Journal articles: 'Descriptor models'

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Ruggieri, Fabrizio, Alessandra Biancolillo, Angelo Antonio D’Archivio, Francesca Di Donato, Martina Foschi, Maria Anna Maggi, and Claudia Quattrociocchi. "Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography." Molecules 28, no. 7 (April 4, 2023): 3218. http://dx.doi.org/10.3390/molecules28073218.

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A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

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Grigorev, V. Yu, S. L. Solodova, D. E. Polianczyk, and O. A. Raevsky. "Classification models of structure - P-glycoprotein activity of drugs." Biomeditsinskaya Khimiya 62, no. 2 (2016): 173–79. http://dx.doi.org/10.18097/pbmc20166202173.

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Thirty three classification models of substrate specificity of 177 drugs to P-glycoprotein have been created using of the linear discriminant analysis, random forest and support vector machine methods. QSAR modeling was carried out using 2 strategies. The first strategy consisted in search of all possible combinations from 1¸5 descriptors on the basis of 7 most significant molecular descriptors with clear physico-chemical interpretation. In the second case forward selection procedure up to 5 descriptors, starting from the best single descriptor was used. This strategy was applied to a set of 387 DRAGON descriptors. It was found that only one of 33 models has necessary statistical parameters. This model was designed by means of the linear discriminant analysis on the basis of a single descriptor of H-bond (SCad). The model has good statistical characteristics as evidenced by results to both internal cross-validation, and external validation with application of 44 new chemicals. This confirms an important role of hydrogen bond in the processes connected with penetration of chemical compounds through a blood-brain barrier

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Gao, Xue-Yao, Kai-Peng Li, Chun-Xiang Zhang, and Bo Yu. "3D Model Classification Based on Bayesian Classifier with AdaBoost." Discrete Dynamics in Nature and Society 2021 (November 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/2154762.

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With the exponential increasement of 3D models, 3D model classification is crucial to the effective management and retrieval of model database. Feature descriptor has important influence on 3D model classification. Voxel descriptor expresses surface and internal information of 3D model. However, it does not contain topological structure information. Shape distribution descriptor expresses geometry relationship of random points on model surface and has rotation invariance. They can all be used to classify 3D models, but accuracy is low due to insufficient description of 3D model. This paper proposes a 3D model classification algorithm that fuses voxel descriptor and shape distribution descriptor. 3D convolutional neural network (CNN) is used to extract voxel features, and 1D CNN is adopted to extract shape distribution features. AdaBoost algorithm is applied to combine several Bayesian classifiers to get a strong classifier for classifying 3D models. Experiments are conducted on ModelNet10, and results show that accuracy of the proposed method is improved.

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Karelson, Mati, Uko Maran, Yilin Wang, and Alan R. Katritzky. "QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications." Collection of Czechoslovak Chemical Communications 64, no. 10 (1999): 1551–71. http://dx.doi.org/10.1135/cccc19991551.

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An overview on the development of QSPR/QSAR equations using various descriptor-mining techniques and multilinear regression analysis in the framework of the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program is given. The description of the methodologies applied in CODESSA is followed by the presentation of the QSAR and QSPR models derived for eighteen molecular activities and properties. The properties cover single molecular species, interactions between different molecular species, properties of surfactants, complex properties and properties of polymers. A review with 54 references.

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Afsar Jahan, Brij Kishore Sharma, and Vishnu Dutt Sharma. "Quantitative structure-activity relationship study on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a 1,2,4-Triazol-3-yl group as a ZBG." GSC Biological and Pharmaceutical Sciences 16, no. 1 (July 30, 2021): 251–65. http://dx.doi.org/10.30574/gscbps.2021.16.1.0199.

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QSAR study has been carried out on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a1,2,4-triazol-3-yl group as a ZBG in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the number of Sulfur atoms (descriptor nS), Balaban mean square distance index (descriptor MSD), molecular electrotopological variation (descriptor DELS), structural information content index of neighborhood symmetry of 2nd and 3rd order (descriptors SIC2 and SIC3), average valence connectivity index chi-4 (descriptor X4Av) in addition to 1st order Galvez topological charge index (descriptor JGI1) and global topological charge index (descriptor JGT) played a pivotal role in rationalization of MMP-13 inhibition activity of titled compounds. Atomic properties such as mass and volume in terms of atomic properties weighted descriptors MATS5m and MATS3v, and certain atom centred fragments such as CH2RX (descriptor C-006), X--CX--X (descriptor C-044), H attached to heteroatom (descriptor H-050) and H attached to C0(sp3) with 1X attached to next C (descriptor H-052) are also predominant to explain MMP-13 inhibition actions of fused pyrimidines. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high-quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

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Banagaaya, Nicodemus, and Wil Schilders. "Simulation of electromagnetic descriptor models using projectors." Journal of Mathematics in Industry 3, no. 1 (2013): 1. http://dx.doi.org/10.1186/2190-5983-3-1.

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Kazhdan, Michael, Bernard Chazelle, David Dobkin, Thomas Funkhouser, and Szymon Rusinkiewicz. "A Reflective Symmetry Descriptor for 3D Models." Algorithmica 38, no. 1 (October 24, 2003): 201–25. http://dx.doi.org/10.1007/s00453-003-1050-5.

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Kuz'min, Victor E., Liudmila N. Ognichenko, Viktor F. Zinchenko, Anatoly G. Artemenko, Angela O. Shyrykalova, and Anna V. Kozhukhar. "QSPR Models for Predicting of the Melting Points and Refractive Indices for Inorganic Substances." International Journal of Quantitative Structure-Property Relationships 5, no. 1 (January 2020): 1–21. http://dx.doi.org/10.4018/ijqspr.2020010101.

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The QSPR methodology is very promising for the creation of new materials, including materials based on inorganic compounds. However, the majority of QSPR descriptor systems are applicable only for organic molecules. In this work the 1D - QSPR descriptor system is proposed for analysis of the properties of various inorganic compounds. These descriptors are easily accessible, as they describe the most fundamental atom properties. The combinatorial schemes for computing these descriptors provide for their wide variety. The effectiveness of the proposed approach has been demonstrated to study the refractive indices and melting points of various inorganic compounds - components of potential optical film-forming materials. The developed QSPR models are suitable for the evaluative virtual screening of inorganic compounds; the mean relative error of prediction is 6 - 15%. The interpretation of the developed models reflects the nature of interatomic interactions in compounds with ionic structure.

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Dinesh Kumar Meena, Brij Kishore Sharma, and Raghuraj Parihar. "Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines." GSC Biological and Pharmaceutical Sciences 20, no. 3 (September 30, 2022): 107–19. http://dx.doi.org/10.30574/gscbps.2022.20.3.0344.

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QSAR study has been carried out on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the reciprocal hyper-detour index (descriptor Rww) and path/walk 5 Randic shape index (descriptor PW5) played a pivotal role in rationalization of CDK2 inhibition activity of titled compounds. Molecular weight (MW), mean atomic volume scaled on Carbon atom (Mv) and atomic properties such as mass and atomic Sanderson electronegativity in terms of atomic properties weighted descriptors MATS1m, MATS3e, MATS4e, GATS3e and GATS8e, certain atom centred fragments such as H attached to C0(sp3) no X attached to next C (descriptor H-046),R--CH--X (descriptor C-027) and R--CX--X (descriptor C-029) are also predominant to explain CDK2 inhibition actions of 6-substituted 2-arylaminopurines. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

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Tahboub, K. A., Th Schmidt, R. Schüpphaus, and P. C. Müller. "Comparison of Descriptor Models and Reduced Dynamic Models for Constrained Robots." IFAC Proceedings Volumes 24, no. 9 (September 1991): 9–14. http://dx.doi.org/10.1016/s1474-6670(17)51024-1.

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Strotov, V. V., P. V. Babyan, and S. A. Smirnov. "AERIAL OBJECT RECOGNITION ALGORITHM BASED ON CONTOUR DESCRIPTOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 91–95. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-91-2017.

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This paper describes the image recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the aerial object of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can gather set of training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types. The proposed orientation estimation algorithm showed good accuracy in all case studies.

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Bashiri, Fereshteh S., Reihaneh Rostami, Peggy Peissig, Roshan M. D’Souza, and Zeyun Yu. "An Application of Manifold Learning in Global Shape Descriptors." Algorithms 12, no. 8 (August 16, 2019): 171. http://dx.doi.org/10.3390/a12080171.

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With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific manifold learning technique, introduced in statistics and machine learning, to develop a global, spectral-based shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant and noise-resistant descriptor. The extensive experiments performed in this study using two standard 3D shape benchmarks—high-resolution TOSCA and McGill datasets—demonstrate that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace–Beltrami operator. The proposed method significantly outperforms state-of-the-art spectral descriptors in shape retrieval and classification. The proposed descriptor is limited to closed manifolds due to its inherited inability to accurately handle manifolds with boundaries.

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Si, Yan, Wenyi Han, Die Yu, Baizhong Bao, Jian Duan, Xiaobin Zhan, and Tielin Shi. "MixedSCNet: LiDAR-Based Place Recognition Using Multi-Channel Scan Context Neural Network." Electronics 13, no. 2 (January 18, 2024): 406. http://dx.doi.org/10.3390/electronics13020406.

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In the realm of LiDAR-based place recognition tasks, three predominant methodologies have emerged: manually crafted feature descriptor-based methods, deep learning-based methods, and hybrid methods that combine the former two. Manually crafted feature descriptors often falter in reverse visits and confined indoor environments, while deep learning-based methods exhibit limitations in terms of generalization to distinct data domains. Hybrid methods tend to fix these problems, albeit at the cost of an expensive computational burden. In response to this, this paper introduces MixedSCNet, a novel hybrid approach designed to harness the strengths of manually crafted feature descriptors and deep learning models while keeping a relatively low computing overhead. MixedSCNet starts with constructing a BEV descriptor called MixedSC, which takes height, intensity, and smoothness into consideration simultaneously, thus offering a more comprehensive representation of the point cloud. Subsequently, MixedSC is fed into a compact Convolutional Neural Network (CNN), which further extracts high-level features, ultimately yielding a discriminative global point cloud descriptor. This descriptor is then employed for place retrieval, effectively bridging the gap between manually crafted feature descriptors and deep learning models. To substantiate the efficacy of this amalgamation, we undertake an extensive array of experiments on the KITTI and NCLT datasets. Results show that MixedSCNet stands out as the sole method showcasing state-of-the-art performance across both datasets, outperforming the other five methods while maintaining a relatively short runtime.

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Xia, Liang-Yong, Qing-Yong Wang, Zehong Cao, and Yong Liang. "Descriptor Selection Improvements for Quantitative Structure-Activity Relationships." International Journal of Neural Systems 29, no. 09 (October 28, 2019): 1950016. http://dx.doi.org/10.1142/s0129065719500163.

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Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure–activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and [Formula: see text]-values.

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Rozin, Mikhail, Valeriy Svechkarev, and Zhanna Tumakova. "Descriptor of self-determination based on cognitive models." SHS Web of Conferences 72 (2019): 04005. http://dx.doi.org/10.1051/shsconf/20197204005.

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There is interest of scientists of various applied fields of science in the possibilities of research within the framework of the concept of self-determination. It is suggested to concentrate on the general semantic content of self-determination, which is inherent in it regardless of the applied focus of research. It is shown that self-determination is manifested as a system property of the sociocultural system activity as a whole. Furthermore, the level of self-determination of the sociocultural system and the potential for development in the target direction are directly proportional to the degree of semantic and causal integration of its elements. It has been identified that a set of factors united by cause-and-effect relationships in an oriented named signed graph reflects both the integration causation and the logical-semantic target organization of the sociocultural system. Thus, based on the figurative approximation of the essence of the studied system property of self-determination and its visual metaphorical representation in the form of a cognitive model, we get the opportunity to study the self-determination descriptor. Analysis of the causal integration of the elements of the sociocultural system or process using cognitive models makes it quite simple to determine the level of self-determination of a system not only at a qualitative but also at a quantitative level.

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Estrada-Manzo, Víctor, Zsófia Lendek, and Thierry Marie Guerra. "Discrete-time Takagi-Sugeno descriptor models: observer design." IFAC Proceedings Volumes 47, no. 3 (2014): 7965–69. http://dx.doi.org/10.3182/20140824-6-za-1003.00978.

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Chen, Jing, Yunjing Gao, Xiaoyan Hu, Dongdong Qin, and Xiaoquan Lu. "Descriptor selection based on variable stability for predicting inhibitor activity." Journal of Theoretical and Computational Chemistry 16, no. 08 (December 2017): 1750074. http://dx.doi.org/10.1142/s0219633617500742.

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Quantitative structure-activity relationship (QSAR) has been a technique to study the relationship between chemical structures and properties, and variable selection is an important problem for finding the informative variables and building reliable models. A variable selection method based on variable stability is proposed and used for selecting the informative descriptors in the QSAR model of inhibitors. In the method, a series of models are built by leave-one-out cross validation (LOOCV), and variable stability is defined as the ratio of the absolute mean value and standard deviation of the regression coefficients in the models for a descriptor. Therefore, the descriptors with larger stabilities are more informative to the model. To further enhance the difference among the descriptors, an exponential parameter is used to modify the standard deviation. The results show that 13 descriptors are selected as informative ones from 1217 descriptors for the QSAR model of inhibitors. An effective prediction model can be constructed by them.

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Tseng, Yufeng J., Anton J. Hopfinger, and Emilio Xavier Esposito. "The great descriptor melting pot: mixing descriptors for the common good of QSAR models." Journal of Computer-Aided Molecular Design 26, no. 1 (December 27, 2011): 39–43. http://dx.doi.org/10.1007/s10822-011-9511-4.

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Gupta, Ashutosh Kumar, Arindam Chakraborty, Santanab Giri, Venkatesan Subramanian, and Pratim Chattaraj. "Toxicity of Halogen, Sulfur and Chlorinated Aromatic Compounds." International Journal of Chemoinformatics and Chemical Engineering 1, no. 1 (January 2011): 61–74. http://dx.doi.org/10.4018/ijcce.2011010105.

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In this paper, quantitative–structure–toxicity–relationship (QSTR) models are developed for predicting the toxicity of halogen, sulfur and chlorinated aromatic compounds. Two sets of compounds, containing mainly halogen and sulfur inorganic compounds in the first set and chlorinated aromatic compounds in the second, are investigated for their toxicity level with the aid of the conceptual Density Functional Theory (DFT) method. Both sets are tested with the conventional density functional descriptors and with a newly proposed net electrophilicity descriptor. Associated R2, R2CV and R2adj values reveal that in the first set, the proposed net electrophilicity descriptor (??±) provides the best result, whereas in the second set, electrophilicity index (?) and a newly proposed descriptor, net electrophilicity index (??±) provide a comparable performance. The potential of net electrophilicity index to act as descriptor in development of QSAR model is also discussed.

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Berdnyk, M. I., A. B. Zakharov, and V. V. Ivanov. "Application Of L1- Regularization Approach In QSAR Problem. Linear Regression And Artificial Neural Networks." Methods and Objects of Chemical Analysis 14, no. 2 (2019): 79–90. http://dx.doi.org/10.17721/moca.2019.79-90.

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One of the primary tasks of analytical chemistry and QSAR/QSPR researches is building of prognostic regression equations based on descriptors sets. The one of the most important problems here is to decrease the number of descriptors in the initial descriptor set which is usually way too big. In current investigation the descriptor set is proposed to be reduced employing the least absolute shrinkage and selection operator (LASSO) approach. Decreased descriptor sets were used for calculations with application of the following QSAR/QSPR methods: ordinary least squares (OLS), the least absolute deviation (LAD) regressions and artificial neural networks (ANN). Contrary to aforementioned methods principal component regression (PCR) and partial least squares (PLS) approaches can produce solutions containing numerous descriptors. In this article we compared the viability of these two different descriptor handling ideologies in application to molecular chemical and physical properties prediction. From the obtained results it is possible to see that there are tasks for which PCR and PLS approaches can fail to produce accurate regression equations. At the same time, methods OLS and LAD that use small amount of descriptors can provide viable solutions for the same cases. It was shown that these small sets of descriptors selected with LASSO approach can be used in ANN to obtain models with even better internal validation characteristics.

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KUMAR, PAWAN, PRITI SINGH, R. K. SINGH, M. ANSARI, and MOHD ADIL KHAN. "Quantum mechanical parameters-based study of aryl sulphonamides as 5-HT6 serotonin ligand using DFT methods." Romanian Journal of Biophysics 34, no. 1 (February 27, 2024): 13–26. http://dx.doi.org/10.59277/rjb.2024.1.02.

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In the present work, quantum mechanical descriptors have been used for the development of quantitative structure activity relationship (QSAR) models for the thirty-two derivatives of aryl sulphonamide and sulfone based 5-HT6 antagonists. Among several classes of serotonin 5-HT6 receptor ligands, aryl sulphonamides reported better affinity towards the receptor. Drugs acting as serotonin ligands are useful in the treatment of a variety of mental disorders. The descriptors that have been used in our study are total energy, log P, molecular weight, dipole moment, heat of formation, LUMO energy, HOMO energy and electrophilicity index. The geometry optimization and evaluation of descriptors of all the compounds has been done with the help of CAChe Pro software using DFT-B88-LYP method with double-zeta valence polarized (DZVP) basis set. The best QSAR model for this set of derivatives has been obtained by combination of descriptors molecular weight, dipole moment and heat of formation. The descriptor molecular weight gives a mono-parametric QSAR model with remarkable predictive ability with positive contribution. The descriptor molecular weight is present in all best bi-parametric and tri-parametric QSAR models. Statistical parameters such as correlation coefficient, cross validation coefficient, standard error etc. were used to validate the predictability of QSAR models.

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Zhang, Jianxin, Xiangguo Wei, Jing Dong, and Bin Liu. "Aggregated Deep Global Feature Representation for Breast Cancer Histopathology Image Classification." Journal of Medical Imaging and Health Informatics 10, no. 11 (November 1, 2020): 2778–83. http://dx.doi.org/10.1166/jmihi.2020.3215.

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Convolutional neural networks (CNNs), successfully used in a great number of medical image analysis applications, have also achieved the state-of-the-art performance in breast cancer histopathology image (BCHI) classification problem recently. However, due to the large varieties among within-class images and insufficient data volume, it is still a challenge to obtain more competitive results by using deep CNN models alone. In this paper, we aim to explore the combination of CNN models with a milestone feature representation method in visual tasks, i.e., vector of locally aggregated descriptors (VLAD), for the BCHI classification, and further propose a novel aggregated deep global feature representation (ADGFR) for this problem. ADGFR adopts the deep features that are extracted from the fully connected layer to form an individual descriptor vector, and augments input images to generate different descriptors for achieving the final aggregated descriptor vector. The individual descriptor vector can effectively keep the global features of benign and malignant image, whose discriminability is further reinforced by the aggregate operation, leading to the more discriminant capability of ADGFR for BCHI. Extensive experiments on the public Break His dataset illuminate that our ADGFR can achieve the optimal classification accuracies of 95.05% at image level and 95.50% at patient level, respectively.

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Bahrami, Behzad, and Masoud Shafiee. "Fuzzy Descriptor Models for Earthquake Time Prediction Using Seismic Time Series." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no. 04 (August 2015): 505–19. http://dx.doi.org/10.1142/s0218488515500221.

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In recent years, singular systems and fuzzy descriptor have attracted a lot of researchers' attention due to their wide practical applications for modeling complex phenomena. In this study, one approach proposed is the fuzzy clustering algorithm based on linear structures to identify the neuro Fuzzy local linear models. Additionally fuzzy descriptor models, a recently proposed neuro fuzzy interpretation of locally linear models, are implemented because of their promise for intuitive incremental learning algorithms e.g. Generalized Fuzzy Clustering Variety (GFCV). The results from the fuzzy descriptor models are compared to the results of several other methods. An efficient technique, based on the error indices of multiple validation sets, is used to optimize the number of neurons and prevent the algorithm from over fitting. The scope of this work is to reveal the advantages of fuzzy descriptor models and compare them to the most successful neural and neuro fuzzy approaches based on prediction accuracy, generalization, and computational complexity. The proposed solution is shown to accurately forecast seismic time series, outperforming several other methods.

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Sun, Zhiyu, Yusen He, Andrey Gritsenko, Amaury Lendasse, and Stephen Baek. "Embedded spectral descriptors: learning the point-wise correspondence metric via Siamese neural networks." Journal of Computational Design and Engineering 7, no. 1 (February 1, 2020): 18–29. http://dx.doi.org/10.1093/jcde/qwaa003.

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Abstract A robust and informative local shape descriptor plays an important role in mesh registration. In this regard, spectral descriptors that are based on the spectrum of the Laplace–Beltrami operator have been a popular subject of research for the last decade due to their advantageous properties, such as isometry invariance. Despite such, however, spectral descriptors often fail to give a correct similarity measure for nonisometric cases where the metric distortion between the models is large. Hence, they are not reliable for correspondence matching problems when the models are not isometric. In this paper, it is proposed a method to improve the similarity metric of spectral descriptors for correspondence matching problems. We embed a spectral shape descriptor into a different metric space where the Euclidean distance between the elements directly indicates the geometric dissimilarity. We design and train a Siamese neural network to find such an embedding, where the embedded descriptors are promoted to rearrange based on the geometric similarity. We demonstrate our approach can significantly enhance the performance of the conventional spectral descriptors by the simple augmentation achieved via the Siamese neural network in comparison to other state-of-the-art methods.

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Roy, Partha Pratim, Jagadish Singh, and Supratim Ray. "Exploring QSAR of Some Antitubercular Agents." International Journal of Quantitative Structure-Property Relationships 3, no. 1 (January 2018): 25–42. http://dx.doi.org/10.4018/ijqspr.2018010102.

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The in vitro vero cell cytotoxicity of 93 antitubercular compounds belonging to the classes of chiral pentaamines, bis-cyclic guanidines, bis-cyclic thioureas, bis-cyclic piperazines, and quinolylhydrazones has been modeled in the present quantitative structure-activity relationship (QSAR) study. Genetic function approximation followed by multiple linear regression (GFA-MLR) based on the mean absolute error (MAE) based criteria was used as the chemometric tool for the model development using 2D descriptors available from open source PaDEL-Descriptor. The developed model was statistically robust (Q2:0.868, R2pred:0.896). Additionally, the r2m metrics, concordance correlation coefficient (CCC) and MAE criteria for the test set validation were also tested. The models indicate importance of autocorrelation descriptors weighted by charge (ATSc3, ATSc5) and some electrotopological state atom type descriptor of fragments -NH-,-O-, >N- for cytotoxicity. The applicability domains of GFA-MLR models were also studied by applying both leverage and standardized residual approaches.

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Kovacevic, Strahinja, Milica Karadzic-Banjac, Lidija Jevric, and Sanja Podunavac-Kuzmanovic. "Linear quantitative structure-ecotoxicity relationship modeling of a series of symmetrical triazine derivatives based on physicochemical parameters." Acta Periodica Technologica, no. 54 (2023): 255–64. http://dx.doi.org/10.2298/apt2354255k.

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The present study reports the Quantitative Structure-Ecotoxicity Relationship (QSER) analysis of a series of 21 1,3,5-triazine derivatives based on multiple-linear regression (MLR) method. The ecotoxicity data were estimated by using in silico approach and included the following parameters: acute algae toxicity (AAT), acute daphnia toxicity (ADT), Daphnia Magna LC50 48h/EPA (DMepa) and Daphnia Magna LC50 48h/DEMETRA (DMdemetra). The ecotoxicity data were correlated with molecular descriptors selected by using the stepwise selection method. The considered molecular descriptors are lipophilicity descriptors (CrippenLogP, ALogp2), Autocorrelation Descriptor Mass (ATSm1, ATSm2, ATSm3, ATSm4), Autocorrelation Descriptor Charge (ATSc2), minimum E-states for (strong) hydrogen bond acceptors (minHBa), maximum E-states for (strong) hydrogen bond acceptors (maxHBa), second kappa shape index (Kier2), maximum atom-type E-State: ?:N:? (maxaaN), sum of path lengths starting from nitrogens (WTPT-5) and McGowan characteristic volume (McGowan_Volume). The modeling resulted in four statistically valid MLR models. The models were validated by the internal and external validation approaches. The external validation confirmed high predictive ability of the established MLRs.

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Chen, Yuanhong, Yu Tian, Guansong Pang, and Gustavo Carneiro. "Deep One-Class Classification via Interpolated Gaussian Descriptor." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 383–92. http://dx.doi.org/10.1609/aaai.v36i1.19915.

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One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their distance to the Gaussian centre and the standard deviation of these distances, offering the model a discriminability w.r.t. the given samples during training. The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples. This enables our model to learn the data description based on the representative normal samples rather than fringe or anomalous samples, resulting in significantly improved normality description. In extensive experiments on diverse popular benchmarks, including MNIST, Fashion MNIST, CIFAR10, MVTec AD and two medical datasets, IGD achieves better detection accuracy than current state-of-the-art models. IGD also shows better robustness in problems with small or contaminated training sets.

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Madaan, Neetika, N. Raveendran Shiju, and Gadi Rothenberg. "Predicting the performance of oxidation catalysts using descriptor models." Catalysis Science & Technology 6, no. 1 (2016): 125–33. http://dx.doi.org/10.1039/c5cy00932d.

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Sari, B., O. Bachelier, J. Bosche, N. Maamri, and D. Mehdi. "Pole placement in non connected regions for descriptor models." Mathematics and Computers in Simulation 81, no. 12 (August 2011): 2617–31. http://dx.doi.org/10.1016/j.matcom.2011.05.002.

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Maschke, B. M., and M. Villarroya. "Properties of descriptor systems arising from bond graph models." Mathematics and Computers in Simulation 39, no. 5-6 (November 1995): 491–97. http://dx.doi.org/10.1016/0378-4754(95)00109-1.

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SHAPOVAL, A. B., and M. G. SHNIRMAN. "SAND DENSITY AS SANDPILE DESCRIPTOR." International Journal of Modern Physics C 19, no. 06 (June 2008): 995–1006. http://dx.doi.org/10.1142/s0129183108012637.

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We investigate a collection of one-parametric families of isotropic sandpile models. The models involve the square lattice slowly accumulating the grains and quickly transferring them as the local piles become over-critical. The paper groups the sand-piles with respect to two features influencing the model dynamics. They are the value of the local transfer's stochasticity and the number of the transferred grains. Every pair generates one-parametric family of the sand-piles. The parameter reflects the relative height of an over-critical pile with respect to the incoming flow of sand. If the stochasticity disappears with the growth of the parameter, the families with the fixed number of the transferred grains have much in common with Bak et al.'s sand-pile [Phys. Rev. Lett.59, 381 (1987)], while the families, whose over-critical piles lose all their grains, tend to the Zhang sand-pile [Phys. Rev. Lett.63, 470 (1989)]. The families with non-disappearing variance give rise to new properties described in terms of the probability distribution of the pile heights.

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Rabtti, El, Maja Natic, Dusanka Milojkovic-Opsenica, Jelena Trifkovic, Tomislav Tosti, Ivan Vuckovic, Vlatka Vajs, and Zivoslav Tesic. "Quantitative structure-toxicity relationship study of some natural and synthetic coumarins using retention parameters." Journal of the Serbian Chemical Society 77, no. 10 (2012): 1443–56. http://dx.doi.org/10.2298/jsc120716091r.

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Four lipophilicity descriptors (RM0, b, C0, PC1) for twelve coumarine derivatives were determined by reversed-phase thin-layer chromatography in order to analyze which descriptor best describes the lipophilicity of coumarines investigated. Moreover, possible chemical toxicity of coumarins, expressed as the probability of a compound to cause organ-specific health effects, was calculated using ACD/Tox Suite program. The quantitative relationships between toxicity and molecular descriptors, including experimentally determined lipophilicity descriptors obtained in current study, were investigated using partial least square regression. The best models were obtained for kidney and liver health effects. Quantitative structure-toxicity relationship models revealed the importance of electric polarization descriptors, size descriptors and lipophilicity descriptors. Obtained models were used for the selection of the structural features of the compounds that are significantly affecting their absorption, distribution, metabolism, excretion, and toxicity.

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Wang, Wufan, Lei Zhang, and Hua Huang. "Revisiting Unsupervised Local Descriptor Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 2680–88. http://dx.doi.org/10.1609/aaai.v37i3.25367.

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Constructing accurate training tuples is crucial for unsupervised local descriptor learning, yet challenging due to the absence of patch labels. The state-of-the-art approach constructs tuples with heuristic rules, which struggle to precisely depict real-world patch transformations, in spite of enabling fast model convergence. A possible solution to alleviate the problem is the clustering-based approach, which can capture realistic patch variations and learn more accurate class decision boundaries, but suffers from slow model convergence. This paper presents HybridDesc, an unsupervised approach that learns powerful local descriptor models with fast convergence speed by combining the rule-based and clustering-based approaches to construct training tuples. In addition, HybridDesc also contributes two concrete enhancing mechanisms: (1) a Differentiable Hyperparameter Search (DHS) strategy to find the optimal hyperparameter setting of the rule-based approach so as to provide accurate prior for the clustering-based approach, (2) an On-Demand Clustering (ODC) method to reduce the clustering overhead of the clustering-based approach without eroding its advantage. Extensive experimental results show that HybridDesc can efficiently learn local descriptors that surpass existing unsupervised local descriptors and even rival competitive supervised ones.

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Kudrinskiy, Alexey, Pavel Zherebin, Alexander Gusev, Olga Shapoval, Jaeho Pyee, Georgy Lisichkin, and Yurii Krutyakov. "New Relevant Descriptor of Linear QNAR Models for Toxicity Assessment of Silver Nanoparticles." Nanomaterials 10, no. 8 (July 25, 2020): 1459. http://dx.doi.org/10.3390/nano10081459.

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The use of silver nanoparticles (NPs) in medical, industrial and agricultural fields is becoming more widespread every year. This leads to an increasing number of experimental toxicological and microbiological studies of silver NPs aimed at establishing the risk–benefit ratio for their application. The following key parameters affecting the biological activity of silver dispersions are traditionally taken into consideration: mean diameter of NPs, surface potential of NPs and equilibrium concentration of Ag+. These characteristics are mainly predetermined by the chemical nature of the capping agent used for stabilization. However, the extent to which they influence the biological activity and the toxicity of silver NPs varies greatly. In this work, dispersions of silver NPs stabilized with a wide array of substances of different chemical nature were used for quantitative evaluation of whether the various measurable properties of silver NPs fit as descriptors of linear QNAR (quantitative nanostructure–activity relationship) models for silver NP toxicity evaluation with respect to a model eukaryotic microorganism—Saccharomyces cerevisiae yeast cells. It was shown that among the factors that determine silver NP toxicity, the charge of particles, their colloidal stability and the ability to generate Ag+ ions carry more importance than the descriptors related to the particle size. A significant synergistic effect between the ζ-potential and the colloidal stability of silver NPs on their toxicity was also discovered. Following this, a new descriptor has been proposed for the integral characterization of the silver dispersion colloidal stability. According to the obtained data, it can be considered applicable for building QNAR models of higher efficacy. The validity testing of the proposed model for theoretical prediction of silver NP toxicity using a wide range of living organisms has shown that this new descriptor correlates with toxicity much better compared to most traditionally used descriptors. Consequently, it seems promising in terms of being used not only in situations involving the rather narrow array of the objects tested, but also for the construction of silver NP toxicity models with respect to other living organisms.

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Nikolova-Jeliazkova, Nina, and Joanna Jaworska. "An Approach to Determining Applicability Domains for QSAR Group Contribution Models: An Analysis of SRC KOWWIN." Alternatives to Laboratory Animals 33, no. 5 (October 2005): 461–70. http://dx.doi.org/10.1177/026119290503300510.

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QSAR model predictions are most reliable if they come from the model's applicability domain. The Setubal Workshop report provides a conceptual guidance for defining a (Q)SAR applicability domain. However, an operational definition is necessary for applying this guidance in practice. It should also permit the design of an automatic (computerised) procedure for determining a model's applicability domain. This paper attempts to address this need for models that use a large number of descriptors (for example, group contribution-based models). The high dimensionality of these models imposes specific computational restrictions on estimating the interpolation region. The Syracuse Research Corporation KOWWIN model for prediction of the n-octanol/water partition coefficient is analysed as a case study. This is a linear regression model that uses 508 fragment counts and correction factors as descriptors, and is based on the group contribution approach. We conclude that the applicability domain estimation by descriptor ranges, combined with Principal Component rotation as a data pre-processing step, is an acceptable compromise between estimation accuracy and the amount of data in the training set.

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Salamh, A. B. S., and H. I. Akyüz. "A Novel Feature Extraction Descriptor for Face Recognition." Engineering, Technology & Applied Science Research 12, no. 1 (February 12, 2022): 8033–38. http://dx.doi.org/10.48084/etasr.4624.

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This paper presents a new feature extraction technique for face recognition. The new model, called multi-descriptor, is based on the well-known method of local binary patterns. It involves many different neighborhoods of the central pixel. Its unique advantage is that this descriptor allows the use of different neighborhood sizes instead of only one point. This structure ensures reasonable effectiveness and also provides the possibility to obtain a different distribution of features. Based on the new descriptor, a face recognition model using the pairwise feature descriptor based on the proposed descriptor was developed in this work, and local binary patterns were created to investigate the similarity and dissimilarity between the two models. For both models, the training was done using the support vector machine method on different face databases to overcome face recognition problems such as camera distance, expression, large head size, and illumination variations. The proposed technique achieved perfect accuracy on almost all tested databases including the Extended Yale B and Grimace database.

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Appell, Michael, David L. Compton, and Kervin O. Evans. "Predictive Quantitative Structure–Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds." Methods and Protocols 4, no. 1 (December 27, 2020): 2. http://dx.doi.org/10.3390/mps4010002.

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Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R2 values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds.

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Ye, Minying, and Kanji Tanaka. "Improved Visual Robot Place Recognition of Scan-Context Descriptors by Combining with CNN and SVM." Journal of Robotics and Mechatronics 35, no. 6 (December 20, 2023): 1622–28. http://dx.doi.org/10.20965/jrm.2023.p1622.

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Visual place recognition from a 3D laser LiDAR is one of the most active research areas in robotics. Especially, learning and recognition of scene descriptors, such as scan context descriptors that map 3D point clouds to 2D point clouds, is one of the promising research directions. Although the scan-context descriptor has a sufficiently high recognition performance, it is still expensive image data and cannot be handled with low-capacity non-deep models. In this paper, we explore the task of compressing the scan context descriptor model while maintaining its recognition performance. To this end, the proposed approach slightly modifies the off-the-shelf classifier model of convolutional neural networks (CNN) from its basis, by replacing the SoftMax part with a support vector machine (SVM). Experiments with publicly available NCLT dataset validate the effectiveness of the proposed approach.

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Mechmeche, Chokri, Hamdi Habib, Mickael Rodrigues, and Naceur BenHadjBraiek. "State and Unknown Inputs Estimations for Multi-Models Descriptor Systems." American Journal of Computational and Applied Mathematics 2, no. 3 (August 31, 2012): 86–93. http://dx.doi.org/10.5923/j.ajcam.20120203.04.

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Mirmomeni, Masoud, Caro Lucas, Babak Nadjar Araabi, and Masoud Shafiee. "Forecasting sunspot numbers with the aid of fuzzy descriptor models." Space Weather 5, no. 8 (August 2007): n/a. http://dx.doi.org/10.1029/2006sw000289.

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41

Dutta, Debojyoti, Rajarshi Guha, David Wild, and Ting Chen. "Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models." Journal of Chemical Information and Modeling 47, no. 3 (April 4, 2007): 989–97. http://dx.doi.org/10.1021/ci600563w.

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Katritzky, Alan R., Dimitar A. Dobchev, Svetoslav Slavov, and Mati Karelson. "Legitimate Utilization of Large Descriptor Pools for QSPR/QSAR Models." Journal of Chemical Information and Modeling 48, no. 11 (October 29, 2008): 2207–13. http://dx.doi.org/10.1021/ci8002073.

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Bloit, Julien, Nicolas Rasamimanana, and Frédéric Bevilacqua. "Modeling and segmentation of audio descriptor profiles with segmental models." Pattern Recognition Letters 31, no. 12 (September 2010): 1507–13. http://dx.doi.org/10.1016/j.patrec.2009.11.003.

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Estrada-Manzo, Victor, Zsófia Lendek, and Thierry Marie Guerra. "Generalized LMI observer design for discrete-time nonlinear descriptor models." Neurocomputing 182 (March 2016): 210–20. http://dx.doi.org/10.1016/j.neucom.2015.12.033.

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Guerra, Thierry Marie, Victor Estrada-Manzo, and Zsófia Lendek. "Observer design for Takagi–Sugeno descriptor models: An LMI approach." Automatica 52 (February 2015): 154–59. http://dx.doi.org/10.1016/j.automatica.2014.11.008.

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Zhao, Chunhui, Wenxuan Wang, Yiming Yan, Nan Su, Shou Feng, Wei Hou, and Qingyu Xia. "A Novel Object-Level Building-Matching Method across 2D Images and 3D Point Clouds Based on the Signed Distance Descriptor (SDD)." Remote Sensing 15, no. 12 (June 7, 2023): 2974. http://dx.doi.org/10.3390/rs15122974.

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In this work, a novel object-level building-matching method using cross-dimensional data, including 2D images and 3D point clouds, is proposed. The core of this method is a newly proposed plug-and-play Joint Descriptor Extraction Module (JDEM) that is used to extract descriptors containing buildings’ three-dimensional shape information from object-level remote sensing data of different dimensions for matching. The descriptor is named Signed Distance Descriptor (SDD). Due to differences in the inherent properties of different dimensional data, it is challenging to match buildings’ 2D images and 3D point clouds on the object level. In addition, features extracted from the same building in images taken at different angles are usually not exactly identical, which will also affect the accuracy of cross-dimensional matching. Therefore, the question of how to extract accurate, effective, and robust joint descriptors is key to cross-dimensional matching. Our JDEM maps different dimensions of data to the same 3D descriptor SDD space through the 3D geometric invariance of buildings. In addition, Multi-View Adaptive Loss (MAL), proposed in this paper, aims to improve the adaptability of the image encoder module to images with different angles and enhance the robustness of the joint descriptors. Moreover, a cross-dimensional object-level data set was created to verify the effectiveness of our method. The data set contains multi-angle optical images, point clouds, and the corresponding 3D models of more than 400 buildings. A large number of experimental results show that our object-level cross-dimensional matching method achieves state-of-the-art outcomes.

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Arslan, Sibel, and Celal Ozturk. "Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification." Applied Sciences 9, no. 9 (May 10, 2019): 1930. http://dx.doi.org/10.3390/app9091930.

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Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

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Mswahili, Medard Edmund, Min-Jeong Lee, Gati Lother Martin, Junghyun Kim, Paul Kim, Guang J. Choi, and Young-Seob Jeong. "Cocrystal Prediction Using Machine Learning Models and Descriptors." Applied Sciences 11, no. 3 (February 1, 2021): 1323. http://dx.doi.org/10.3390/app11031323.

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Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.

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YANG, XIBEI, ZEHUA CHEN, HUILI DOU, MING ZHANG, and JINGYU YANG. "NEIGHBORHOOD SYSTEM BASED ROUGH SET: MODELS AND ATTRIBUTE REDUCTIONS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 03 (May 17, 2012): 399–419. http://dx.doi.org/10.1142/s0218488512500201.

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The neighborhood system based rough set is a generalization of Pawlak's rough set model since the former uses the neighborhood system instead of the partition for constructing target approximation. In this paper, the neighborhood system based rough set approach is employed to deal with the incomplete information system. By the coverings induced by the maximal consistent blocks and the support sets of the descriptors, respectively, two neighborhood systems based rough sets are explored. By comparing with the original maximal consistent block and descriptor based rough sets, the neighborhood system based rough sets hold the same lower approximations and the smaller upper approximations. Furthermore, the concept of attribute reduction is introduced into the neighborhood systems and the corresponding rough sets. The judgement theorems and discernibility functions to compute reducts are also presented. Some numerical examples are employed to substantiate the conceptual arguments.

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Glienke, Judith, Michael Stelter, and Patrick Braeutigam. "How do water matrices influence QSPR models in wastewater treatment?–A case study on the sonolytic elimination of phenol derivates." PLOS Water 2, no. 11 (November 14, 2023): e0000201. http://dx.doi.org/10.1371/journal.pwat.0000201.

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As the demand of freshwater increases with simultaneously aggravated climatic challenges, the development of efficient and effective water purification methods is of high importance. Qualitative Structure-Property Relationships (QSPRs) can support this process by calculating a correlation between the molecular structure and the degradability of water pollutants in a defined removal procedure, expressed by the kinetic constant of their removal. This can help to receive more mechanistical interpretation of the underlying process, but also to reduce experimental costs and time. As most QSPR models in wastewater treatment research are based on experimental data using ultrapure water as reaction solutions, it is still unknown to which extent QSPR models for different water matrices differ from each other with regard to selected descriptors and performance. Therefore, in this study the sonolytic degradation of 32 phenol derivates was investigated for three different water matrices (NaCl, Glucose, NaCl+Glucose) and compared to a previous study in ultrapure water. With only very few exceptions, the addition of water additives reduced the degradability of the target analytes. Based on these four datasets, QSPR modelling, respecting all five OECD principles for reliable QSPR models, were performed using numerous internal and external validations as well as statistical quality assurances to ensure good regression abilities as well as stability and predictivity. As the final four models were compared, it was observed that the descriptor selection and model calculation were highly impacted by the water additives. This was also confirmed when the descriptor pools of the best 10 models for each water composition were compared, as the descriptor pools were also highly dissimilar, indicating a shift in structural importance when changing the water composition. It could be shown that water matrices significantly influence the results of QSPR modelling even at very low concentrations of the matrix components.

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Journal articles on the topic 'Descriptor models'

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