Set Of Descriptors Research Articles

Automatic Expansion of Metadata Standards for Historic Costume Collections

Objective: This project focuses on Artificial Intelligence (AI) supported enhancement of descriptive metadata for fashion collections (otherwise known as costume or dress and textile collections) through expanding costume-specific controlled terms. The authors use Natural Language Processing (NLP) techniques along with a human-in-the-loop process to support selection of descriptive terms for inclusion in the controlled terms of a metadata schema. This project seeks to expand upon existing domain-specific schema, Costume Core, by enhancing the schema with a comprehensive set of descriptors. This enhancement will allow for more accurate and detailed descriptions of costume artifacts. This article describes this process and the outcomes of AI approaches for providing this metadata expansion, who this process is for, ethical considerations, and lessons learned.Methods: We approached our problem with an investigation into using word embeddings to aid in supporting the suggesting of new metadata terms. Several word embedding models were applied with the more descriptive one chosen for final use in a human-in-the-loop selection process. This selection process provided domain experts to identify which terms chosen by the model are of relevant value. We then compare what was chosen by the domain experts and what the model produced to get an idea as to the value the model provides in the process of metadata expansion.Results: The metadata expansion process was a success. An AI supported process aided domain experts in choosing relevant terms to include in their metadata schema. Therefore, the results were a methodology for using identified AI models for the problem, an interactive system to aid the domain experts (software system), and how to evaluate the results.Conclusion: The application of AI technologies (word embeddings) provided a successful pipeline for supporting domain experts to expand the metadata schema with additional descriptors.  Enhancing the metadata schema with additional descriptors improves its usability for fashion collection managers and allows for a more precise description of the artifacts. As a result, many new terms that were expertly chosen were added to the metadata schema.

The empty pelvis syndrome: a core data set from the PelvEx collaborative.

Empty pelvis syndrome (EPS) is a significant source of morbidity following pelvic exenteration (PE), but is undefined. EPS outcome reporting and descriptors of radicality of PE are inconsistent; therefore, the best approaches for prevention are unknown. To facilitate future research into EPS, the aim of this study is to define a measurable core outcome set, core descriptor set and written definition for EPS. Consensus on strategies to mitigate EPS was also explored. Three-stage consensus methodology was used: longlisting with systematic review, healthcare professional event, patient engagement, and Delphi-piloting; shortlisting with two rounds of modified Delphi; and a confirmatory stage using a modified nominal group technique. This included a selection of measurement instruments, and iterative generation of a written EPS definition. One hundred and three and 119 participants took part in the modified Delphi and consensus meetings, respectively. This encompassed international patient and healthcare professional representation with multidisciplinary input. Seventy statements were longlisted, seven core outcomes (bowel obstruction, enteroperineal fistula, chronic perineal sinus, infected pelvic collection, bowel obstruction, morbidity from reconstruction, re-intervention, and quality of life), and four core descriptors (magnitude of surgery, radiotherapy-induced damage, methods of reconstruction, and changes in volume of pelvic dead space) reached consensus-where applicable, measurement of these outcomes and descriptors was defined. A written definition for EPS was agreed. EPS is an area of unmet research and clinical need. This study provides an agreed definition and core data set for EPS to facilitate further research.

Predicting and understanding the ductility of BCC high entropy alloys via knowledge-integrated machine learning

Despite the fact that body-centered cubic high entropy alloys (BCC HEAs) exhibit exceptional strengthening mechanisms over a wide temperature range, their application is often hindered by poor room temperature ductility. Consequently, the accurate prediction of BCC HEAs′ ductility becomes critical in alloy design but is challenged by the intricate nature of these alloy systems. In this study, a comprehensive machine learning (ML) strategy with integrated material knowledge is proposed to address this problem. By referring to plasticity theories, thermodynamics as well as atomic properties, we develop a set of cost-effective material descriptors with various mathematical forms such as averaging, mismatching, maximum-minimum ranging and averaging as binary pairs for HEAs. These descriptors are proved to effectively incorporate material knowledge into ML modelling. They also offer potential alternatives to those obtained from costly first principles calculations. Classification and regression tasks are conducted to predict the ductility of BCC HEAs, and Gradient boosting algorithm exhibiting the best performance for both tasks is eventually selected. The established optimized models achieve high predictive accuracies exceeding 0.85. Through feature engineering, five key features related to valence electron concentration (VEC) and atomic size are screened out. These features lay the foundation for explicit formulae developed via Symbolic Classification, enabling the straightforward numerical prediction of BCC HEAs′ ductility. These key features are further analyzed by an interpretable ML method and compared against experimental and computational plasticity factors, elucidating their influences on various plasticity mechanisms. An in-depth exploration of why the proposed ML models outperform conventional plasticity criteria for HEAs is also presented. This study highlights an effective knowledge-integrated ML approach in predicting material properties resulting from complex mechanisms, with a particular focus on HEAs that often exhibit sparse data.

“DompeKeys”: a set of novel substructure-based descriptors for efficient chemical space mapping, development and structural interpretation of machine learning models, and indexing of large databases

The conversion of chemical structures into computer-readable descriptors, able to capture key structural aspects, is of pivotal importance in the field of cheminformatics and computer-aided drug design. Molecular fingerprints represent a widely employed class of descriptors; however, their generation process is time-consuming for large databases and is susceptible to bias. Therefore, descriptors able to accurately detect predefined structural fragments and devoid of lengthy generation procedures would be highly desirable. To meet additional needs, such descriptors should also be interpretable by medicinal chemists, and suitable for indexing databases with trillions of compounds. To this end, we developed—as integral part of EXSCALATE, Dompé’s end-to-end drug discovery platform—the DompeKeys (DK), a new substructure-based descriptor set, which encodes the chemical features that characterize compounds of pharmaceutical interest. DK represent an exhaustive collection of curated SMARTS strings, defining chemical features at different levels of complexity, from specific functional groups and structural patterns to simpler pharmacophoric points, corresponding to a network of hierarchically interconnected substructures. Because of their extended and hierarchical structure, DK can be used, with good performance, in different kinds of applications. In particular, we demonstrate how they are very well suited for effective mapping of chemical space, as well as substructure search and virtual screening. Notably, the incorporation of DK yields highly performing machine learning models for the prediction of both compounds’ activity and metabolic reaction occurrence. The protocol to generate the DK is freely available at https://dompekeys.exscalate.eu and is fully integrated with the Molecular Anatomy protocol for the generation and analysis of hierarchically interconnected molecular scaffolds and frameworks, thus providing a comprehensive and flexible tool for drug design applications.

Headspace – Solid phase microextraction vs liquid injection GC-MS analysis of essential oils: Prediction of linear retention indices by multiple linear regression

AbstractDue to the relative independence from the operational parameters, the linear retention indices (LRIs) are useful tool in gas chromatography-mass spectrometry (GC-MS) qualitative analysis. The aim of the current study was to develop a multiple linear regression (MLR) model for the prediction of LRIs as a function of selected molecular descriptors. Liquid injection GC-MS was used for the analysis of Essential oils (Rose, Lavender and Peppermint) separating the ingredients by a semi-standard non-polar stationary phase. As a result, a sum of 103 compounds were identified and their experimental LRIs were derived relying on reference measurements of a standard mixture of n-alkanes (from C8 to C20). As a next step, a set of molecular descriptors was generated for the distinguished chemical structures. Further, a stepwise MLR was applied for the selection of the significant descriptors (variables) which can be used to predict the LRIs. From an exploit set of over 2000 molecular descriptors, it was found that only 16 can be regarded as significant and independent variables. At this point split validation was applied: the identified compounds were randomly divided into training (85%) and validation (15%) sets. The training set (87 compounds) was used to derive two MLR models by applying i) the ‘enter’ algorithm (R2 = 0.9960, RMSЕ = 17) and ii) the ‘stepwise’ one (R2 = 0.9958, RMSЕ = 17). The predictive power was assessed by the validation set (16 compounds) as follows i) q2F1 = 0.9896, RMSE = 25 and ii) q2F1 = 0.9886, RMSE = 26, respectively. The adequateness of both regression approaches was further evaluated. Newly developed headspace-solid phase microextraction (HS-SPME) procedures in combination with GC-MS were used for an alternative analysis of the studied Essential oils. Twelve additional compounds, not previously detected by the liquid sample introduction mode of analysis, were identified for which the values of the significant descriptors were within the working range of the developed MLRs. For the last compounds, the LRIs were calculated and the experimental data was used as an external set for assessment of the regression models. The predictive power for both regression approaches was assessed as follows: Enter RMSE = 41, q2F2 = 0.9503 and Stepwise RMSE = 40, q2F2 = 0.9521.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework

ABSTRACT: Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew’s correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins. Communicated by Ramaswamy H. Sarma

Toward the Rational Design of Organic Catalysts for Organocatalysed Atom Transfer Radical Polymerisation.

Thanks to their diversity, organic photocatalysts (PCs) have been widely used in manufacturing polymeric products with well-defined molecular weights, block sequences, and architectures. Still, however, more universal property-performance relationships are needed to enable the rational design of such PCs. That is, a set of unique descriptors ought to be identified to represent key properties of the PCs relevant for polymerisation. Previously, the redox potentials of excited PCs (PC*) were used as a good descriptor for characterising very structurally similar PCs. However, it fails to elucidate PCs with diverse chromophore cores and ligands, among which those used for polymerisation are a good representative. As showcased by model systems of organocatalysed atom transfer radical polymerisation (O-ATRP), new universal descriptors accounting for additional factors, such as the binding and density overlap between the PC* and initiator, are proposed and proved to be successful in elucidating the experimental performances of PCs in polymerisation. While O-ATRP is exemplified here, the approach adopted is general for studying other photocatalytic systems.

High-Throughput Predictions of Accurate Enthalpies of Formation for Larger Molecules Utilizing the Bond Difference Correction Method.

The prediction of standard enthalpies of formation (EOFs) for larger molecules involves a trade-off between accuracy and cost, often resulting in non-negligible errors. The connectivity-based hierarchy (CBH) and simple bond additivity correction (BAC) are two promising means for evaluating EOFs, although they cannot achieve strict chemical accuracy. Calculated errors in the CBH are confirmed from accumulated systematic errors associated with bond differences in chemical environments. On the basis of a new set of bond descriptors, our developed bond difference correction (BDC) method effectively solves incremental errors with molecular size and inability applications for aromatic molecules. To balance the accuracy between non-aromatic and aromatic molecules, a more accurate BAC-based method with unpaired electrons and p hybrid orbitals (BAC-EP) is developed. With the incorporation of the two methods above, strict chemical accuracy by the largest deviation is achieved at low costs. These universal, ultrafast, and high-throughput methods greatly contribute to self-consistent thermodynamic parameters in combustion mechanisms.

A novel methodology for data analysis of dynamic angle of repose tests and powder flow classification

We present a new post-processing methodology to analyse powder flow image data gathered via dynamic angle of repose tests. We aim to expand the flow descriptors, allowing a more detailed and nuanced measurement of flow rheology. This makes the data extraction reliable even if the free surface profile is not clearly identifiable. After defining 30 flow descriptors to be measured from powder flow snapshots, we use Principal Component Analysis to understand their relations with the physics of the system. The set of descriptors is optimised, and the most significant ones are identified, allowing the physics to be captured with fewer essential parameters. We demonstrate that a comprehensive picture of powder flow is achievable simply with the centre of mass and the flow merging point. Our research demonstrates that traditional data extraction methodologies are insufficient to fully describe the flow, making our framework ideal for enhancing the understanding of flow properties.

Core Descriptor Sets for Rectal Prolapse Outcomes Research Using a Modified Delphi Consensus.

There is wide variation in prolapse care. To determine core descriptor sets for rectal prolapse to enhance outcomes research. Descriptors for patients undergoing rectal prolapse surgery were generated through a systematic review and expert opinion. Stakeholders were recruited internationally via listserv and social media. Experts were encouraged to consider the minimum descriptors that could be considered during clinical care, and descriptors were grouped into core descriptor sets. Consensus was defined as greater than 70% agreement. A 3-round Delphi process using a 9-point Likert scale based on expert results was distributed via survey. The final interactive meeting used a polling platform. The Pelvic Floor Disorders Consortium interdisciplinary group convened to advance the clinical care of pelvic floor disorders. To achieve expert consensus for core descriptor sets for rectal prolapse using a modified Delphi method. A total of 206 providers participated, with survey response rates of 82% and 88%, respectively. Responders were from North America (56%), Europe (29%), and Latin America, Asia, Australia, New Zealand, and Africa (15%). Ninety-one percent of participants identified as colorectal surgeons and 80% reported >5 years of experience (35% reported >15 years). Fifty-seven attendees participated in the final meeting and voted on core descriptor sets. Ninety-three percent of participants agreed that descriptors such as age, BMI, frailty, nutrition, and the American Society of Anesthesiology score correlated to physiologic status. One hundred percent of participants agreed to include baseline bowel function. One hundred percent of participants reported willingness to complete a synoptic operative report. Follow-up intervals 1, 3, and 5 years after surgery (76%) with a collection of recurrence and functional outcomes at those time periods reached an agreement. Individual bias, self-identification of experts, and paucity of knowledge related to rectal prolapse. This represents the first steps toward international consensus to unify language and data collection processes for rectal prolapse. See Video Abstract . ANTECEDENTES:Existe una amplia variación en la atención del prolapso.OBJETIVO:Determinar conjuntos de descriptores básicos para el prolapso rectal para mejorar los resultados de la investigación.DISEÑO:Los descriptores para pacientes sometidos a cirugía de prolapso rectal se generaron a través de una revisión sistemática y la opinión de expertos. Las partes interesadas fueron reclutadas internacionalmente a través de listas de servicio y redes sociales. Se animó a los expertos a considerar los descriptores mínimos que podrían considerarse durante la atención clínica, y los descriptores se agruparon en conjuntos de descriptores básicos. El consenso se definió como > 70% de acuerdo.AJUSTE:Se distribuyó mediante encuesta un proceso Delphi de tres rondas que utiliza una escala Likert de 9 puntos basada en resultados de expertos. La reunión interactiva final utilizó una plataforma de votación.PARTICIPANTES:El grupo interdisciplinario del Consorcio de Trastornos del Suelo Pélvico se reunió para avanzar en la atención clínica de los trastornos del suelo pélvico.MEDIDAS PRINCIPALES DE RESULTADOS:Lograr el consenso de expertos para los conjuntos de descriptores básicos para el prolapso rectal utilizando un método Delphi modificado.RESULTADOS:Participaron 206 proveedores con tasas de respuesta a la encuesta del 82% y 88% respectivamente. Los encuestados procedían de América del Norte (56%), Europa (29%) y América Latina, Asia, Australia, Nueva Zelanda y África (15%). El noventa y uno por ciento se identificó como cirujanos colorrectales y el 80% reportó más de 5 años de experiencia (35% > 15 años). Cincuenta y siete asistentes participaron en la reunión final y votaron sobre conjuntos de descriptores básicos. El noventa y tres por ciento estuvo de acuerdo en que descriptores como edad, índice de masa corporal, fragilidad, nutrición y puntuación de la Sociedad Estadounidense de Anestesiología se correlacionaban con el estado fisiológico. El cien por ciento estuvo de acuerdo en incluir la función intestinal inicial. El 100% refirió disposición para realizar un informe operativo sinóptico. Los intervalos de seguimiento 1,3,5 años después de la cirugía (76%) con un conjunto de recurrencias y los resultados funcionales en esos períodos de tiempo coincidieron.LIMITACIONES:Sesgo individual, autoidentificación de los expertos y escasez de conocimientos relacionados con el prolapso rectal.CONCLUSIONES:Esto representa los primeros pasos hacia un consenso internacional para unificar el lenguaje y los procesos de recolección de datos para el prolapso rectal. (Traducción-Yesenia Rojas-Khalil ).

Multistatic radar distribution geometry effects on parameter estimation accuracy

AbstractAn analysis of the parameter estimation uncertainty for the target location and velocity achievable using a single‐transmitter‐multiple‐receiver multistatic radar system is presented. A framework for establishing measures of multistatic radar parameter uncertainties by expansion of the bistatic radar parameter uncertainty measures is presented for systems containing omnidirectionally radiating nodes. The methodology uses analytical methods based on the Cramér–Rao Lower Bounds applied to scenarios in a two‐dimensional physical space with a single target exhibiting Doppler characteristics and a bistatic angle dependent radar cross‐section. A set of geometric descriptors is proposed to characterise the system, and parameter uncertainty results are reported as a function of these descriptors. The results indicate that angular separation between the transmitter and the centre of the receiver distribution is of greater importance than the quantity of receivers within the system when low uncertainty estimation capabilities are desired, though a minimum of two receivers must be available. The proportion of receivers within the system which contributed information crucial to obtaining the minimum estimation uncertainty is reported for systems containing different quantities of receivers. It was observed that, as the total number of receivers available increased, the proportion of receivers required to achieve the minimal uncertainty level reduces significantly.

Characterization of Kinetics-Controlled Morphologies in the Growth of Silver Crystals from a Primary Lead Melt

Silver, a precious metal, can be recovered as a by-product of the processing of non-ferrous metals such as lead. In this work, silver crystals grown from the controlled cooling of a 10% silver–90% lead melt have been examined to quantify crystal morphologies developed under industrial conditions. X-ray tomography (XCT) is adapted to quantify the size and morphology of silver crystal structures grown from the Ag-Pb melt. The examination utilized high X-ray energies and small sample sizes to mitigate attenuation and enhance image quality. Examination of single crystal dendrites under high magnification demonstrates that silver crystals, even those grown under commercial conditions, yield a Face-Centered Cubic (FCC) crystalline lattice, which could be important for the practical extension of this work to the commercial production of Ag nano-crystals and crystalline supra-molecular structures. The crystals observed are composed of multiple twinned euhedral grains in a variety of dendritic to acicular arrangements, yielding a substantial heterogeneity of crystalline forms. XCT data were used to generate size and shape descriptors for the individual crystals. The results were compared to an equivalent set of descriptors generated from laser sizing examination of a sample of unconsolidated crystals from the same experimental run. The correspondence to within 9% of the crystal equivalent diameters determined independently by the XCT and laser sizing demonstrates a favorable outcome in particle sizing as achieved by visual inspection of XCT results. XCT examination of crystal assemblages identifies small octahedral crystals and larger triangular platelets. The structures expected for FCC crystals grown at thermodynamically controlled conditions are not observed in our systems, suggesting the possibility of the first crystal nuclei form at such conditions, but their growth transition to kinetically controlled mechanisms occurs as their size increases above a threshold cutoff. Based on literature observations, this size threshold is much smaller than the resolution of the XCT instrumentation employed herein. Our characterization data are in fact consistent with thermodynamics/kinetics—and then kinetics-controlled mechanisms—as the crystal size increases. This observation is important because the systems considered here are representative of commercial processes. As such, this work extends prior crystal growth concepts, which were explored in aqueous systems often probed by electrodeposition.

Transforming image descriptions as a set of descriptors to construct classification features

<span>The article develops methods to solve a fundamental problem in computer vision: image recognition of visual objects. The results of the research on the construction of modifications for the space of classification features based on the application of the transformation of the structural description through the decomposition in the orthogonal basis and the implementation of the distance matrix model between the components of the description are presented. The application of the system of orthogonal functions as an apparatus for the transformation of the description showed the possibility of a significant gain in the speed of processing while maintaining high indicators of classification accuracy and interference resistance. The synthesized feature systems’ effectiveness has been confirmed in terms of a significant increase in the rate of codes and a sufficient level of efficiency. An experimental example showed that the time spent calculating the relevance of descriptions according to their modified presentation is more than ten times shorter than for traditional metric approaches. The developed classification features can be used in applied tasks where the time of visual objects’ identification is critical.</span>

Ranking and measuring the dynamics in the reasons-for-buying selected produce

Aim of study: Individual purchasing behaviour depends on economics, psychology, marketing and sensory science. Given that the list of reasons-for-buying is almost unlimited, we have defined 14 pre-set descriptors thought to entail the more important attributes when make buying decisions within a food group of selected fruits and vegetables. Area of study: We have used a United States buyer data base of over 175,000 observations. Material and methods: Each household was asked to rank their first, second, and third most important reasons for buying, within the set of descriptors. The overriding goal was to gain insight into the attributes and change over time. Using empirical models, the relative importance of the attributes is shown and forecasted for a decade beyond 2021. Main results: Price and quality were expected to be the main drivers; however, the organic attribute is one requiring significant changes in the production, inspection, distribution and marketing policies, hence considering future expectations for organics is particularly important. Research highlights: Preferences for organics have grown, but what are the expectations a decade from now? Will that interest remain so for many years to come?

LMFD: lightweight multi-feature descriptors for image stitching

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains.

Determining the orientation of circular areas in video images

One of the ways to describe objects in images is to identify some of their characteristic points, or points of attention. The neighborhoods of attention points are described by descriptors (sets of features) in such way that they can be identified and compared. Based on these features, the search for identical points on other images is carried out. The feature selection process should take into account not only the position of the focus point, but also the size and possible shape of the local neighborhood. Different methods of finding identical characteristic points are known, which use rectangular or circular neighborhood to describe them. The rectangular windows used for neighborhood analysis do not allow selecting the same areas on the reference image and image which is rotated and analyzed. Round windows allow this. The article proposes and studies algorithms for determining the orientation of circular areas in video images, which makes it possible to calculate, in addition to invariant characteristics, characteristics that take into account the location of individual sections of such areas. These are the algorithms: • determination of the orientation of the circular area by moments of the first and second order; • calculation of the mutual orientation of the investigated circular area and the standard according to the moments of the first and second order. As a result of research and experiments, it was established that depending on the algorithm and the content of the circular areas, there are possible deviations in determining their orientation directions. Factors affecting this: accuracy of finding characteristic points; application of certain orientation determination algorithms; differences in the angle of video recording of the reference image and the image under investigation; the location of characteristic points, inside the object or on its border, especially when the background changes; size of neighborhood (analysis window) of characteristic points. It seems appropriate to continue the research orientation determination accuracy of circular areas by various algorithms. To reduce the influence of various side factors, artificial rather than real images should be studied

Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems

Mutagenicity is considered an important endpoint from the regulatory, environmental and medical points of view. Due to the wide number of compounds that may be of concern and the enormous expenses (in terms of time, money, and animals) associated with rodent mutagenicity bioassays, this endpoint is a major target for the development of alternative approaches for screening and prediction. The majority of old-aged expert systems and quantitative structure-activity relationship (QSAR) models may show reduced performance over time for their application on newer chemical candidates; thus, researchers constantly try to improve the modeling strategies. In our report, we initially performed traditional classification-based linear discriminant analysis (LDA) QSAR modeling using the benchmark Ames dataset of diverse chemicals (6512 compounds) to recognize the relationship between the molecules and their potential mutagenic behavior. The classical LDA QSAR model is developed from a selected set of 2D descriptors. The LDA QSAR model was developed by using a total of 31 descriptors identified from the analysis of the most discriminating features. Additionally, we have used similarity-derived features obtained from the read-across (RA) to develop an RA-based QSAR model. The developed RA-based LDA QSAR model has better predictivity, transferability, and interpretability compared to the LDA QSAR model, and it uses a very small number of descriptors compared to the classical QSAR model. Different machine learning (ML) models were also developed using the descriptors appearing in the read-across-based LDA QSAR model for comparative studies. We have checked the prediction quality of 216 true external set compounds using the novel similarity-derived RA model. The performance of the OECD toolbox is also compared with the RA-derived LDA QSAR model for a true external set. The current study aimed to explore the significance of the read-across-based algorithm and its application to the most current experimental mutagenicity data to complement already available expert systems.

Understanding the contagiousness of Covid-19 strains: A geometric approach

Protein–protein interaction occurs on surface patches with some degree of complementary geometric and chemical features. Building on this understanding, this study endeavors to characterize the spike protein of the SARS-CoV-2 virus at the morphological and geometrical levels in its Alpha, Delta, and Omicron variants. In particular, the affinity between different SARS-CoV-2 spike proteins and the ACE2 receptor present on the membrane of the human respiratory system cells is investigated. To achieve an adequate degree of geometrical accuracy, the 3D depth maps of the proteins in exam are filtered by developing an ad-hoc convolutional filter with a kernel implemented as a sphere of varying radius, simulating a ball rolling on the surface (similar to the 'rolling ball' filter). This ball ideally models a hypothetical molecule that could interface with the protein and is inspired by the geometric approach to macromolecule–ligand interactions proposed by Kuntz et al. in 1982. The aim is to mitigate the imperfections and to obtain a smoother surface that could be studied from a geometrical perspective for binding purposes. A set of geometric descriptors, borrowed from the 3D face analysis context is then mapped point-by-point onto protein depth maps. Following a feature extraction phase inspired by Histogram of Oriented Gradients and Local Binary Patterns, the final histogram features are used as input for a Support Vector Machine classifier to automatically classify the proteins according to their surface affinity, where a similarity in shape is observed between ACE2 and the spike protein of the SARS-CoV-2 Omicron variant. Finally, Root Mean Square Error analysis is used to quantify the geometrical affinity between the ACE2 receptor and the respective Receptor Binding Domains of the three SARS-CoV-2 variants, culminating in a geometrical explanation for the higher contagiousness of Omicron relative to the other variants under study.

Quantifying and comparing the effects of key chemical descriptors on metal–organic frameworks water stability with CatBoost and SHAP

Metal-organic frameworks (MOFs) were considered suitable candidates for a range of industrial applications, including adsorption, separation, sensing and catalysis, due to their advantages of diverse structures and adjustable functions. One of the criteria for determining the commercial viability of MOFs is their stability in water vapor. Here, we established a novel Categorical Boosting (CatBoost) machine learning approach to model more than 200 datasets of empirical measurements of MOF water stability, and used a comprehensive set of chemical descriptors to represent MOF composition including metal ions, organic ligands, and metal–ligand molar ratios. CatBoost algorithm was significantly superior to other gradient algorithms in accuracy, precision and F1-Score. Also, the CatBoost output was interpreted using the Shapley additive interpretation (SHAP) method. Besides providing guidelines for future experimental screening of stable candidates for MOFs, the interpretable Catboost model can also be used for MOFs screening of other design criteria.

Continuous symmetry and chirality measures: approximate algorithms for large molecular structures

Quantifying imperfect symmetry of molecules can help explore the sources, roles and extent of structural distortion. Based on the established methodology of continuous symmetry and chirality measures, we develop a set of three-dimensional molecular descriptors to estimate distortion of large structures. These three-dimensional geometrical descriptors quantify the gap between the desirable symmetry (or chirality) and the actual one. They are global parameters of the molecular geometry, intuitively defined, and have the ability to detect even minute structural changes of a given molecule across chemistry, including organic, inorganic, and biochemical systems. Application of these methods to large structures is challenging due to countless permutations that are involved in the symmetry operations and have to be accounted for. Our approach focuses on iteratively finding the approximate direction of the symmetry element in the three-dimensional space, and the relevant permutation. Major algorithmic improvements over previous versions are described, showing increased accuracy, reliability and structure preservation. The new algorithms are tested for three sets of molecular structures including pillar[5]arene complexes with Li+, C100 fullerenes, and large unit cells of metal organic frameworks. These developments complement our recent algorithms for calculating continuous symmetry and chirality measures for small molecules as well as protein homomers, and simplify the usage of the full set of measures for various research goals, in molecular modeling, QSAR and cheminformatics.