SAR Modeling Research Articles

Assessing the potential of Eddy detection in MIZ using SAR and Lagrangian modeling: A test case on Fram Strait

The analysis of ocean eddies in the marginal ice zone via remote sensing and modeling data is a challenging task. However, it is of crucial importance for various scientific applications and anthropogenic activities in the Arctic. Models often struggle to accurately represent eddies near the MIZ due to the intricate nature of sea ice-ocean interactions, unresolved small-scale processes, and coarse resolution. Nevertheless, combining the information provided from both SAR and model data offers promising results that can potentially improve eddy detection accuracy near the MIZ. Furthermore, accurate characterization of the spatial and temporal variability of eddies near the MIZ demands a holistic approach that incorporates multi-platform observations, including numerical models and remote sensing data. This study demonstrates a specific test case on the intercomparison of the eddy signatures located in the MIZ in the Fram Strait based on remote sensing SAR scenes and Lagrangian modeling data from the two global oceanographic reanalysis GOFS 3.1 and GLORYS 12 V1. The study specifically displays the strong agreement in the eddy polarity and synchronism with reality, as well as differences in spatial scales and location of eddy centers. Overall, the obtained results support the further use of the presented approach for studying the eddies in the MIZ regions in the Arctic.

Spatial Autoregressive Quantile Regression Modeling of the Distribution of Drug Users in the District Karo

BNN data shows that an average of 50 people die from drugs every day, and Karo Regency is in second place for the distribution of drug abuse in North Sumatra after Medan City. Variables that have this risk include drug factors, namely availability and ease of obtaining drugs, individual factors, environmental factors, family factors, and social factors. Based on this, a model is needed to determine the development of the case. The SARQR model combines SAR modeling with quantile regression (QR). Combining the SAR model with quantile regression produces a model that is good for overcoming the problems of dependency and heterogeneity in modeling spatial data and is resistant to outlier data. This research aims to determine the Spatial Autoregressive Quantile Regression Model for the distribution of drug users in the Karo Regency. The type of research used is quantitative research. The data type used is secondary data, namely, the kind of data already existing, and the data source used in this research is drug users in the Karo district. The research results show that the Spatial Autoregressive Quantile Regression model for the distribution of drug users in Karo Regency obtained estimation results for the distribution parameters of drug users using a significance test. This model explains that the factors that significantly influence drug abuse are age, gender, occupation, and other underlying factors.

A method for in vitro data and structure curation to optimize for QSAR modelling of minimum absolute potency levels and a comparative use case

Large screening programs such as the US Tox21 are releasing experimental in vitro results for many endpoints of relevance for human health. In (Q)SAR modelling, it is essential to clearly define the endpoint (OECD QSAR Validation Principle 1) and extract the most robust data points according to the definition. We have developed a comprehensive data curation procedure to interpret in vitro experimental data sets for (Q)SAR development, with modules for selecting actives according to quality of curve fittings, magnitude of activity and ‘absolute’ potency cut-offs, requiring non-cytotoxicity at activity concentration; extracting only very robust inactives; selecting only substances tested in high purity; and accounting for assay signal interference. A structure curation procedure with uniform representation of tautomeric classes of substances is also developed. The detailed method and a use case of modelling Tox21 data for an estrogen receptor α agonism assay with and without use of the method is presented.

Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia

This paper presents a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.

Potency and metabolic stability: a molecular hybrid case in the design of novel PF74-like small molecules targeting HIV-1 capsid protein.

PF74 (1) is a potent and well-characterized prototypical small molecule targeting human immunodeficiency virus type 1 (HIV-1) capsid protein (CA), but not a viable antiviral lead due to the lack of metabolic stability. We report herein our molecular hybridization-based medicinal chemistry efforts toward potent and metabolically stable PF74-like small molecules. The design of the new sub-chemotype 4 rationally combines binding features of two recently reported PF74-like compounds 2 and 3. The subsequent confirmation and structure-activity relationship (SAR) of hit 4a entailed the chemical synthesis of 37 novel analogs, most of which showed modest but meaningful thermal shift, and low μM antiviral activity. The most potent analogs (4a, 4d, 4o, and 4r) all exhibited noticeably improved metabolic stability over PF74. Molecular modeling suggests that these new analogs bind to the PF74 binding site. Overall, our work demonstrated that the molecular hybridization approach is suitable for designing compounds with balanced potency and metabolic stability.

DNA binding studies of novel diazatruxenones analogs as potential anticancer agents: Synthesis, antitumor investigation, DNA binding, SAR and molecular modeling calculation

A series of polycyclic skeleton of truxene and triazatruxene analogs has been synthesized and evaluated for antitumor and DNA binding activities. The synthesized structures were confirmed by different spectroscopic techniques such as IR, 1HNMR, 13CNMR, and mass spectroscopy. The antitumor screening was performed adopting the NCI protocol against 60 different cell lines. Compounds 2 and 8 proved to be the most active ones among the other target compounds. In a trial to investigate the mechanism of action of the target compounds, DNA binding activity was also investigated. Compounds 3f, 4–8 exhibited good binding activity explaining their mechanism. In addition, molecular modeling studies were also performed for more clearance of the data obtained from the biological screening.

A combinatorial target screening strategy for deorphaning macromolecular targets of natural product

Natural products, as an ideal starting point for molecular design, play a pivotal role in drug discovery; however, ambiguous targets and mechanisms have limited their in-depth research and applications in a global dimension. In-silico target prediction methods have become an alternative to target identification experiments due to the high accuracy and speed, but most studies only use a single prediction method, which may reduce the accuracy and reliability of the prediction. Here, we firstly presented a combinatorial target screening strategy to facilitate multi-target screening of natural products considering the characteristics of diverse in-silico target prediction methods, which consists of ligand-based online approaches, consensus SAR modelling and target-specific re-scoring function modelling. To validate the practicability of the strategy, natural product neferine, a bisbenzylisoquinoline alkaloid isolated from the lotus seed, was taken as an example to illustrate the screening process and a series of corresponding experiments were implemented to explore the pharmacological mechanisms of neferine. The proposed computational method could be used for a complementary hypothesis generation and rapid analysis of potential targets of natural products.

Tubulin targeted antimitotic agents based on adamantane lead compound: synthesis, SAR and molecular modeling

5-Hydroxymethyl-2-methoxyphenyl adamantane-1-acetate inhibits cell proliferation and stimulates depolymerization of microtubules of cancer cells to free tubulin. Its analogues were synthesized via the Steglich or Mitsunobu reactions to determine the role of structural subunits of the molecule in tubulin binding. Based on the structure–activity relationship studies, metabolically stable 1-[2-(5-hydroxymethyl-2-methoxyphenyl)ethyl]adamantane was invented, which exhibits a dual-target profile and retains in vitro activity observed for the lead compound.

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency’s National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA’s Chemistry Dashboard and made freely available to the scientific community.

Kinase inhibitions in pyrido[4,3-h] and [3,4-g]quinazolines: Synthesis, SAR and molecular modeling studies.

New pyrido[3,4-g]quinazoline derivatives were prepared and evaluated for their inhibitory potency toward 5 protein kinases (CLK1, DYRK1A, GSK3, CDK5, CK1). A related pyrido[4,3-h]quinazoline scaffold with an angular structure was also synthesized and its potency against the same protein kinase panel was compared to the analogous pyrido[3,4-g]quinazoline. Best results were obtained for 10-nitropyrido[3,4-g]quinazoline 4 toward CLK1 with nanomolar activities.

Multi-Sensor SAR Geodetic Imaging and Modelling of Santorini Volcano Post-Unrest Response

Volcanic history of Santorini over recent years records a seismo-volcanic unrest in 2011–12 with a non-eruptive behavior. The volcano deformation state following the unrest was investigated through multi-sensor Synthetic Aperture Radar Interferometry (InSAR) time series. We focused on the analysis of Copernicus Sentinel-1, Radarsat-2 and TerraSAR-X Multi-temporal SAR Interferometric (MT-InSAR) results, for the post-unrest period 2012–17. Data from multiple Sentinel-1 tracks and acquisition geometries were used to constrain the E-W and vertical components of the deformation field along with their evolution in time. The interpretation of the InSAR observations and modelling provided insights on the post-unrest deformation pattern of the volcano, allowing the further re-evaluation of the unrest event. The increase of subsidence rates on Nea Kameni, in accordance with the observed change of the spatial deformation pattern, compared to the pre-unrest period, suggests the superimposition of various deformation sources. Best-fitting inversion results indicate two deflation sources located at southwestern Nea Kameni at 1 km depth, and in the northern intra-caldera area at 2 km depth. A northern sill-like source interprets the post-unrest deflation attributed to the passive degassing of the magma intruded at 4 km during the unrest, while an isotropic source at Nea Kameni simulates a prevailing subsidence occurring since the pre-unrest period (1992–2010).

Imidazolo and tryptophan-imidazolo hybrid derived ureas/thioureas as potent bioactive agents – SAR and molecular modelling studies

Herein, we used an imidazole derivative (IMD) which showed promising antibacterial, antifungal and antioxidant properties in our earlier investigation. Prompted by this, we converted IMD to hydrazide (IMH) by hydrazinolysis which was derivatized to various ureas (3–7) and thioureas (8–12). On the other hand, IMH was conjugated to Boc-Trp-OH as it has been shown in the past that hybridization of two molecules produced promising biologically active compounds. Boc of the conjugate was removed and further converted into several urea (14–18) and thiourea (19–23) derivatives. All the title compounds so also the starting materials and intermediates were assessed for potential biological applications. The results showed that compounds 3, 4, 8, 9, 14, 15, 19 and 20 were excellent antioxidants as revealed by DPPH, DMPD and ABTS assays. Further, certain analogues like 5–7, 10–12, 16–18 and 21–23 were found to be potent antimicrobials against pathogenic bacteria and fungi whereas good anti-inflammatory activity was obtained for molecules 5–7, 10–12, 16–18 and 21–23. All together, derivatives of the conjugates have shown superior activity over non-conjugated compounds and the former have exhibited potent activity against standard drugs in all the assays. In a quest to understand the binding interactions of the compounds with active site of tyrosine kinase (PDB ID: 2HCK), glucosamine-6-phosphate (GlcN-6-P) synthase (PDB ID: 2VF5) and cyclooxygenase-2 (PDB ID: 1CX2) enzymes, the correlation studies were conducted using molecular modelling which showed good receptor binding interactions with several amino acids of the enzymes. Overall, the current investigation may be considered for the discovery of lead compound(s) for treating multiple disorder conditions using singular molecular entity.

Classification (Agonist/Antagonist) and Regression "Structure-Activity" Models of Drug Interaction with 5-HT6.

One promising target for novel psychotropic drugs is the 5-HT6 receptor, GProtein- Coupled Receptor (GPCR) family, displaying seven transmembrane domains. There is considerable interest in how both 5-HT6 receptor agonist and antagonist compounds can have marked procognitive effects. An exact structure of the 5-HT6 receptor is not available, so application of powerful methods of (Q)SAR and molecular modelling, which play an essential role in modern drug design, are currently limited to structure-based homology models. The present study is devoted to a detailed QSAR analysis of 61 drugs (26 agonists and 35 antagonists) acting on the 5-HT6 receptor (rattus norvegicus and homo sapiens). Five classification methods were used: k-Nearest Neighbors (k-NN), Logistic Regression (LG), Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM). Multiple Regression Analysis (MRA) was involved also for regression analysis. Spectra of Inter Atomic Interactions (SIAI) were applied in the search for ligand centres interacting with the 5- HT6 receptor. SAR and QSAR models based on the use of HYBOT, MOLTRA, VolSurf+, and SYBYL programs, and having cross-validated coefficients of determination of at least 0.80, show a predominant influence of H-bond acceptor ability and hydrophobicity on the type of ligand activity and degree of inhibition.

Spaceborne PolInSAR and ground-based TLS data modeling for characterization of forest structural and biophysical parameters

Mapping of forest biophysical parameters is an effective tool for determination of forest inventories, vegetation modeling, and the global carbon cycle. The present study aims at quantification of the forest biophysical parameters of a sub-tropical Forest of India with Terrestrial Laser Scanner (TLS) and Polarimetric Interferometry SAR (PolInSAR) modeling approaches. TLS data was acquired in single and multiple scans and it was found that the multiple scanning approaches have the capability to provide forest parameters with very high accuracy. The RANSAC algorithm was implemented on TLS point cloud data to derive forest structural parameters. The TLS modeled DBH and stem volume exhibited a coefficient of determination of 0.88 and 0.80 and RMSE of 2.85 cm and 0.4 cu.m respectively. PolInSAR Coherence Amplitude Inversion (CAI) and RVoG techniques were implemented to retrieve forest stand height. Both the PolInSAR inversion based models were employed with the integration of complex coherences occurring in different polarimetric channels. CAI estimated the forest height precisely nearly equivalent to the field measured forest height with a coefficient of determination of 0.52, percent accuracy 85.85% and RMSE of 2.94 m. Application of the RVoG model further improved the statistics of the forest height with a coefficient of determination of 0.57, percent accuracy 89.94% and RMSE of 2.13 m. Measurement of tree height estimation using RVoG showed false vegetation height for dry riverbed and the same feature was characterized by the CAI model with zero vegetation height. The research showed that the forest vegetation height characterization of CAI model is better than the RVoG. This study has successfully investigated the potential of spaceborne PolInSAR and ground-based TLS data for forest structural and biophysical parameters retrieval.

Design, Synthesis, SAR and Molecular Modeling Studies of Novel Imidazo[2,1-b][1,3,4]Thiadiazole Derivatives as Highly Potent Antimicrobial Agents.

In this study, a novel series of phenyl substituted imidazo[2,1-b][1,3,4]thiadiazole derivatives were synthesized, characterized and explored for antibacterial activity against Gram-negative Escherichia coli, Gram-positive Staphylococcus aureus and Bacillus subtilis and antifungal activity against Candida albicans. Most of the synthesized compounds exhibited remarkable antimicrobial activities, some of which being ten times more potent than positive controls. The most promising compound showed excellent activity with MIC value of 0.03 μg/ml against both S. aureus and B. subtilis (MIC values of positive compound Chloramphenicol are 0.4 μg/ml and 0.85 μg/ml, respectively). Furthermore, structure-activity relationship was also investigated with the help of computational tools. Some physicochemical and ADME properties of the compounds were calculated too. The combination of electronic structure calculations performed at PM6 level and molecular docking simulations using Glide extra-precision mode showed that the hydrophobic nature of keto aryl ring with no electron withdrawing substituents at para position enhances activity while electron-donating substituents at the second aryl ring is detrimental to activity.

Organocatalyzed and mechanochemical solvent-free synthesis of novel and functionalized bis-biphenyl substituted thiazolidinones as potent tyrosinase inhibitors: SAR and molecular modeling studies

Eluding the involvement of solvents in organic synthesis and introducing environment friendly procedures can control environmental problems. A facile and an efficient solvent free mechanochemical method (grinding) is achieved to synthesize novel bis-biphenyl substituted thiazolidinones using non-toxic and cheap N-acetyl glycine (NAG). Organocatalytic condensation of a series of Schiff's bases bearing different substituents with thioglycolic acid produces a variety of thiazolidinones derivatives in good to excellent yield. In vitro inhibition studies against mushroom tyrosinase of these thiazolidinone analogues revealed that many of them possessed good to excellent tyrosinase inhibition at low micro-molar concentrations. In particular, six compounds exhibited potent inhibitory potential with IC50 values ranging from 0.61 ± 0.31 to 21.61 ± 0.11 μM as compared with that of standard kojic acid (IC50 6.04 ± 0.11 μM). Further molecular docking studies revealed that the thiazolidinones moiety plays a key role in the inhibition mechanism by well fitting into the enzyme bounding pocket.

Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity.

Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

Applicability domain: towards a more formal definition

In recent years the applicability domain (AD) of a prediction system has become an important concern in (Q)SAR modelling, especially in the context of human safety assessment. Today AD is an active research topic, and many methods have been designed to estimate the adequacy of a model and the confidence in its outcome for a given prediction task. Unfortunately, the wide spectrum of techniques developed for this purpose is based on various definitions of the concept of AD, often taking into account different types of information. This variety of methodologies confuses the end users and makes the comparison of the AD for different models almost impossible. In this article, we demonstrate that AD is not a monolithic concept and can be broken down into three well-defined sub-domains assessing confidence at the model, prediction and decision levels, respectively. By leveraging this separation of concerns we have an opportunity to clarify, formalize and extend the definition of AD. We propose a framework that captures this new vision with the aim to initiate a global effort to converge towards a common AD definition within the (Q)SAR community.

Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability.

BackgroundEven though circular fingerprints have been first introduced more than 50 years ago, they are still widely used for building highly predictive, state-of-the-art (Q)SAR models. Historically, these structural fragments were designed to search large molecular databases. Hence, to derive a compact representation, circular fingerprint fragments are often folded to comparatively short bit-strings. However, folding fingerprints introduces bit collisions, and therefore adds noise to the encoded structural information and removes its interpretability. Both representations, folded as well as unprocessed fingerprints, are often used for (Q)SAR modeling.ResultsWe show that it can be preferable to build (Q)SAR models with circular fingerprint fragments that have been filtered by supervised feature selection, instead of applying folded or all fragments. Compared to folded fingerprints, filtered fingerprints significantly increase predictive performance and remain unambiguous and interpretable. Compared to unprocessed fingerprints, filtered fingerprints reduce the computational effort and are a more compact and less redundant feature representation. Depending on the selected learning algorithm filtering yields about equally predictive (Q)SAR models. We demonstrate the suitability of filtered fingerprints for (Q)SAR modeling by presenting our freely available web service Collision-free Filtered Circular Fingerprints that provides rationales for predictions by highlighting important structural features in the query compound (see http://coffer.informatik.uni-mainz.de).ConclusionsCircular fingerprints are potent structural features that yield highly predictive models and encode interpretable structural information. However, to not lose interpretability, circular fingerprints should not be folded when building prediction models. Our experiments show that filtering is a suitable option to reduce the high computational effort when working with all fingerprint fragments. Additionally, our experiments suggest that the area under precision recall curve is a more sensible statistic for validating (Q)SAR models for virtual screening than the area under ROC or other measures for early recognition. Graphical Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0173-z) contains supplementary material, which is available to authorized users.

Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data

Structural parameters of the woody component in African savannahs provide estimates of carbon stocks that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component can be characterised by various quantifiable woody structural parameters, such as tree cover, tree height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species diversity and phenological status – a defining but challenging set of characteristics typical of African savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody component because of their ability to sense within-canopy properties of the vegetation and its insensitivity to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s structure can be sensed differently with SAR depending on the frequency or wavelength of the sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency was more effective in the modelling of the CC (coefficient of determination or R2 of 0.77), TCV (R2 of 0.79) and AGB (R2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and C-band) both as individual and combined (X+C-band) datasets. The addition of the shortest wavelengths also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g. dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration of all three frequencies (X+C+L-band) yielded the best overall results for all three metrics (R2=0.83 for CC and AGB and R2=0.85 for TCV), the improvements were noticeable but marginal in comparison to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring in this environment.