR, molecular docking, and pharmacophore modeling

The development of severe drug resistance caused by the extensive use of anti-HIV agents has resulted in resistance mutation that compromise efficacy of anti-retroviral. We have selected triazolothienopyrimidine derivatives for our study since these derivatives have potentially inhibited HIV-1 replication. The objectives of our study were to identify the important pharmacophoric features and correlate 3D chemical structure of of triazolothienopyrimidines and predict their anti-HIV activity using 2D-, 3D-QSAR, pharmacophore modeling, and docking studies to explore their binding to HIV-1 reverse transcriptase. Partial least square and k-nearest neighbor methodology were used to develop 2D-QSAR and 3D-QSAR models. The statistical significance of developed QSAR models was checked by internal and external validation. The developed 2D-QSAR model indicated that a significance of presence of electron withdrawing groups, number of hydrogen bond acceptor, Z component dipole moment, and the moment of inertia at X axis of the compounds on their anti-HIV potential. 3D-QSAR results indicated the influence of electrostatic and steric field descriptors in the anti-HIV potential of triazolothienopyrimidines. The pharmacophore mapping represented the importance of aromatic and hydrogen bond acceptor features of compounds to interact with the target. Docking studies revealed that the binding of the triazolothienopyrimidines to HIV-1 reverse transcriptase involved extensive hydrophobic and polar interactions. Collectively, the results of QSAR, pharmacophore mapping, and the docking studies provide an insight into the understanding of the relationship between triazolothienopyrimidines and anti-HIV activity, which may assist in designing new triazolothienopyrimidines with enhanced HIV-1 reverse transcriptase inhibitory and anti-HIV activity.

QSAR and Pharmacophore Mapping Studies on Benzothiazinimines to Relate their Structural Features with anti-HIV Activity.

The development of severe drug resistance caused by the extensive use of anti-HIV agents has resulted in a greatly extensive reduction in these drugs efficacy. To identify the important pharmacophoric features and correlate 3D chemical structure of benzothiazinimines with their anti-HIV potential using 2D, 3D-QSAR and pharmacophore modeling studies. QSAR and pharmacophore mapping studies have been used to relate structural features. 2D QSAR and 3D QSAR studies were performed using partial least square and k-nearest neighbor methodology, coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing, to derive QSAR models which were further validated for statistical significance. The physicochemical descriptor XAHydrophilicArea and SsOHE-index, and alignmentindependent descriptor T_C_Cl_6 showed significant correlation with the anti-HIV activity of benzothiazinimines in 2D QSAR. 3D QSAR results showed the significant effect of electrostatic and steric field descriptors in the anti-HIV potential of benzothiazinimines. The generated pharmacophore hypothesis demonstrated the importance of aromaticity and hydrogen bond acceptors. The significant models obtained in this study suggested that these techniques could be used as a guidance for designing new benzothiazinimines with enhanced anti-HIV potential.