Assessment of liquid-liquid partition for the assignment of descriptors for the solvation parameter model

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and McGowan's characteristic volume, V, to model transfer properties between condensed phases. The V descriptor is assigned from structure. For compounds liquid at 20 °C the E descriptor can be assigned from the characteristic volume and its refractive index. The E descriptor for compounds solid at 20 °C and the S, A, and B descriptors are experimental properties traditionally assigned from chromatographic, liquid-liquid partition, and solubility measurements. In this report liquid-liquid partition constants in totally organic and aqueous biphasic systems are evaluated as a standalone technique for descriptor assignments. Using six totally organic biphasic systems the S, A, and B descriptors were assigned with an average absolute deviation (AAD) of about 0.04, 0.03, and 0.04, respectively, compared with the best estimate of the true descriptor values for 65 compounds. The E descriptor for compounds solid at 20 °C can only be estimated with an AAD of approximately 0.1. For six aqueous biphasic systems the B descriptor is assigned with a lower AAD of 0.028 and higher AAD of 0.08 and 0.05 for the S and A descriptors, respectively, than for the totally organic biphasic systems for compounds with a reliable value for the E descriptor. The preferred system for descriptor assignments utilizes both totally organic biphasic systems (heptane-1,1,1-trifluoroethanol, isopentyl ether-propylene carbonate, isopentyl ether-ethanolamine, heptane-ethylene glycol, heptane-formamide, and 1,2-dichloroethane-ethylene glycol) and aqueous biphasic systems (octanol-water, cyclohexane-water) with the possible substitution of some systems with alternative systems of similar selectivity. For 55 varied compounds this combination of eight organic and aqueous biphasic systems resulted in an AAD of approximately 0.03, 0.02, and 0.02 for the S, A, and B descriptors compared to the best estimate of the true descriptor value. For 30 compounds solid at 20 °C the AAD for the E descriptor of 0.11 is poorly assigned. The relative average absolute deviation in percent (RAAD) corresponds to 9.7 %, 3.1 %. 4.0 % and 8.3 % for E, S, A, and B, respectively, for the eight biphasic systems. Liquid-liquid partition is compared to reversed-phase liquid and gas chromatography as a standalone technique for descriptor assignments.

Characterization of indigenous populations of cannabis in Iran: a morphological and phenological study

BACKGROUND

Cannabis is a historically, culturally, and economically significant crop in human societies, owing to its versatile applications in both industry and medicine. Over many years, native cannabis populations have acclimated to the various environments found throughout Iran, resulting in rich genetic and phenotypic diversity. Examining phenotypic diversity within and between indigenous populations is crucial for effective plant breeding programs. This study aimed to classify indigenous cannabis populations in Iran to meet the needs of breeders and breeding programs in developing new cultivars.

RESULTS

Here, we assessed phenotypic diversity in 25 indigenous populations based on 12 phenological and 14 morphological traits in male and female plants. The extent of heritability for each parameter was estimated in both genders, and relationships between quantitative and time-based traits were explored. Principal component analysis (PCA) identified traits influencing population distinctions. Overall, populations were broadly classified into early, medium, and late flowering groups. The highest extent of heritability of phenological traits was found in Start Flower Formation Time in Individuals (SFFI) for females (0.91) Flowering Time 50% in Individuals (50% of bracts formed) (FT50I) for males (0.98). Populations IR7385 and IR2845 exhibited the highest commercial index (60%). Among male plants, the highest extent of Relative Growth Rate (RGR) was observed in the IR2845 population (0.122 g.g- 1.day- 1). Finally, populations were clustered into seven groups according to the morphological traits in female and male plants.

CONCLUSIONS

Overall, significant phenotypic diversity was observed among indigenous populations, emphasizing the potential for various applications. Early-flowering populations, with their high RGR and Harvest Index (HI), were found as promising options for inclusion in breeding programs. The findings provide valuable insights into harnessing the genetic diversity of indigenous cannabis for diverse purposes.

Determination of solvation parameter model compound descriptors by gas chromatography

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and the gas-liquid partition constant at 25 °C on n-hexadecane, L, to model transfer properties in gas-condensed phase biphasic systems. The E descriptor for compounds liquid at 20 °C is available by calculation using a refractive index value while E for solid compounds at 20 °C and the S, A, B, and L descriptors are determined by experiment. As a single-technique approach, it is shown that with up to 20 retention factor measurements on four columns comprising a poly(siloxane) containing methyloctyl or dimethyldiphenylsiloxane monomers (SPB-Octyl or HP-5), a poly(siloxane) containing methyltrifluoropropylsiloxane monomers (Rtx-OPP or DB-210), a poly(siloxane) containing bis(cyanopropylsiloxane) monomers (HP-88 or SGE BPX-90), and a poly(ethylene glycol) stationary phase (DB-WAXetr or HP-INNOWAX) are suitable for assigning the S, A, and L descriptors. Using the descriptors in the updated WSU compound descriptor database as target values the average absolute error in the descriptor assignments for 52 varied compounds in the temperature range 60-140 °C was 0.072 for E, 0.016 for S, 0.008 for A, and 0.022 for L corresponding to 30 %, 3.5 %, and 0.6 % as a relative average absolute error for E, S, and L, respectively. For the higher temperature range of 160-240 °C and 34 varied compounds that are liquid at 20 °C the average absolute error for the S, A and L descriptors was 0.026, 0.020, and 0.031, respectively, with the largest relative average absolute error for S of 3.2 % (< 1 % for the L descriptor). For 35 varied compounds that are solid at 20 °C the relative absolute error for the E, S, A, and L descriptors in the higher temperature range was 0.068, 0.035, 0.020, and 0.020, respectively, with a relative average absolute error for E (6.5 %), S (3.5 %) and L (0.88 %). The S, A, and L descriptor can be accurately assigned on the four-column system over a wide temperature range. The E descriptor for solid compounds at 20 °C exhibits greater variability than desirable. The B descriptor cannot be assigned by the four-column system, which lack hydrogen-bond acid functional groups, and is only poorly assigned on the weak hydrogen-bond acid ionic liquid column SLB-IL100.

Determination of descriptors for the principal flavor compounds of the cinnamons of commerce by gas chromatography and liquid-liquid partition

Descriptors for fourteen semivolatile organic compounds associated with the authenticity, botanical origin, and flavor potential of the cinnamons of commerce were determined using the Solver method and experimental retention factors determined by gas chromatography at several temperatures on a minimum of seven selectivity-selected, open-tubular columns and liquid-liquid partition constants in up to twenty totally organic biphasic systems. The six descriptors that encode the solvation properties of the compounds were used to predict water-gas, octanol-gas, and octanol-water partition constants commonly employed to assess environmental distribution properties. For octanol-water partition constants, log KOW, the predicted partition constants exhibited an average absolute deviation of 0.12 for log KOW experimental - log KOW predicted (n = 14). Soil-water, soil-air, urban aerosol-air, skin-water permeation, and non-specific toxicity to the fathead minnow were predicted for the same compounds to assess their potential environmental impact. The product terms of the solvation parameter model provide a useful insight into the contribution of individual intermolecular interactions to the distribution properties of the cinnamon compounds and their environmental impact.

Agro-morphological and structural diversity of rice germplasm revealed by SSR markers in Benin Republic

BACKGROUND

In developing countries, rice is a staple food and cash crop for the people. In Benin Republic, paddy rice production has increased over time. Accordingly, local varieties were replaced by improved varieties, leading unfortunatley to a loss of the diversity of Beninese rice germplasm.

METHODS AND RESULTS

The investigation focused on the structure and genetic diversity of 72 rice accessions collected throughout 22 villages using 13 quantitative traits and 17 SSR markers. The descriptive analysis of the 13 quantitative parameters showed a significant difference among the accessions, with a grouping in three clusters. Group I (16.66% of samples) was composed of accessions with long, wide and thick grains alongside with four controls TOG5681, TOG5307, Azucena and Moroberekan. Group II (7% of samples) contained accessions with late vegetative cycle. Group III contained most of the accessions (76.39% of the samples), including accessions such as the CG14 and Nipponbare control lines, and almost all the improved varieties. The molecular analysis revealed a significant diversity (mean number of alleles: 4.47 with polymorphism information content of 0.633). Population structure based on molecular markers showed three primary populations with a mixture of phenotypic groupings at ΔK, K = 3.

CONCLUSION

This study showed that Beninese rice germplasm was divided into two structures: phenotypically similar cultivars but genotypically distinct (homonyms), and phenotypically different cultivars but genotypically similar (synonyms). Some local cultivars such Bagou19, Bagou20 and Koud44 can be used for large scale production due to their agronomics and molecular traits. The molecular structure obtained in this investigation might be used for future conservation and breeding programs.

Revised descriptors for polycyclic aromatic and related hydrocarbons for the prediction of environmental properties using the solvation parameter model

Revised descriptors for twenty-five polycyclic aromatic and related hydrocarbons (PAHs) forming a component of the Wayne State University (WSU) descriptor database are provided for use with the solvation parameter model. The descriptors are determined by the Solver method using experimental data for calibrated gas-liquid and reversed-phase liquid chromatographic retention factors and liquid-liquid partition constants in totally organic biphasic systems. The characteristic solvation properties of the PAHs are accounted for mainly by the additional dispersion interactions (E descriptor) and dipole-type interactions (S descriptor) resulting from the availability of easily polarizable electrons that complement typical dispersion interactions for saturated hydrocarbons. The descriptors afford acceptable prediction of the water-air partition constant (average absolute deviation AAD = 0.17, n = 22), octanol-air partition constant (AAD = 0.12, n = 20), and water-octanol partition constant (AAD = 0.10, n = 23). A two-parameter model containing only the V and B descriptors provides an unbiased prediction of aqueous solubility for the PAHs with an AAD = 0.26 (n = 22). The descriptors estimated by convenient chromatographic and partition constant measurements are demonstrated to be a viable alternative to the experimental determination of environmental properties otherwise only available by tedious, expensive, and low data throughput experimental techniques.

Multivariate analysis for yield and yield-related traits of amaranth genotypes from Ethiopia

The genus Amaranthus is one of the few dicotyledonous, non-grass mesophytes that use specialized C4 annuals or short-lived perennials to produce significant amounts of edible small-seeded pseudo cereals. In this study, we characterized the genetic diversity of 120 genotypes of amaranths collected from diverse amaranth-growing regions of Ethiopia using multivariate analysis of yield and yield-related traits. The experiments were carried out at Hawassa University, in the years 2020 and 2021. The experimental design was set up using an alpha lattice design and replicated two times. The collected data were examined for 24 descriptors. Principal component analysis showed that the first six principal components with eigenvalues greater than one contributed 80.41% of the variability. However, the first two principal components explained 52.42% of the total variation. The highest contributing traits in the first component were days to flowering, basal stem diameter, plant height at flowering, plant height at maturity, auxiliary inflorescence length, number of branches, terminal inflorescence lateral length, days to maturity, terminal inflorescence stalk length, leaf number, leaf length, top lateral branch length. The traits with the greatest weight on the second component were leaf area, basal lateral branch length, leaf length, and leaf width, grain filling period, grain sinking filling rate, and grain yield. Therefore, selection based on these traits would be effective for yield improvement in amaranth genotypes. Additionally, the hierarchical clustering grouped all the genotypes into five clusters. The pairwise generalized squared distance (D²) among the five clusters based on Mahalanobis's D² statistics revealed the maximum and highly significant genetic distance was observed between II and III (277.79), while the minimum inter-cluster distance observed between clusters I and II (39.50). The findings suggest that amaranth genotypes in Ethiopia have a lot of genetic variation, which might be used for future breeding and ought to be conserved.

DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes

B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including designing antibodies, therapeutics, and vaccines. Lab-based experimental identification of these proteins is time-consuming and challenging. Computational techniques have been proposed to discover BCEs, but most lack of significant accomplishments. This work uses classical and deep learning models (DLMs) with sequence-based features to predict immunity stimulator BCEs from proteomics sequences. The proposed convolutional neural network-based model outperforms other models with an accuracy (ACC) of 0.878, an F-measure of 0.871, and an area under the receiver operating characteristic curve (AUC) of 0.945. The proposed strategy achieves 58.7% better results on average than other state-of-the-art approaches based on the Mathews Correlation Coefficient (MCC) results. The established model is accessible through a web application located at http://deeplbcepred.pythonanywhere.com.

Recent advances in density functional theory approach for optoelectronics properties of graphene

Graphene has received tremendous attention among diverse 2D materials because of its remarkable properties. Its emergence over the last two decades gave a new and distinct dynamic to the study of materials, with several research projects focusing on exploiting its intrinsic properties for optoelectronic devices. This review provides a comprehensive overview of several published articles based on density functional theory and recently introduced machine learning approaches applied to study the electronic and optical properties of graphene. A comprehensive catalogue of the bond lengths, band gaps, and formation energies of various doped graphene systems that determine thermodynamic stability was reported in the literature. In these studies, the peculiarity of the obtained results reported is consequent on the nature and type of the dopants, the choice of the XC functionals, the basis set, and the wrong input parameters. The different density functional theory models, as well as the strengths and uncertainties of the ML potentials employed in the machine learning approach to enhance the prediction models for graphene, were elucidated. Lastly, the thermal properties, modelling of graphene heterostructures, the superconducting behaviour of graphene, and optimization of the DFT models are grey areas that future studies should explore in enhancing its unique potential. Therefore, the identified future trends and knowledge gaps have a prospect in both academia and industry to design future and reliable optoelectronic devices.

Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2

Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the "COVID Moonshot" project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00410-9.

ProFeatX: A parallelized protein feature extraction suite for machine learning

Machine learning algorithms have been successfully applied in proteomics, genomics and transcriptomics. and have helped the biological community to answer complex questions. However, most machine learning methods require lots of data, with every data point having the same vector size. The biological sequence data, such as proteins, are amino acid sequences of variable length, which makes it essential to extract a definite number of features from all the proteins for them to be used as input into machine learning models. There are numerous methods to achieve this, but only several tools let researchers encode their proteins using multiple schemes without having to use different programs or, in many cases, code these algorithms themselves, or even come up with new algorithms. In this work, we created ProFeatX, a tool that contains 50 encodings to extract protein features in an efficient and fast way supporting desktop as well as high-performance computing environment. It can also encode concatenated features for protein-protein interactions. The tool has an easy-to-use web interface, allowing non-experts to use feature extraction techniques, as well as a stand-alone version for advanced users. ProFeatX is implemented in C++ and available on GitHub at https://github.com/usubioinfo/profeatx. The web server is available at http://bioinfo.usu.edu/profeatx/.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Agromorphological characterization of cacao (Theobroma cacao L.) accessions from the germplasm bank of the National Institute of Agrarian Innovation, Peru

Agromorphological characterization of cacao accessions in Peru is currently an important tool in the conservation and genetic improvement of cacao germplasm. The objective of this study was to carry out the morphological and agronomic characterization of 113 cacao accessions from the Huarangopampa germplasm bank. Tree, leaf, flower, fruit and seed descriptors were used. The data collected were processed by descriptive statistics using multivariate techniques. Five groups were formed according to similar characteristics. The accessions of group 1 are vigorous trees with an pod index of 19.27 pods/kg of seeds; the groups that presented better differential characteristics were group 2 with erect tree architecture, intermediate vigorousness, purple seed color and pod index of 20.07 pods/kg of seeds and group 3, which had the highest number of accessions with the lowest pod index of 18.77 pods/kg of seeds, besides being vigorous trees and having purple seeds. On the other hand, group 4 presented a particular characteristic of white seed color and high pod indexes with 22.11 pods/kg of seeds. Finally, group 5 accessions were characterized by intermediate tree architecture and vigor with an pod index of 21.3 pods/kg of seeds. The morphoagronomic characterization constitutes a first advance in the identification of cacaos with potential for genetic improvement and advances in the Peruvian chocolate industry.

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The morphoagronomic characterization of cacao clones resulted in the differentiation of 5 groups.

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Group three presented the largest number of accessions differentiated by quantitative characteristics.

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The productivity indicator was decisive in the formation of the groups.

Structural and vibrational investigation of Cis-Trans isomers of potent insecticide allethrin

In this research, the optimised structural and vibrational properties of cis-trans isomers of powerful insecticide allethrin were theoretically studied in gas phase and in aqueous and ethanol solutions by using hybrid B3LYP/6-311 +  + g(d,p) level of theory. The results revealed that the permittivity of solvent has influence on the properties of both isomers, thus, higher dipole moments and solvation energies are observed in water, a solvent of higher permittivity (78.355) than ethanol. Complete vibrational assignments of both isomers were done by combining the experimental IR spectrum of allethrin with the scaled quantum mechanical force field (SQMFF) methodology and the determination of corresponding scaled force constants in gas phase and aqueous solution are reported. Different signs of dihedral O2C10C6C4 angles of both isomers (negative in cis and positive in trans) support the differences in the vibrational assignments. Natural bond orbital (NBO) calculations suggest that both isomers are highly stable in gas phase and aqueous solution and that the side chains and five member's rings are involved in the n → σ* interactions. However, atoms in molecules (AIM) studies reveal a higher stability of form cis in both media than the trans one. Merz-Kollman (MK), Mulliken and natural population atomic (NPA) charges for both isomers support the higher hydration of trans isomer in aqueous media and, hence, the higher solvation energy in water (ΔG_C/ZPVE =  - 80.29 kJ/mol). Changes in the bond orders of O and C atoms of side chain are observed in water as a consequence of hydration. The higher stability of the cis form in the above solutions could be explained by the lower solvation energy in water, as supported by AIM calculations. The studies of frontier orbital reveal that the cis form in both media is sligthly more reactive than the trans form.

Extended continuous similarity indices: theory and application for QSAR descriptor selection

Extended (or n-ary) similarity indices have been recently proposed to extend the comparative analysis of binary strings. Going beyond the traditional notion of pairwise comparisons, these novel indices allow comparing any number of objects at the same time. This results in a remarkable efficiency gain with respect to other approaches, since now we can compare N molecules in O(N) instead of the common quadratic O(N²) timescale. This favorable scaling has motivated the application of these indices to diversity selection, clustering, phylogenetic analysis, chemical space visualization, and post-processing of molecular dynamics simulations. However, the current formulation of the n-ary indices is limited to vectors with binary or categorical inputs. Here, we present the further generalization of this formalism so it can be applied to numerical data, i.e. to vectors with continuous components. We discuss several ways to achieve this extension and present their analytical properties. As a practical example, we apply this formalism to the problem of feature selection in QSAR and prove that the extended continuous similarity indices provide a convenient way to discern between several sets of descriptors.

Descriptors for some Compounds with Pharmacological Activity; Calculation of Properties

Abraham model solute descriptors have been determined for nisoldipine, nizatidine, loratadine, zonisamide, oxaprozin, rebamipide, domperidone, temozolomide, 'florfenicol', florfenicol A, dapsone, chrysin, benorilate, β-lapachone, and Ipriflavone based on published partition coefficients, molar solubilities and gas chromatographic retention indices. The calculated solute descriptors, combined with our previously published Abraham model correlations, are used to predict several important physicochemical and biological properties, such as air-water, air-blood, air-lung, air-fat, air-skin, water-lipid, water-membrane and water-skin partition coefficients, as well as permeation from water through skin.

Machine Learning for In Silico ADMET Prediction

ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule's pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety are considered some of the major causes of clinical attrition in the development of new chemical entities. In past decades, various machine learning or quantitative structure-activity relationship (QSAR) methods have been successfully integrated in the modeling of ADMET. Recent advances have been made in the collection of data and the development of various in silico methods to assess and predict ADMET of bioactive compounds in the early stages of drug discovery and development process.

Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features

In vivo micronucleus assay is the widely used genotoxic test to determine the extent of chromosomal aberrations caused by the chemicals in human beings, which plays a significant role in the drug discovery paradigm. To reduce the uncertainties of the in vivo experiments and the expenses, we intended to develop novel machine learning-based tools to predict the toxicity of the compounds with high precision. A total of 372 compounds with known toxicity information were retrieved from the PubChem Bioassay database and literature. The fingerprints and descriptors of the compounds were generated using PaDEL and ChemSAR, respectively, for the analysis. The performance of the models was assessed using the three tires of evaluation strategies such as fivefold, tenfold, and validation by external dataset. Further, structural alerts causing genotoxicity of the compounds were identified using SARpy method. Of note, fingerprint-based random forest model built in our analysis is able to demonstrate the highest accuracy of about 0.97 during tenfold cross-validation. In essence, our study highlights that structural alerts such as chlorocyclohexane and trimethylamine are likely to be the leading cause of toxicity in humans. Indeed, we believe that random forest model generated in this study is appropriate for reduction of test animals and should be considered in the future for the good practice of animal welfare.

Learning 3D medical image keypoint descriptors with the triplet loss

PURPOSE

We propose to learn a 3D keypoint descriptor which we use to match keypoints extracted from full-body CT scans. Our methods are inspired by 2D keypoint descriptor learning, which was shown to outperform hand-crafted descriptors. Adapting these to 3D images is challenging because of the lack of labelled training data and high memory requirements.

METHOD

We generate semi-synthetic training data. For that, we first estimate the distribution of local affine inter-subject transformations using labelled anatomical landmarks on a small subset of the database. We then sample a large number of transformations and warp unlabelled CT scans, for which we can subsequently establish reliable keypoint correspondences using guided matching. These correspondences serve as training data for our descriptor, which we represent by a CNN and train using the triplet loss with online triplet mining.

RESULTS

We carry out experiments on a synthetic data reliability benchmark and a registration task involving 20 CT volumes with anatomical landmarks used for evaluation purposes. Our learned descriptor outperforms the 3D-SURF descriptor on both benchmarks while having a similar runtime.

CONCLUSION

We propose a new method to generate semi-synthetic data and a new learned 3D keypoint descriptor. Experiments show improvement compared to a hand-crafted descriptor. This is promising as literature has shown that jointly learning a detector and a descriptor gives further performance boost.

Solvation parameter model: Tutorial on its application to separation systems for neutral compounds

The solvation parameter model affords a useful tool to model distribution properties of neutral compounds in biphasic separation systems. Common applications include column characterization and method development in gas chromatography; reversed-phase, micellar and hydrophilic interaction liquid chromatography; supercritical fluid chromatography; and micellar electrokinetic chromatography. The characterization of the distribution properties of liquid-liquid partition systems is another major application of this model. This tutorial is aimed at establishing good practices for the application of the model to separation systems. Suitable experimental protocols to determine system constants by multiple linear regression analysis and descriptors by the Solver method are presented; statistical tools to evaluate model quality are discussed; and model-specific data analysis tools based on system maps and correlation diagrams are described.

ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

QNAR modeling of cytotoxicity of mixing nano-TiO2 and heavy metals

The Quantitative Structure-Activity Relationship (QSAR) has been used to investigate organic mixtures but QSAR in the nanomaterial field (QNAR) is still new. Toxicity is a result of the interaction of many substances. QNAR research focuses on a single nanomaterial in the long-term. It is difficult to find an appropriate descriptor to build a model due to the complexity of the mixture. Here, we attempt to build a QNAR model to predict cell viability for HK-2 cells exposed to a mixture containing nano-TiO₂ and heavy metals. HK-2 cells were exposed to four groups of mixtures containing heavy-metals and nanomaterials and CCK8 was added to obtain the number of living cells. At the same time, ROS was investigated to study this mechanism. Each descriptor of the components and mixtures were obtained using the formula D_mix= [Formula: see text] respectively. We used the Multiple Partial Least Squares Regression (PLS) and Random Forest Regression (RF) to build a QNAR model. Both models reliably predict and assess viability of HK-2 cells exposed to the mixture. The RF model showed greater stability and higher precision in toxicity predictability and can be applied to environmental nano-toxicology.

Computer-aided modeling of triazole analogues, docking studies of the compounds on DNA gyrase enzyme and design of new hypothetical compounds with efficient activities

The increasing problem of multi-drug resistant-tuberculosis has focused attention on developing new drugs that are not only active against drug-resistant tuberculosis, but also shorten the lengthy therapy. Therefore, this work employs the application of modeling technique to predict the inhibition activities of some prominent compounds which been reported to be efficient against Mycobacterium tuberculosis. To accomplish the purpose of this work, multiple regression and genetic function approximation were adopted to create the model. The established model was swayed with topological descriptors; MATS7s, SpMin4_Bhv, TDB3v and RDF70v. More also, interactions between the compounds and the target protein 'DNA gyrase' were evaluated via molecular docking approach utilizing the PyRx and discovery studio simulation software. Based on the docking analysis, compound 20 has the most noticeable binding affinity of -16.5 kcal/mol. Therefore, compound 20 served as a reference structural template and insight to design fourteen novel hypothetical agents with more prominent anti-tubercular activities. More also, compound 20j was observed with the highest activity among the designed compounds with a prominent binding affinity of -24.3 kcal/mol. Therefore, this research recommends in-vivo, in-vitro screening and pharmacokinetic properties to be carried out in order to determine the toxicity of the designed compounds.Communicated by Ramaswamy H. Sarma.

Oral nutritional support to adult patients with acute intestinal Graft Versus Host Disease (GVHD): A proposal for dietary intervention as a model to clinical trials

BACKGROUND

Acute Graft Versus Host Disease (GVHD) affects about 20%-80% of the patients after the hematopoietic stem cell transplant (HSCT) and it is amongst the main causes of morbidity and mortality both in children and adults. The intestine is one of the most affected organs by GVHD causing important alterations in the nutritional status and quality of life, considering that the dysfunctional intestine could decrease food intake as well as an inappropriate dietary plan could worsen the clinical condition. In addition to GVHD, chemotherapy conditioning regimen suppresses the immune system, promotes mucositis and increases the risk of infectious complications. Taking the above into consideration, when per oral diet is possible; the food choices should be carefully planned and monitored to promote nutritional support and avoid worsening the intestinal function and clinical condition.

OBJECTIVE

This work was aimed to present a practice guideline proposal, to be validated, based on literature review, regarding to oral dietary recommendations for acute intestinal GVHD after HTSC.

METHODS

Two research phases were defined: Phase one: evidence-based literature review; Phase 2: Practice Guideline Proposal. 1: Evidence based literature review SEARCH METHODS: A literature review (1997-2019) was performed including PubMed, in English, and Lilacs, in Portuguese electronic database to address the subject of dietary intervention for intestinal GVHD related to the HSCT, with children and adults, whose receiving oral or tube feeding nutrition therapy.

SELECTION CRITERIA

The study selection was based on the PRISMA method. Controlled clinical trials were searched. Randomization was not possible considering the rare condition.

DATA COLLECTION AND ANALYSIS

Two independent authors assessed the abstracts of the selected studies to determine the articles feasible to compose the review as the base to elaborate the practice guideline proposal protocol, object of the present study. To determine the level of evidence of the selected article, GRADE criteria were used.

MAIN RESULTS

One controlled clinical trial study was included. The study was developed in Japan with a total of 35 patients. The dietary plan was characterized by gradual increasing food consistency/density. They found better nutritional parameters in the treated group, however, following GRADE criteria, we rated the quality of evidence as very low.

AUTHORS' CONCLUSIONS

We could not demonstrate confidence in the effect estimate based on the selected study. However, considering the lack of literature information and the relevance of the topic, we decided to proceed and propose a practice guideline for an oral diet protocol for acute intestinal GVHD as a reference to be a starting point to validate protocols in future clinical trials. 2: Practice Guideline Proposal The criteria to elaborate the protocol were based on the RIGHT Statement. In addition to the literate information about diet and intestinal health, recommendations already adopted in the Service of Bone Marrow Transplant in the Complex Hospital of Clinics of Curitiba, in the state of Paraná, Brazil, were also considered.

Pyrazines: A diverse class of earthy-musty odorants impacting drinking water quality and consumer satisfaction

The presence of earthy-musty odors in drinking water is a major concern for water suppliers and consumers worldwide. While geosmin and 2-methylisoborneol are the most studied earthy-musty odor-causing compounds, pyrazine and its alkyl and methoxy compounds possess similar odors and are widely distributed in nature, foods, and beverages. In this study, odor characteristics of pyrazines and their presence in natural and treated waters were determined. Pyrazine, 2,6-dimethyl-pyrazine (DMP), 2,3,5-trimethyl-pyrazine (TrMP), 2-ethyl-5(6)-methyl-pyrazine (EMP), 2,3,5,6-tetramethyl-pyrazine (TeMP), 2-isobutyl-3-methoxy-pyrazine (IBMP) and 2-isopropyl-3-methoxy-pyrazine (IPMP) were measured in source and finished drinking water across China. 2-Methoxy-3,5-dimethyl-pyrazine (MDMP), IBMP, and IPMP were investigated in rivers in Virginia, USA. The results showed that "musty" and "sweet" were the most common descriptors for pyrazine, DMP, MDMP, TrMP, and TeMP. While IBMP and IPMP were never detected in 140 source or drinking water samples from across China, pyrazine, DMP, MDMP, TrMP, and TeMP occurred throughout with concentrations of n.d.-62.2 ng/L-aq in source water and n.d.-39.6 ng/L-aq in finished water. IBMP, IPMP, and MDMP were present in two Virginia rivers; MDMP occurred in 18% of the samples with concentrations of n.d.-4.4 ng/L, many of which were above the aqueous odor threshold of 0.043 ng/L MDMP. The removal efficiencies through conventional water treatment were poor, ranging from negative removals to ∼10%. Advanced oxidation water treatment could only remove EMP and TrMP. The widespread presence of earthy-musty-sweet pyrazines in source and drinking waters on two continents, their poor removal during water treatment, and ng/L odor threshold concentrations confirm their potential to be T&O issues for consumers.

A review: The challenge, consensus, and confusion of describing odors and tastes in drinking water

Documentation exists for many chemicals that cause tastes and odors in water, however, water suppliers do not routinely monitor for these chemicals. Effective management of a taste-and-odor (T&O) problem in drinking water often requires good verbal description of the offending sensory experience. Experience demonstrates that obtaining verbal descriptions is challenging. To improve our understanding of communications, sensory science literature was reviewed to obtain descriptors for twenty-one chemicals acknowledged to cause T&O issues in drinking water. The review focused on pure chemicals above their odor threshold concentrations. Results reveal that descriptors follow four general categories. For select chemicals, strong consensus exists around a single or very few appropriate descriptors. Examples are "salty" for sodium and "chlorinous" for free chlorine. The next category has moderate agreement for several descriptors, with at least one major descriptor. For example the microbiological metabolite 2-methylisoborneol is most commonly described as "earthy/musty/moldy" but also "camphor, grass, and sweet". Some chemicals have weak agreement on their descriptors, but overall associate words with similar meaning. An example is the chemical toluene with descriptors of "solvent-like" words including "solvent", "gasoline", "paint-like", "cleaning fluid", and "etherish", but also "vinegar" and "sweet". The last chemical category possesses diverse descriptors with no consensus. For example, the oxylipin n-heptanal is described as "oily, fatty, chemical, musty/earthy/moldy, rancid, sweaty, grass, sickening, and stale". While descriptor diversity for select chemicals may not identify the cause of T&O, understanding that certain chemicals are perceived very differently aids in effective communications and eliminates confusion from expecting consumers or utility personnel to respond with consensus.

Descriptors for terpene esters from chromatographic and partition measurements: Estimation of human odor detection thresholds

We have used gas chromatographic retention data together with other data to obtain Abraham descriptors for 30 terpene esters. These include the air-water partition coefficient, as log Kw, for which no experimental values are available for any terpene ester. The other descriptors are the ester dipolarity, S, the hydrogen bond basicity, B, (the ester hydrogen bond acidity is zero for the esters studied), and L the logarithm of the air-hexadecane partition coefficient. Both S and B are larger than those for simple aliphatic esters, as expected from the terpene ester structures that include ring systems and ethylenic double bonds. These descriptors can then be used to obtain a large number of physicochemical and environmental properties of terpene esters. We have analyzed experimental results on human odor detection thresholds and have constructed another equation for the calculation of these thresholds, to go with a previous equation that we have reported. Then the descriptors for terpene esters can be used to estimate the important odor detection thresholds.

Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques

Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a combination of supervised and unsupervised machine learning techniques to fill the missing values. A series of classification models (supervised learning) was developed to predict class label, and self-organizing map approach (unsupervised learning) was used to estimate relative distances between nanoparticles and refine results obtained during supervised learning. In this study, genotoxicity of 49 silicon and metal oxide nanoparticles in Ames and Comet tests. Collected literature data did not demonstrate significant variations related to the change of size including selected bulk materials. Genotoxicity-related features of nanomaterials were represented by ionic characteristics. General tendencies found in the current study were convincingly linked to known theories of genotoxic action at nano-level. Mechanisms of primary and secondary genotoxic effects were discussed in the context of developed models.

The In Silico Fischer Lock-and-Key Model: The Combined Use of Molecular Descriptors and Docking Poses for the Repurposing of Old Drugs

Not always lead compound and/or derivatives are suitable for the specific biological target for which they are designed but, in some cases, discarded compounds proved to be good binders for other biological targets; therefore, drug repurposing constitute a valid alternative to avoid waste of human and financial resources. Our virtual lock-and-key methods, VLKA and Conf-VLKA, furnish a strong support to predict the efficacy of a designed drug a priori its biological evaluation, or the correct biological target for a set of the selected compounds, allowing thus the repurposing of known and unknown, active and inactive compounds.

3D-QSAR studies of the chemical modification of hydroxyl groups of biomass (cellulose, hemicelluloses and lignin) using quantum chemical descriptors

The models of Quantitative Structure-Activity Relationship (QSAR) are indeed useful for understanding the mechanism of chemical modification of wood by the method of acetylation. This study shows that the electrophile index ω in combination with other descriptors, namely LUMO energy, HOMO, hardness (η) and chemical potential (μ), prove their utility in predicting the chemical modification of wood (gain in mass). The QSAR models are developed through use of Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) methods. The statistical results indicate that the multiple correlation coefficient R² = 0.987, R² adjusted = 0.981 and RMCE = 0.012. This shows both the favorable estimation stability and the appropriate predictive power. The results also indicate that there are significant correlations between the weight gain and the highest occupied molecular orbital HOMO. They also show that the chemical potential μ which are useful for modeling the wood modification are characterized by large hydrophobic properties and high electrophilic powers and therefore could be applied effectively in estimating the acetylation of wood. Finally, the developed model shows that cellulose and hemicellulose are mostly affected by the chemical modification of hydroxyl groups.

Novel Electrotopological Atomic Descriptors for the Prediction of Xenobiotic Cytochrome P450 Reactions

Cytochrome P450 (CYP) is an enzyme family that plays a crucial role in metabolism, mainly metabolizing xenobiotics to produce non-toxic structures, however, some metabolized products can cause hepatotoxicity. Hence, predicting the structures of CYP products is an important task in designing non-hepatotoxic drugs. Here, we have developed novel atomic descriptors to predict the sites of metabolism (SoM) in CYP substrates. We proposed descriptors that describe topological and electrostatic characteristics of CYP substrates using Gasteiger charge. The proposed descriptors were applied to CYP3A4 data analysis as a case study. As a result of the descriptor selection, we obtained a gradient boosting decision tree-based SoM classification model that used 139 existing descriptors and the proposed 45 descriptors, and the model performed well in terms of the Matthews correlation coefficient. We also developed a structure converter to predict CYP products. This converter correctly generated 51 structural formulas of experimentally observed CYP3A4 products according to a manual evaluation.

Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework

The European market for pesticides is currently legislated through the well-developed Regulation (EC) No. 1107/2009. This regulation promotes the competitiveness of European agriculture, recognizing the necessity of safe pesticides for human and animal health and the environment to protect crops against pests, diseases and weeds. In this sense, nanotechnology can provide a tremendous opportunity to achieve a more rational use of pesticides. However, the lack of information regarding nanopesticides and their fate and behavior in the environment and their effects on human and animal health is inhibiting rapid nanopesticide incorporation into European Union agriculture. This review analyzes the recent state of knowledge on nanopesticide risk assessment, highlighting the challenges that need to be overcame to accelerate the arrival of these new tools for plant protection to European agricultural professionals. Novel nano-Quantitative Structure-Activity/Structure-Property Relationship (nano-QSAR/QSPR) tools for risk assessment are analyzed, including modeling methods and validation procedures towards the potential of these computational instruments to meet the current requirements for authorization of nanoformulations. Future trends on these issues, of pressing importance within the context of the current European pesticide legislative framework, are also discussed. Standard protocols to make high-quality and well-described datasets for the series of related but differently sized nanoparticles/nanopesticides are required.

Activity evaluation of some psychoactive drugs with the application of QSAR/QSPR modeling methods

A set of psychoactive drugs has been analyzed with the use of quantitative structure-activity/property relationships methods. The purpose of this study was to demonstrate both the common and differentiating characteristics of the above-mentioned chemical compounds, physicochemical as well as pharmacological based on the quantum chemical calculations and selected biological activity data and chromatographic retention parameters. During the study, the ab initio model of molecular modeling was performed and PCA, FA, and MLR as the types of chemometric approach. QSAR/QSPR models were proposed based on chosen statistically significant descriptors. The relationship between the structure and biological activity data was able to class and describe the psychoactive properties of the molecules studied. The applied chemometric approaches revealed the influential features of tested structures responsible for their pharmacological activity together with some additional physicochemical properties.

Evaluation of the antioxidant properties of curcumin derivatives by genetic function algorithm

The prevalence of degenerative diseases in recent time has triggered extensive research on their control. This condition could be prevented if the body has an efficient antioxidant mechanism to scavenge the free radicals which are their main causes. Curcumin and its derivatives are widely employed as antioxidants. The free radical scavenging activities of curcumin and its derivatives have been explored in this research by the application of quantitative structure activity relationship (QSAR). The entire data set was optimized at the density functional theory (DFT) level using the Becke's three-parameter Lee-Yang-Parr hybrid functional (B3LYP) in combination with the 6-311G^∗ basis set. The training set was subjected to QSAR studies by genetic function algorithm (GFA). Five predictive QSAR models were developed and statistically subjected to both internal and external validations. Also the applicability domain of the developed model was accessed by the leverage approach. Furthermore, the variation inflation factor, (VIF), mean effect (MF) and the degree of contribution (DC) of each descriptor in the resulting model were calculated. The developed models met all the standard requirements for acceptability upon validation with highly impressive results (R=0.965,R2=0.931,Q2(RCV2)=0.887,Rpred2=0.844,cRp2=0.842s=0.226,rmsep=0.362). Based on the results of this research, the most crucial descriptor that influence the free radical scavenge of the curcumins is the nsssN (count of atom-type E-state: >N-) descriptor with DC and MF values of 12.980 and 0.965 respectively.

Application of two-dimensional binary fingerprinting methods for the design of selective Tankyrase I inhibitors

In the present study, five important binary fingerprinting techniques were used to model novel flavones for the selective inhibition of Tankyrase I. From the fingerprints used: the fingerprint atom pairs resulted in a statistically significant 2D QSAR model using a kernel-based partial least square regression method. This model indicates that the presence of electron-donating groups positively contributes to activity, whereas the presence of electron withdrawing groups negatively contributes to activity. This model could be used to develop more potent as well as selective analogues for the inhibition of Tankyrase I. Schematic representation of 2D QSAR work flow.

Suitability of existing Musa morphological descriptors to characterize East African highland 'matooke' bananas

Morphological traits are commonly used for characterizing plant genetic resources. Germplasm characterization should be based on distinctly identifiable, stable and heritable traits that are expressed consistently and are easy to distinguish by the human eye. Characterization and documentation of a representative sample of East African highland bananas (Lujugira-Mutika subgroup) was carried out following an internationally accepted standard protocol for bananas. Eleven cultivars were characterized using an existing set of minimum descriptors (31 qualitative and quantitative traits) with the aim of determining stable descriptors and the ability of these descriptors to distinguish among East African highland banana cultivars. There was variation in stability of these descriptors within cultivars and across the 11 cultivars. Only 10 (32%) out of 31 descriptors studied were stable in the 11 cultivars. However, they had similar scores and therefore are not suitable to distinguish between cultivars within this group. Nonetheless, these 10 descriptors may be useful for distinguishing the East African highland bananas as a group from other groups of bananas. A few descriptors were unique to the cultivar 'Tereza' and may be used to distinguish this cultivar from other 'matooke' cultivars. None of the quantitative descriptors were stable.

Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

Spectral-dynamic representation of DNA sequences

A graphical representation of DNA sequences in which the distribution of a particular base B=A,C,G,T is represented by a set of discrete lines has been formulated. The methodology of this approach has been borrowed from two areas of physics: spectroscopy and dynamics. Consequently, the set of discrete lines is referred to as the B-spectrum. Next, the B-spectrum is transformed to a rigid body composed of material points. In this way a dynamic representation of the DNA sequence has been obtained. The centers of mass of these rigid bodies, divided by their moments of inertia, have been taken as the descriptors of the spectra and, thus, of the DNA sequences. The performance of this method on a standard set of data commonly applied by authors introducing new approaches to bioinformatics (the first exons of β-globin genes of different species) proved to be very good.

Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forests and Extra-trees regressors

Antimicrobial peptides are host defense peptides being viewed as replacement to broad-spectrum antibiotics due to varied advantages. Hepatitis is the commonest infectious disease of liver, affecting 500 million globally with reported adverse side effects in treatment therapy. Antimicrobial peptides active against hepatitis are called as anti-hepatitis peptides (AHP). In current work, we present Extratrees and Random Forests based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra-trees regression model yielded a very high performance in terms coefficient of determination (R2) as 0.95 for test set and 0.7 for the independent dataset. We hypothesize that the developed model can further be used to identify potentially active anti-hepatitis peptides with a high level of reliability.

Conf-VLKA: A structure-based revisitation of the Virtual Lock-and-key Approach

In a previous work, we developed the in house Virtual Lock-and-Key Approach (VLKA) in order to evaluate target assignment starting from molecular descriptors calculated on known inhibitors used as an information source. This protocol was able to predict the correct biological target for the whole dataset with a good degree of reliability (80%), and proved experimentally, which was useful for the target fishing of unknown compounds. In this paper, we tried to remodel the previous in house developed VLKA in a more sophisticated one in order to evaluate the influence of 3D conformation of ligands on the accuracy of the prediction. We applied the same previous algorithm of scoring and ranking but, this time, combining it with a structure-based approach as docking. For this reason, we retrieved from the RCSB Protein Data Bank (PDB), the available 3D structures of the biological targets included into the previous work, and we used them to calculate poses of the 7352 dataset compounds in the VLKA biological targets. First, docking protocol has been used to retrieve docking scores, then, from the docked poses of each molecule, 3D-descriptors were calculated (Conf-VLKA), While the use of the simple docking scores proved to be inadequate to improve compounds classification, the Conf-VLKA showed some interesting variations compared to the original VLKA, especially for targets whose ligands present a high number of rotamers. This work represent a first preliminary study to be completed using other techniques such as induced fit docking or molecular dynamics structure clustering to take into account the protein side chains adaptation to ligands structures.

The Marine Strategy Framework Directive and the ecosystem-based approach – pitfalls and solutions

The European Marine Strategy Framework Directive aims at good environmental status (GES) in marine waters, following an ecosystem-based approach, focused on 11 descriptors related to ecosystem features, human drivers and pressures. Furthermore, 29 subordinate criteria and 56 attributes are detailed in an EU Commission Decision. The analysis of the Decision and the associated operational indicators revealed ambiguity in the use of terms, such as indicator, impact and habitat and considerable overlap of indicators assigned to various descriptors and criteria. We suggest re-arrangement and elimination of redundant criteria and attributes avoiding double counting in the subsequent indicator synthesis, a clear distinction between pressure and state descriptors and addition of criteria on ecosystem services and functioning. Moreover, we suggest the precautionary principle should be followed for the management of pressures and an evidence-based approach for monitoring state as well as reaching and maintaining GES.

Assessment of the electronic structure and properties of trichothecene toxins using density functional theory

A comprehensive quantum chemical study was carried out on 35 type A and B trichothecenes and biosynthetic precursors, including selected derivatives of deoxynivalenol and T-2 toxin. Quantum chemical properties, Natural Bond Orbital (NBO) analysis, and molecular parameters were calculated on structures geometry optimized at the B3LYP/6-311+G** level. Type B trichothecenes possessed significantly larger electrophilicity index compared to the type A trichothecenes studied. Certain hydroxyl groups of deoxynivalenol, nivalenol, and T-2 toxin exhibited considerable rotation during molecular dynamics simulations (5 ps) at the B3LYP/6-31G** level in implicit aqueous solvent. Quantitative structure activity relationship (QSAR) models were developed to evaluate toxicity and detection using genetic algorithm, principal component, and multilinear analyses. The models suggest electronegativity and several 2-dimensional topological descriptors contain important information related to trichothecene cytotoxicity, phytotoxicity, immunochemical detection, and cross-reactivity.

Non-dopamine receptor ligands for the treatment of Parkinson's disease. Insight into the related chemical/property space

Extensive biochemical and clinical studies have increasingly recognized Parkinson's disease as a highly complex and multi-faceted neurological disorder having branched non-motor symptoms including sleep disorders, pain, constipation, psychosis, depression, and fatigue. A wide range of biological targets in the brain deeply implicated in this pathology resulted in a plethora of novel small-molecule compounds with promising activity. This review thoroughly describes the chemical space of non-dopamine receptor ligands in terms of diversity, isosteric/bioisosteric morphing, and molecular descriptors.

Reactivity of polychlorinated biphenyls in nucleophilic and electrophilic substitutions

To explain the chemical reactivity of polychlorinated biphenyls in nucleophilic (S(N)) and electrophilic (S(E)) substitutions, quantum chemical calculations were carried out at the B3LYP/6-31G(d) level of the Density Functional Theory in gas phase. Carbon atomic charges in biphenyl structure were calculated by the Atoms-in-Molecules method. Chemical hardness and global electrophilicity index parameters were determined for congeners. A comparison of calculated descriptors and experimental data for congener reactivity in the S(N) and S(E) reactions was made. It is shown that interactions in the S(N) mechanism are reactions of the hard acid-hard base type, these are the most effective in case of highly chlorinated substrates. To explain the congener reactivity in the SE reactions, correct descriptors were not established. The obtained results can be used to carry out chemical transformations of the polychlorinated biphenyls in order to prepare them for microbiological destruction or preservation.

Good Environmental Status of marine ecosystems: what is it and how do we know when we have attained it?

The European Marine Strategy Framework Directive (MSFD) requires EU Member States (MS) to achieve Good Environmental Status (GEnS) of their seas by 2020. We address the question of what GEnS entails especially with regard to the level at which targets are set (descriptors, criteria, indicators), to scales for assessments (regional, sub-divisions, site-specific), and to difficulties in putting into practice the GEnS concept. We propose a refined and operational definition of GEnS, indicating the data and information needed to all parts of that definition. We indicate the options for determining when GEnS has been met, acknowledge the data and information needs for each option, and recommend a combination of existing quantitative targets and expert judgement. We think that the MSFD implementation needs to be less complex than shown for other similar directives, can be based largely on existing data and can be centred on the activities of the Regional Seas Conventions.

Descriptors of 2D-dynamic graphs as a classification tool of DNA sequences

A new tool of the classification of DNA sequences is introduced. The method is based on 2D-dynamic graphs and their descriptors. Using the descriptors created by centers of masses, moments of inertia, angles between the x axis and the principal axis of inertia of the 2D-dynamic graphs one can obtain classification diagrams in which similar sequences are clustered in separated areas.

In silico drug metabolism and pharmacokinetic profiles of natural products from medicinal plants in the Congo basin

Purpose

Drug metabolism and pharmacokinetics (DMPK) assessment has come to occupy a place of interest during the early stages of drug discovery today. The use of computer modelling to predict the DMPK and toxicity properties of a natural product library derived from medicinal plants from Central Africa (named ConMedNP). Material from some of the plant sources are currently employed in African Traditional Medicine.

Methods

Computer-based methods are slowly gaining ground in this area and are often used as preliminary criteria for the elimination of compounds likely to present uninteresting pharmacokinetic profiles and unacceptable levels of toxicity from the list of potential drug candidates, hence cutting down the cost of discovery of a drug.

In the present study, we present an in silico assessment of the DMPK and toxicity profile of a natural product library containing ~3,200 compounds, derived from 379 species of medicinal plants from 10 countries in the Congo Basin forests and savannas, which have been published in the literature. In this analysis, we have used 46 computed physico-chemical properties or molecular descriptors to predict the absorption, distribution, metabolism and elimination and toxicity (ADMET) of the compounds.

Results

This survey demonstrated that about 45% of the compounds within the ConMedNP compound library are compliant, having properties which fall within the range of ADME properties of 95% of currently known drugs, while about 69% of the compounds have ≤ 2 violations. Moreover, about 73% of the compounds within the corresponding “drug-like” subset showed compliance.

Conclusions

In addition to the verified levels of “drug-likeness”, diversity and the wide range of measured biological activities, the compounds from medicinal plants in Central Africa show interesting DMPK profiles and hence could represent an important starting point for hit/lead discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-12) contains supplementary material, which is available to authorized users.

Graphical and numerical representations of DNA sequences: statistical aspects of similarity

New approaches aiming at a detailed similarity/dissimilarity analysis of DNA sequences are formulated. Several corrections that enrich the information which may be derived from the alignment methods are proposed. The corrections take into account the distributions along the sequences of the aligned bases (neglected in the standard alignment methods). As a consequence, different aspects of similarity, as for example asymmetry of the gene structure, may be studied either using new similarity measures associated with four-component spectral representation of the DNA sequences or using alignment methods with corrections introduced in this paper. The corrections to the alignment methods and the statistical distribution moment-based descriptors derived from the four-component spectral representation of the DNA sequences are applied to similarity/dissimilarity studies of β-globin gene across species. The studies are supplemented by detailed similarity studies for histones H1 and H4 coding sequences. The data are described according to the latest version of the EMBL database. The work is supplemented by a concise review of the state-of-art graphical representations of DNA sequences.