Types Of Descriptors Research Articles

DFT Studies of Substituted Phenols Cytotoxicity I. Para‐substituted Phenols

AbstractNew type of cytotoxicity descriptors based on Cu(II) coordination ability of p‐substituted phenols and subsequent O→Cu electron density transfer is introduced. Geometries of phenols and of their Cu(II) complexes are optimized at the DFT level of theory. The amount of electron density transfer is evaluated using the copper charge or the electron density Laplacian at the Cu−O bond critical point. The Cu‐phenol interaction energy is a measure of the phenol reactivity. Statistical parameters for the linear dependence of the published cytotoxicity data on the above predictors are much better than the classical ones.

Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors.

Hydrogen evolution by alternating conjugated copolymers has attracted much attention in recent years. To study alternating copolymers with data-driven strategies, two types of multidimension fragmentation descriptors (MDFD), structure-based MDFD (SMDFD), and electronic property-based MDFD (EPMDFD), have been developed with machine learning (ML) algorithms for the first time. The superiority of SMDFD-based models has been demonstrated by the highly accurate and universal predictions of electronic properties. Moreover, EPMDFD-based, experimental-parameter-free ML models were developed for the prediction of the hydrogen evolution reaction, displaying excellent accuracy (real-test accuracy = 0.91). The combination of explainable ML approaches and first-principles calculations was employed to explore photocatalytic dynamics, revealing the importance of electron delocalization in the excited state. Virtual designing of high-performance candidates can also be achieved. Our work illustrates the huge potential of ML-based material design in the field of polymeric photocatalysts toward high-performance photocatalysis.

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity at cholinergic synapses in various regions of the nervous system. The inhibition of acetylcholinesterase is frequently used to treat Alzheimer's disease. In this study, a merged BindingDB and ChEMBL dataset containing molecules with reported half-maximal inhibitory concentration (IC50) values for AChE (7032 molecules) was used to build machine learning classification models for selecting potential AChE inhibitors from the SistematX dataset (8593 secondary metabolites). A total of seven fivefold models with accuracy above 80% after cross-validation were obtained using three types of molecular descriptors (VolSurf, DRAGON 5.0, and bit-based fingerprints). A total of 521 secondary metabolites (6.1%) were classified as active in this stage. Subsequently, virtual screening was performed, and 25 secondary metabolites were identified as potential inhibitors of AChE. Separately, the crystal structure of AChE in complex with (-)-galantamine was used to perform molecular docking calculations with the entire SistematX dataset. Consensus analysis of both methodologies was performed. Only eight structures achieved combined probability values above 0.5. Finally, two sesquiterpene lactones, structures 15 and 24, were predicted to be able to cross the blood-brain barrier, which was confirmed in the VolSurf+ quantitative model, revealing these two structures as the most promising secondary metabolites for AChE inhibition among the 8593 molecules tested. A consensus analysis of classification models and molecular docking calculations identified four potential inhibitors of acetylcholinesterase from the SistematX dataset (8593 structures).

3D Interaction Homology: Hydropathic Analyses of the "π-Cation" and "π-π" Interaction Motifs in Phenylalanine, Tyrosine, and Tryptophan Residues.

Three-dimensional (3D) maps of the hydropathic environments of protein amino acid residues are information-rich descriptors of preferred conformations, interaction types and energetics, and solvent accessibility. The interactions made by each residue are the primary factor for rotamer selection and the secondary, tertiary, and even quaternary protein structure. Our evolving basis set of environmental data for each residue type can be used to understand the protein structure. This work focuses on the aromatic residues phenylalanine, tyrosine, and tryptophan and their structural roles. We calculated and analyzed side chain-to-environment 3D maps for over 70,000 residues of these three types that reveal, with respect to hydrophobic and polar interactions, the environment around each. After binning with backbone ϕ/ψ and side chain χ1, we clustered each bin by 3D similarities between map-map pairs. For each of the three residue types, four bins were examined in detail: one in the β-pleat, two in the right-hand α-helix, and one in the left-hand α-helix regions of the Ramachandran plot. For high degrees of side chain burial, encapsulation of the side chain by hydrophobic interactions is ubiquitous. The more solvent-exposed side chains are more likely to be involved in polar interactions with their environments. Evidence for π-π interactions was observed in about half of the residues surveyed [phenylalanine (PHE): 53.3%, tyrosine (TYR): 34.1%, and tryptophan (TRP): 55.7%], but on an energy basis, this contributed to only ∼4% of the total. Evidence for π-cation interactions was observed in 14.1% of PHE, 8.3% of TYR, and 26.8% of TRP residues, but on an energy basis, this contributed to only ∼1%. This recognition of even these subtle interactions in the 3D hydropathic environment maps is key support for our interaction homology paradigm of protein structure elucidation and possibly prediction.

Computer-Aided Design of New Physical Solvents for Hydrogen Sulfide Absorption

Treatment of hydrogen sulfide (H2S) is important in many industrial processes including oil refineries, natural and biogas processing, and coal gasification. The most mature technology for the selective capture of H2S is based on its absorption by chemical or physical solvents. However, only several compounds are currently used as physical (co)solvents in industry, and the search for new ones is an important task. The experimental screening of physical (co)solvents requires much time and many resources, while solubility modeling might enable one to reduce the number of solvents for the experimental evaluation. In this study, a workflow for the in silico discovery of new physical solvents for H2S absorption was suggested and experimentally validated. A data set composed of 99 H2S physical solvents was collected and predictive quantitative structure–property relationships for H2S solubility were built using a random forest algorithm and two types of molecular descriptors: ISIDA fragments and quantum-chemical descriptors. Virtual screening of industrially produced chemicals and their structural analogues enabled identification of the ones with predicted high solubility values. They can be suggested as starting points for further exploration of the H2S physical solvents chemical space. The predicted solubility value for one of the compounds found in virtual screening, 1,3-dimethyl-2-imidazolidinone, was confirmed experimentally.

A machine learning platform to estimate anti-SARS-CoV-2 activities

Strategies for drug discovery and repositioning are urgently need with respect to COVID-19. Here we present REDIAL-2020, a suite of computational models for estimating small molecule activities in a range of SARS-CoV-2-related assays. Models were trained using publicly available, high-throughput screening data and by employing different descriptor types and various machine learning strategies. Here we describe the development and use of eleven models that span across the areas of viral entry, viral replication, live virus infectivity, in vitro infectivity and human cell toxicity. REDIAL-2020 is available as a web application through the DrugCentral web portal ( http://drugcentral.org/Redial ). The web application also provides similarity search results that display the most similar molecules to the query, as well as associated experimental data. REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. There is an urgent need to identify drugs that may be effective against SARS-CoV-2. A platform with a range of machine learning models is made available to predict anti-COVID-19 activity in candidate drugs and to help prioritize compounds for virtual screening.

Pain Quality Among Hospitalized Postcraniotomy Brain Tumor Patients

Purpose/Aims The aim of this study was to describe how persons given a diagnosis of a brain tumor who have had a craniotomy describe the quality of their pain after surgery. Design A qualitative descriptive design was used. Methods Qualitative descriptive methods as described by Sandelowski guided this study. Semistructured interviews were conducted with patients hospitalized on a neurological step-down unit in an urban teaching hospital in the Midwestern United States. Interviews focused on the quality of participants' pain after surgery. Narratives were analyzed using standard content analysis. Results Twenty-seven participants were interviewed. Most were White and female. Most underwent a craniotomy using an anterior approach with sedation. Participants described the quality of their pain with 6 different types of descriptors: pain as pressure, pain as tender or sore, pain as stabbing, pain as throbbing, pain as jarring, and pain as itching. Conclusions Participants' descriptions of their pain quality after surgery provide a different understanding than do numerical pain ratings. Clinicians should use questions to explore patients' individual pain experiences, seeking to understand the quality of patients' pain and their perceptions.

Agro-morphological characterization of yellow passion fruit (Passiflora edulis f. flavicarpa Degener) reveals elite genotypes for a breeding program in Colombia

Yellow passion fruit cultivation in Colombia lacks detailed genetic studies that allow the establishment of intraspecific variability as a basis for a breeding program. The aim of this research was to establish the genetic relationships among accessions of different geographic origins through an agro-morphological characterization. This research was carried out in the municipality of Palestina (Caldas) at the Luker farm located at 1050 m a.s.l. Fifty-two accessions from Colombia (47), Ecuador (3), Brazil (1), and Costa Rica (1) were characterized with 45 agro-morphological descriptors complemented with phytosanitary evaluations. The quantitative descriptors (25) were analyzed by variance decomposition and principal components (PCA), and the qualitative descriptors (18) were analyzed with the multiple correspondence factor analysis (MCFA). The classification analysis by the neighbor-joining (NJ) method was used for both types of descriptors. PCA results showed six components explaining 71.6% of the total variance that are mainly associated with descriptors related to the size of the flower (tube, nectar camera, and operculum) and weight of the fruit (pulp, shell, and juice content). Thirteen qualitative descriptors were selected, and four factors (44.38% inertia) were identified, mainly associated with pubescence and anthocyanins in the bract, petiole, leaf, and ripe fruit color. The NJ classification analysis showed no relationship between accessions by geographic origin, and the distance between individuals of the same accession was higher than among accessions for both types of descriptors. Thrips (Neohydatothrips signifier) and scab (Cladosporium cladosporioides) were the pests with the highest incidence in the accessions. Six elite accessions were identified according to the selection index with outstanding fruit quality characteristics, yield, and tolerance to phytosanitary problems. The high intra-accession variability and traits of certain accessions are the basis for future breeding programs to obtain more productive and tolerant cultivars.

In silico and in vitro Studies of Imidazolium Ionic Liquids as Effective Antibacterial Agents against Multidrug Resistant Escherichia coli Strains

Background: Escherichia coli especially its multiresistant strains as the common foodborne pathogens cause bloodstream infections, nosocomial pneumonia, infections of the skin and soft tissues. Therefore, the search for new effective biologically active compounds has been rapidly increasing in recent few decades. In this paper, we describe Quantitative Structure-Activity Relationships (QSAR) studies, molecular docking and in vitro antibacterial activity evaluation of a series of imidazolium-based Ionic Liquids (ILs) against E. coli spp. Methods: M2D fragment-based, classification and regression QSAR models were created using machine learning methods and types of descriptors via the OCHEM server. Biological testing of a series of synthesized imidazolium ILs with predicted activity was performed by the disc diffusion method. The most typical structures of symmetric and asymmetric ILs with high anti-E. coli activity (1e, 1h) were docked into the active site of Enoyl-Acyl Carrier Protein Reductase (ENR) in E. coli. Results: Symmetric imidazolium ILs with C8 alkyl chain length demonstrated the highest antibacterial activity in comparison to the high antibacterial potential of asymmetric ILs with C12 alkyl chain length against drug-sensitive and drug-resistant E. coli strains including hemolytic E. coli. It should be noted that symmetric ILs with C6 or C9 alkyl chain length have a slightly lower activity against certain E. coli strains. The key role in the binding of compounds (1e, 1h) in the E. coli ENR active site is associated with the NAD molecule and the amino acid residue Tyr146. Conclusion: The highly active symmetric and asymmetric imidazolium ILs can be considered as promising drug-candidates effective against E. coli spp. pathogens including multidrug-resistant strains.

On Computations of Topological Descriptors of Kagome Lattice

The analysis of graphs and networks is a multidisciplinary research field from its origin. As such, it has been extensively used also in biomedicine, cheminformatics, and in bioinformatics, where approaches based on graph descriptors have been offered for communicating with the numerous challenging tasks. In quantitative structure-activity (QSAR) and quantitative structure-property (QSPR) relationships studies, graph invariants are used to forecast the biological activities and properties of nanomaterials. In these studies, degree-based topological descriptors have founded comprehensive acceptance among the different types of descriptors, because of the ease of generation and the speed with which these calculations can be performed. In this article, we first compute the M-polynomials of 2D kagome lattice and then derive the closed form of various significant topological degree-based indices in terms of the M-polynomials and partition techniques. Furthermore, we sketch surfaces that show the dependence of some topological indices on the parameters of the structure.

Novel methods for noisy 3D point cloud based object recognition

3D point cloud based object recognition becomes increasingly important in the last few years, as the widely use of point cloud over the low-cost 3D sensors have developed rapidly. However, the obtained 3D point cloud is inevitably contaminated with noise due to physical and environmental factors, which has a negative impact on recognition task. To address this problem, a complete object recognition framework for 3D noisy point cloud is presented into which a pre-processing step of filtering is integrated for the first time. In the filtering phase, our two proposed approaches, named Guided 3D Point Cloud Filter (G3DF) and Iterative Guidance Normal Filter (IGNF), are taken into account to produce high-quality point cloud model. Then, on the basis of advantages of local-based and global-based descriptors, a new type of feature descriptor, called Local-to-Global Histogram (LGH), is proposed, which contains Local Viewpoint Feature Histogram (LVFH) and Local Ensemble of Shape Function (LESF). Experimental results show that the comprehensive classification performance yielded by using proposed filters and descriptors is competitive compared to other state-of-the-art combinations. In particularly, the composition of G3DF and LVFH is more suited for real-time applications.

Flame: an open source framework for model development, hosting, and usage in production environments

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

Impact of karst areas on runoff generation, lateral flow and interbasin groundwater flow at the storm-event timescale

Abstract. Karst development influences the hydrological response of catchments. However, such an impact is poorly documented and even less quantified, especially over short scales of space and time. The aim of this article is thus to define karst influence on the different hydrological processes driving runoff generation, including interbasin groundwater flow (IGF) for elementary catchments at the storm-event timescale. IGFs are estimated at the scale of the river reach, by comparing inlet and outlet flows as well as the effective rainfall from the topographic elementary catchment. Three types of storm-event descriptors (characterizing water balance, hydrograph shape and lateral exchanges) were calculated for the 20 most important storm events of 108 stations in three French regions (Cévennes Mountains, Jura Mountains and Normandy), representative of different karst settings. These descriptors were compared and analysed according to catchment geology (karst, non-karst or mixed) and seasonality in order to explore the specific impact of karst areas on water balance, hydrograph shape, lateral exchanges and hydrogeological basin area. A statistical approach showed that, despite the variations with study areas, karst promotes (i) higher water infiltration from rivers during storm events, (ii) increased characteristic flood times and peak-flow attenuation, and (iii) lateral outflow. These influences are interpreted as mainly due to IGF loss that can be significant at the storm-event scale, representing around 50 % of discharge and 20 % of rainfall in the intermediate catchment. The spatial variability of such effects is also linked to contrasting lithology and karst occurrence. Our work thus provides a generic framework for assessing karst impact on the hydrological response of catchments to storm events; moreover, it can analyse flood-event characteristics in various hydro-climatic settings and can help with testing the influence of other physiographic parameters on runoff generation.

Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids

Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new candidates with low cost. In this study, we developed a novel data mining and computational modeling workflow proven to be applicable by screening new analgesic opioids. To this end, a large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. There were 114 PubChem bioassays selected to build quantitative structure-activity relationship (QSAR) models based on the testing results across the probe compounds. The compounds tested in each bioassay were used to develop 12 models using the combination of three machine learning approaches and four types of chemical descriptors. The model performance was evaluated by the coefficient of determination (R 2) obtained from 5-fold cross-validation. In total, 49 models developed for 14 bioassays were selected based on the criteria and were identified to be mainly associated with binding affinities to different opioid receptors. The models for these 14 bioassays were further used to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).

Virtual Screening and Molecular Design of Potential SARS-COV-2 Inhibitors

According to recent studies, the main Mpro protease of the SARS-CoV-2 virus, which is the most important target in the development of promising drugs for the treatment of COVID-19, is evolutionarily conservative and has not undergone significant changes compared with the main Mpro protease of the SARS-CoV virus. Many researchers note the similarity between the binding sites of the main Mpro protease of SARS-CoV and SARS-CoV-2 viruses; thus, with the spreading epidemic, further studies on inhibitors of the main Mpro protease of the SARS-CoV virus to fight COVID-19 seems logical. In the course of the study, satisfactory QSAR models are built using simplex, fractal, and HYBOT descriptors; the Partial Least Squares (PLS), Random Forest (RF), Support Vectors, Gradient Boosting (GBM) methods; and the OCHEM Internet platform (https://ochem.eu), in which different types of molecular descriptors and machine learning methods are implemented. The structural interpretation, which allowed us to identify molecular fragments that increase and decrease the activity of SARS-CoV inhibitors, is performed for the obtained models. The results of the structural interpretation are used for the rational molecular design of potential SARS-CoV-2 inhibitors. The resulting QSAR models are used for the virtual screening of 2087 FDA-approved drugs.

Intelligent System for Parasitized Malaria Infection Detection Using Local Descriptors

Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based features and color based features. The extracted features are finally fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed and different combinations of feature types are attempted. The achieved results showed that when using combined vectors of local descriptors, the system gives the desired accuracy which is 100%. The achieved result demonstrates the effectiveness of using local descriptors in solving malaria infection detection problem.

Cover Image

COVER ILLUSTRATION Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with highly heterogenous progression patterns. Presented are DTI contrasts of increased radial diffusivity for patients with ALS, that demonstrated a widespread white matter pathology if compared to healthy controls (top left). In Steinbach et al. the D50 disease progression model was applied which provides mathematically defined descriptors of the different progression types. It was used to quantify measures of disease covered independent from those of disease aggressiveness. Higher aggressiveness of patients' individual ALS disease was associated with elevated radial diffusivity in association tracts (top right). A positive correlation was found with the amount of disease covered (bottom left) as well as the calculated functional state (bottom right) at the day of MRI, representing individual disease accumulation early in disease. Harmonizing disease quantification by modelling thus greatly increased the sensitivity to identify ALS related brain disease.

QSSR Modeling of Bacillus Subtilis Lipase A Peptide Collision Cross-Sections in Ion Mobility Spectrometry: Local Descriptor Versus Global Descriptor.

To investigate the structure-dependent peptide mobility behavior in ion mobility spectrometry (IMS), quantitative structure-spectrum relationship (QSSR) is systematically modeled and predicted for the collision cross section Ω values of totally 162 single-protonated tripeptide fragments extracted from the Bacillus subtilis lipase A. Two different types of structure characterization methods, namely, local and global descriptor as well as three machine learning methods, namely, partial least squares (PLS), support vector machine (SVM) and Gaussian process (GP), are employed to parameterize and correlate the structures and Ω values of these peptide samples. In this procedure, the local descriptor is derived from the principal component analysis (PCA) of 516 physicochemical properties for 20 standard amino acids, which can be used to sequentially characterize the three amino acid residues composing a tripeptide. The global descriptor is calculated using CODESSA method, which can generate > 200 statistically significant variables to characterize the whole molecular structure of a tripeptide. The obtained QSSR models are evaluated rigorously via tenfold cross-validation and Monte Carlo cross-validation (MCCV). A comprehensive comparison is performed on the resulting statistics arising from the systematic combination of different descriptor types and machine learning methods. It is revealed that the local descriptor-based QSSR models have a better fitting ability and predictive power, but worse interpretability, than those based on the global descriptor. In addition, since the QSSR modeling using local descriptor does not consider the three-dimensional conformation of tripeptide samples, the method would be largely efficient as compared to the global descriptor.

An Indexing Algorithm Based on Clustering of Minutia Cylinder Codes for Fast Latent Fingerprint Identification

Latent fingerprint identification is one of the leading forensic activities to clarify criminal acts. However, its computational cost hinders the rapid decision making in the identification of an individual when large databases are involved. To reduce the search time used to generate the fingerprint candidates' order to be compared, fingerprint indexing algorithms that reduce the search space while minimizing the increase in the error rate (compared to the identification) are developed. In the present research, we propose an algorithm for indexing latent fingerprints based on minutia cylinder codes (MCC). This type of minutiae descriptor presents a fixed structure, which brings advantages in terms of efficiency. Besides, in recent studies, this descriptor has shown an identification error rate, at the local level, lower than the other descriptors reported in the literature. Our indexing proposal requires an initial step to construct the indices, in which it uses k-means++ clustering algorithm to create groups of similar minutia cylinder codes corresponding to the impressions of a set of databases. K-means++ allows for a better outcome over other clustering algorithms because of the selection of the proper centroids. The buckets associated with each index are populated with the background databases. Then, given a latent fingerprint, the algorithm extracts the minutia cylinder codes associated with the clusters' indices with the lowest distance respect to each descriptor of this latent fingerprint. Finally, it integrates the votes represented by the fingerprints obtained to select the candidate impressions. We conduct a set of experiments in which our proposal outperforms current rival algorithms in presence of different databases and descriptors. Also, the primary experiment reduces the search space by four orders of magnitude when the background database contains more than one million impressions.

Generalization of New Degree Based Topological Indices of Silicate Network Graph

In the fields of chemical graph theory and mathematical chemistry, a topological index also known as a connectivity index is a type of a molecular descriptor that is calculated based on the molecular graph of a chemical compound. Topological indices are numerical parameters of a graph. Topological indices are used for example in the development of quantitative structure-activity relationships (QSARs) and other properties of molecules are correlated with their chemical structure, in that Silicates are the most common minerals in the Earth’s crust and mantle, it has a wide variety of physical properties which paved way to find the topological indices. In this paper gives us the generalized new form of topological indices such as Arithmetic-Geometric Index (AG Index), SK Index, SK1 Index and SK2 Index of Silicate network graphs.