Two-dimensional Descriptors Research Articles

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism.

Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for the toxicities associated with the drugs. Identification of reactive metabolites of drug candidates can be very helpful in the initial stages of drug discovery. Quinones are soft electrophiles that are generated as reactive intermediates during metabolism. Quinones make up more than 40% of the reactive metabolites. In this work, a reliable data set of 510 molecules was used to develop machine learning and deep learning-based predictive models to predict the formation of quinone-type metabolites. For representing molecules, two-dimensional (2D) descriptors, PubChem fingerprints, electro-topological state (E-state) fingerprints, and metabolic reactivity-based descriptors were used. Developed models were compared to the existing Xenosite web server using the untouched test set of 102 molecules. The best model achieved an accuracy of 86.27%, while the Xenosite server could achieve an accuracy of only 52.94% on the test set. Descriptor analysis revealed that the presence of greater numbers of polar moieties in a molecule can prevent the formation of quinone-type metabolites. In addition, the presence of a nitrogen atom in an aromatic ring and the presence of metabolophores V51, V52, and V53 (SMARTCyp descriptors) decrease the probability of quinone formation. Finally, a tool based on the best machine learning models was developed, which is accessible at http://14.139.57.41/quinonepred/.

Evaluation of the Topology Space of DNA-Encoded Libraries.

DNA-encoded libraries (DELs) are widely used in the discovery of drug candidates, and understanding their design principles is critical for accessing better libraries. Most DELs are combinatorial in nature and are synthesized by assembling sets of building blocks in specific topologies. In this study, different aspects of library topology were explored and their effect on DEL properties and chemical diversity was analyzed. We introduce a descriptor for DEL topological assignment (DELTA) and use it to examine the landscape of possible DEL topologies and their coverage in the literature. A generative topographic mapping analysis revealed that the impact of library topology on chemical space coverage is secondary to building block selection. Furthermore, it became apparent that the descriptor used to analyze chemical space dictates how structures cluster, with the effects of topology being apparent when using three-dimensional descriptors but not with common two-dimensional descriptors. This outcome points to potential challenges of attempts to predict DEL productivity based on chemical space analyses alone. While topology is rather inconsequential for defining the chemical space of encoded compounds, it greatly affects possible interactions with target proteins as illustrated in docking studies using NAD/NADP binding proteins as model receptors.

Quantifying the Two-Dimensional Driving Patterns of Chemisorbed Oxygen and Particle Size on NO Reduction Activity and Mechanism.

Quantification in the driving patterns of activity descriptors on structure-activity relationships and reaction mechanisms over heterogeneous catalysts is still a great challenge and needs to be addressed urgently. Herein, with the example of typical Mn-based catalysts, based on the activity regularity and many characterizations, the chemisorbed oxygen density (ρOβ) and particle size (dTEM) have been proposed as the two-dimensional descriptors for selective catalytic reduction of NO, whose role is in quantifying the contents of vacancy defects and the amounts of active sites located on terraces or interfaces, respectively. They can be utilized to construct and quantify the driving patterns for the structure-activity relationships and reaction mechanisms of NO reduction. As a consequence, a complementary modulation for Ea by ρOβ and dTEM is described quantitatively in terms of the fitted functions. Moreover, based on the structure-activity relationships and the quantification laws of in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), the reaction efficiency (RE) of the specific combined NOx-intermediate is identified as the trigger to drive the Langmuir-Hinshelwood mechanism and modulated by the descriptors complementally and collaboratively following the fitted quantification functions. Either of the two descriptors at its lower values plays a dominant role in regulating Ea and RE, and the dominant factor evolves progressively: dTEM ↔ coupling dTEM with ρOβ ↔ ρOβ, when the dependency of Ea and RE on the descriptors is adopted to identify the dominant factor and domains. Therefore, this work has quantitatively accounted for the essence of activity modulation and may provide insight into the quantitative driving patterns for reaction activity and mechanism.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls.

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons.

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanisticallyinterpretable based on both internal and external validation metrics (R2 range of 0.605-0.959. Q2(Loo) range of 0.509-0.904, and Q2F1 range of 0.526-0.959). The intelligent consensus predictor tool has been used for theimprovement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q2F1 = 0.808 and Q2F2 = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors

Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein segments, randomly selected from simulated model datasets. The advantage of 2DUV descriptors over one-dimensional linear absorption and circular dichroism spectra lies in the cross-peak information that reflects interactions between local regions of the protein. Thanks to their ultrafast (∼200 fs) nature, 2DUV measurements can be used in the future to probe conformational variations in the course of protein dynamics.

Nitroaromatics as hypoxic cell radiosensitizers: A 2D-QSAR approach to explore structural features contributing to radiosensitization effectiveness

Hypoxia is the prime component of tumor microenvironment that plays a pivotal role in cancer progression. Nitroaromatic compounds are known to enhance the sensitivity of hypoxic cells to ionizing radiation. The application of computational tools like Quantitative Structure-Activity Relationship (QSAR) can be used to predict newly developed nitroaromatics or compounds with missing data. In the present work, three datasets consisting of 18 nitrofurans, 11 nitrothiophenes and 84 nitroimidazoles were utilised for two-dimensional QSAR modeling to retrieve their structural features essential to elicit radiosensitivity. The work comprises two parts: (i) local modeling using individual datasets; and (ii) global modeling by clubbing the three datasets. The two-dimensional descriptors were calculated using Dragon (version 7.0) software. The developed models were obtained using various feature selection techniques applied in “Small Dataset Modeling” and “Double Cross Validation” tools available from https://dtclab.webs.com/software-tools. Finally, the models were validated using stringent metrics following the Organisation for Economic Co-operation and Development (OECD) guidelines. The developed models are robust, predictive, and are useful tools to predict the radiosensitization of newly developed nitroaromatics. Furthermore, the global model was used to predict two external sets comprising 10 and 47 compounds, and the prediction ability was validated using the “Prediction Reliability Indicator” tool.

QSAR modelling of organic dyes for their acute toxicity in Daphnia magna using 2D-descriptors

ABSTRACT The present study reports quantitative structure-activity relationship (QSAR) models for 22 organic dyes spanning a broad chemical domain to predict their toxicity in Daphnia magna [log (1/EC50)]. Only two-dimensional descriptors with clear physicochemical meaning were used to construct the QSAR models. The process of development, validation, and interpretation of models adheres to the stringent recommendations of the Organization for Economic Cooperation and Development (OECD) guidelines. In this study, the multi-layered stepwise regression method and linear discriminant analysis (LDA) method were employed for the deployment of regression – and classification-based models respectively; however, the final regression-based QSAR models were obtained through the partial least squares (PLS) regression. Additionally, the applicability domain of the developed models was verified. The constructed models should be applicable in the absence of toxicity data of new or untested dye structures, particularly when the compounds fall within the developed models’ scope, and also implementable to develop more environmentally friendly alternatives.

Skin sensitization quantitative QSAR models based on mechanistic structural alerts

Allergic contact dermatitis is increasingly of interest for the hazard characterization of chemicals. in vivo animal testing is usually adopted but in silico approaches are becoming the new frontier due to their swiftness and economic efficiency. Indeed, in silico models can rationalise the experimental outcomes besides having predictive ability. The aim of the present work was to explore the electrophilic chemical behaviour responsible for allergic contact dermatitis using quantitative QSAR regression models. Eight models were proposed, using an experimental LLNA dataset of 366 chemicals. Each model is unique to encode a type of electrophilic reactivity domain. The models were obtained using autocorrelation, electro-topological and atom centered fragment based on two-dimensional descriptors, which incorporated the electronic and stereochemical features of substances interacting with skin proteins to induce skin cell proliferation. Finally, simple steps were proposed to integrate the eight models for the application on the test chemicals.

QSPR modeling of absorption maxima of dyes used in dye sensitized solar cells (DSSCs)

Dye-sensitized solar cells (DSSCs) have recently received a significant attention as possible sources of renewable energy. As a result, a significant effort is being made to develop organic dyes for highly power conversion efficient DSSCs, in order to overcome the disadvantages of previous solar cell systems, such as cost reduction, weight reduction, and production methods that minimize environmental pollution. As shown by multiple recent research publications, computational techniques such as quantitative structure–property relationship (QSPR) modeling may aid in the development of suitable dyes for DSSCs satisfying many fundamental desired characteristics. The current report provides robust, externally verified QSPR models for five chemical classes of organic dyes (Triphenylamines, Phenothiazines, Indolines, Porphyrins and Coumarins) based on experimentally determined absorption maxima values. The size of the dye data points utilized to develop the models is the largest known to date. The QSPR models were constructed using only two-dimensional descriptors with clear physicochemical meaning. Using the best subset selection approach, we built 5, 3, 4, 3 and 2 descriptor models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin and Coumarin classes, respectively. The models were validated both internally and externally, and then consensus predictions were made for specific categories of dyes using the developed partial least squares (PLS) models, and the “Intelligent consensus predictor” tool (http://teqip.jdvu.ac.in/QSAR_Tools/) was used to determine whether the quality of test set compound predictions can be improved through the “intelligent” selection of multiple PLS models. We identified from the insights gained from the developed models several chemical attributes that are important in enhancing the absorption maxima. Thus, our study may be utilized to predict the λmax values of novel or untested organic dyes and to give insights that will aid in the development of new dyes for use in solar cells with increased λmax values and enhanced power conversion efficiency.

QSPR modelling for investigation of different properties of aminoglycoside-derived polymers using 2D descriptors

ABSTRACT The quantitative structure‐property relationship (QSPR) method is commonly used to predict different physicochemical characteristics of interest of chemical compounds with an objective to accelerate the process of design and development of novel chemical compounds in the biotechnology and healthcare industries. In the present report, we have employed a QSPR approach to predict the different properties of the aminoglycoside-derived polymers (i.e. polymer DNA binding and aminoglycoside-derived polymers mediated transgene expression). The final QSPR models were obtained using the partial least squares (PLS) regression approach using only specific categories of two-dimensional descriptors and subsequently evaluated considering different internationally accepted validation metrics. The proposed models are robust and non-random, demonstrating excellent predictive ability using test set compounds. We have also developed different kinds of consensus models using several validated individual models to improve the prediction quality for external set compounds. The present findings provide new insight for exploring the design of an aminoglycoside-derived polymer library based on different identified physicochemical properties as well as predict their property before their synthesis.

Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms

Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential endocrine-disrupting effects. Quantitative structure-activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure-activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.

In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides.

The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds. The BCF data of diverse organophosphate (n = 55) was collected from the Pesticide Properties Database and used as a model data set in the present study to explore physicochemical properties and structural alert concerning BCF. The structures were downloaded from Pubchem, ChemSpider database. Two splitting techniques (biological sorting and structure-based) were used to divide the whole dataset into training and test set compounds. The QSAR study was carried out with two-dimensional descriptors (2D) calculated from PaDEL by applying genetic algorithm (GA) as chemometric tools using QSARINS software. The models were statistically robust enough both internally as well as externally (Q2: 0.709-0.722, Q2 Ext: 0.717-0.903, CCC: 0.857-0.880). Overall molecular mass, presence of fused, and heterocyclic ring with electron-withdrawing groups affect the BCF value. The developed models reflected extended applicability domain (AD) and reliable predictions than the reported models for the studied chemical class. Finally, predictions of unknown organophosphate pesticides and the toxic nature of unknown organophosphate pesticides were commented on. These findings may be useful for the scientific community in prioritizing high potential pesticides of organophosphate class.

HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors.

Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two-dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best-performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well-curated benchmarking set named MUBD-HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel-scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i-Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i-Finder is available at https://github.com/jwxia2014/HDAC3i-Finder.

In Silico Modeling of Small Molecule Carboxamides as Inhibitors of SARS-CoV 3CL Protease: An Approach Towards Combating COVID-19.

The quantitative structure-activity relationship (QSAR) approach is most widely used for the prediction of biological activity of potential medicinal compounds. A QSAR model is developed by correlating the information obtained from chemical structures (numerical descriptors/ independent variables) with the experimental response values (the dependent variable). In the current study, we have developed a QSAR model to predict the inhibitory activity of small molecule carboxamides against severe acute respiratory syndrome coronavirus (SARS-- CoV) 3CLpro enzyme. Due to the structural similarity of this enzyme with SARS-CoV-2, the causative organism of the recent pandemic, the former may be used for the development of therapies against coronavirus disease 19 (COVID-19). The final multiple linear regression (MLR) model was based on four two-dimensional descriptors with definite physicochemical meaning. The model was strictly validated using different internal and external quality metrics. The model showed significant statistical quality in terms of determination coefficient (R2=0.748, adjusted R2 or R2 adj = 0.700), cross-validated leave-one-out Q2 (Q2=0.628) and external predicted variance R2 pred = 0.723. The final validated model was used for the prediction of external set compounds as well as to virtually design a new library of small molecules. We have also performed a docking analysis of the most active and least active compounds present in the dataset for comparative analysis and to explain the features obtained from the 2D-QSAR model. The derived model may be useful to predict the inhibitory activity of small molecules within the applicability domain of the model only based on the chemical structure information prior to their synthesis and testing.

Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice

Organophosphates (OPs) are highly toxic compounds, with widespread application in agricultural and chemical industries, whose introduction into the environment poses serious hazards to humans and ecological systems. To assess and ultimately mitigate these hazards, this study predicted the acute toxicity of OPs according to their chemical structure and administration route. The acute toxicity data of 161 OPs in two species via six different administration routes were manually collected and used to develop a series of quantitative structure–toxicity relationship (QSTR) models with robust and practical predictive abilities. The random forest algorithm was used to develop the models, employing both quantum chemical and two-dimensional descriptors according to OECD guidelines. Correlation results and feature similarities indicated that whereas acute toxicity data from rats and mice via the same administration route were combinable for modeling, data from different routes were not. Six QSTR models for each route in a single species and two QSTR models for a single route in the two species were constructed, achieving practical predictive performance. Despite significant variances in their datasets, the prediction models could predict the acute toxicity of novel or unknown OPs, realize rapid assessment, and provide guidance for regulatory decisions to reduce the hazards of OPs.

In silico development of anesthetics based on barbiturate and thiobarbiturate inhibition of GABAA

The inhibition of GABAA can be used in general anesthesia. Although, barbiturates and thiobarbiturates are used in anesthesia, the mechanism of their action hasn’t been established. QSAR modeling is a wieldy used technique in these cases and this study presents the QSAR modeling for a group of barbiturates and thiobarbiturates with determined anesthetic activity. Developed QSAR models were based on conformation independent and 2D descriptors as well as field contribution. As descriptors used for developing conformation independent QSAR models, (SMILES) notation and local invariants of the molecular graph were used. Monte Carlo optimization method was applied for building QSAR models for two defined activities. Methodology for developing QSAR models capable of dealing with the small dataset that integrates dataset curation, “exhaustive” double cross-validation and a set of optimal model selection techniques including consensus predictions was used. Two-dimensional descriptors with definite physicochemical meaning were used and modeling was done with the application of both partial least squares and multiple linear regression models with three latent variables related to simple and interpretable 2D descriptors. Different statistical methods, including novel method - the index of ideality of correlation, were used to test the quality of the developed models, especially robustness and predictability and all obtained results were good. In this study, obtained results indicate that there is a very good correlation between all developed models. Molecular fragments that account for the increase/decrease of a studied activity were defined and further used for the computer-aided design of new compounds as potential anesthetics.

Chemometric Modelling of Heat Release Capacity, Total Heat Release and Char Formation of Polymers to Assess Their Flammability Characteristics.

The quantitative structure-property relationship (QSPR) approach has widely been used to predict several physicochemical properties of materials employing the information obtained from their chemical structures (numerical descriptors). In the present work, we have generated three individual QSPR models for three different endpoints for a large number of polymers in order to determine their fire retardant property such as heat release capacity, total heat release, and %Char, using the only two-dimensional descriptors with definite physicochemical meaning. Relevant subsets of descriptors were selected employing a genetic algorithm approach; subsequently, the selected descriptors were utilised for the identification of the best combination of the variables for the model generation, while the final models were developed employing the partial least squares (PLS) regression algorithm. The generated models were rigorously validated using various internationally accepted internal and external validation metrics. All the models showed promising statistical quality in terms of determination coefficient (0.802, 0.842 and 0.826), cross-validated leave-one-out Q2 (0.759, 0.810 and 0.752) and predictive R2pred or Q2ext (0.810, 0.900 and 0.847) for HRC (nTraining =62, nTest =28), THR (nTraining =64, nTest =21) and %char (nTraining =49, nTest =21) datasets, respectively. All the certified models were used for prediction of flammability characteristics of 37 external set compounds, and further, the quality of prediction was determined by using the PRI software tool. The final models of HRC, THR and %Char formation of polymers may be useful to predict the flammability characteristics of polymers quickly before their synthesis and used as a better alternative approach to the experimental testing of flammability of polymers.

Video action recognition based on visual rhythm representation

Advances in video acquisition and storage technologies have promoted a great demand for automatic recognition of actions. The use of cameras for security and surveillance purposes has applications in several scenarios, such as airports, parks, banks, stations, roads, hospitals, supermarkets, industries, stadiums, schools. An inherent difficulty of the problem is the complexity of the scene under usual recording conditions, which may contain complex background and motion, multiple people on the scene, interactions with other actors or objects, and camera motion. Most recent databases are built primarily with shared recordings on YouTube and with snippets of movies, situations where these obstacles are not restricted. Another difficulty is the impact of the temporal dimension since it expands the size of the data, increasing computational cost and storage space. In this work, we present a methodology of volume description using the Visual Rhythm (VR) representation. This technique reshapes the original volume of the video into an image, where two-dimensional descriptors are computed. We investigated different strategies for constructing the representation by combining configurations in several image domains and traversing directions of the video frames. From this, we propose two feature extraction methods, Naïve Visual Rhythm (Naïve VR) and Visual Rhythm Trajectory Descriptor (VRTD). The first approach is the straightforward application of the technique in the original video volume, forming a holistic descriptor that considers action events as patterns and formats in the visual rhythm image. The second variation focuses on the analysis of small neighborhoods obtained from the process of dense trajectories, which allows the algorithm to capture details unnoticed by the global description. We tested our methods in eight public databases, one of hand gestures (SKIG), two in first person (DogCentric and JPL), and five in third person (Weizmann, KTH, MuHAVi, UCF11 and HMDB51). The results show that the developed techniques are able to extract motion elements along with format and appearance information, achieving competitive accuracy rates compared to state-of-the-art action recognition approaches.