PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery.

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

Kinetic Intricacies of the Light Emission and Antiradical Influence of Exogenous Bioantioxidants Transformation Products in the Chemiluminescence Bioantioxidant Assay.

The subject matter of the reported work refers to studying the interactions followed by the excited-state generation, which are chemical models of oxidative processes leading to a weak light emission emerging from living cells, and to explore the possibilities of using them as tools for evaluating the activity of oxygen-metabolism modulators, most prominently, natural bioantioxidants of biomedical value in particular. Methodologically, major attention is paid to analyzing the shapes of the time profiles of the light emission derived from a model sensory system in the presence of lipid samples of vegetable and animal (fish) origin rich in bioantioxidants. As a result, a modified reaction mechanism involving 12 elementary steps is proposed to rationalize the light-emission kinetics in the presence of natural bioantioxidants. We conclude that free radicals formed from bioantioxidants and their dimerization products contribute significantly to the general antiradical activity of lipid samples, which should be taken into account in developing efficient bioantioxidant assays for biomedical applications and while establishing the mechanisms of bioantioxidant effects on metabolic processes in vivo.

COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity.

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.

Aging Induces Profound Changes in sncRNA in Rat Sperm and These Changes Are Modified by Perinatal Exposure to Environmental Flame Retardant.

Advanced paternal age at fertilization is a risk factor for multiple disorders in offspring and may be linked to age-related epigenetic changes in the father's sperm. An understanding of aging-related epigenetic changes in sperm and environmental factors that modify such changes is needed. Here, we characterize changes in sperm small non-coding RNA (sncRNA) between young pubertal and mature rats. We also analyze the modification of these changes by exposure to environmental xenobiotic 2,2',4,4'-tetrabromodiphenyl ether (BDE-47). sncRNA libraries prepared from epididymal spermatozoa were sequenced and analyzed using DESeq 2. The distribution of small RNA fractions changed with age, with fractions mapping to rRNA and lncRNA decreasing and fractions mapping to tRNA and miRNA increasing. In total, 249 miRNA, 908 piRNA and 227 tRNA-derived RNA were differentially expressed (twofold change, false discovery rate (FDR) p ≤ 0.05) between age groups in control animals. Differentially expressed miRNA and piRNA were enriched for protein-coding targets involved in development and metabolism, while piRNA were enriched for long terminal repeat (LTR) targets. BDE-47 accelerated age-dependent changes in sncRNA in younger animals, decelerated these changes in older animals and increased the variance in expression of all sncRNA. Our results indicate that the natural aging process has profound effects on sperm sncRNA profiles and this effect may be modified by environmental exposure.

How to reconcile wood production and biodiversity conservation? The Pan-European boreal forest history gradient as an "experiment".

There are currently competing demands on Europe's forests and the finite resources and services that they can offer. Forestry intensification that aims at mitigating climate change and biodiversity conservation is one example. Whether or not these two objectives compete can be evaluated by comparative studies of forest landscapes with different histories. We test the hypothesis that indicators of wood production and biodiversity conservation are inversely related in a gradient of long to short forestry intensification histories. Forest management data containing stand age, volume and tree species were used to model the opportunity for wood production and biodiversity conservation in five north European forest regions representing a gradient in landscape history from very long in the West and short in the East. Wood production indicators captured the supply of coniferous wood and total biomass, as well as current accessibility by transport infrastructure. Biodiversity conservation indicators were based on modelling habitat network functionality for focal bird species dependent on different combinations of stand age and tree species composition representing naturally dynamic forests. In each region we randomly sampled 25 individual 100-km2 areas with contiguous forest cover. Regarding wood production, Sweden's Bergslagen region had the largest areas of coniferous wood, followed by Vitebsk in Belarus and Zemgale in Latvia. NW Russia's case study regions in Pskov and Komi had the lowest values, except for the biomass indicator. The addition of forest accessibility for transportation made the Belarusian and Swedish study region most suitable for wood and biomass production, followed by Latvia and two study regions in NW Russian. Regarding biodiversity conservation, the overall rank among regions was opposite. Mixed and deciduous habitats were functional in Russia, Belarus and Latvia. Old Scots pine and Norway spruce habitats were only functional in Komi. Thus, different regional forest histories provide different challenges in terms of satisfying both wood production and biodiversity conservation objectives in a forest management unit. These regional differences in northern Europe create opportunities for exchanging experiences among different regional contexts about how to achieve both objectives. We discuss this in the context of land-sharing versus land-sparing.

Green infrastructure development at European Union's eastern border: Effects of road infrastructure and forest habitat loss.

The functionality of forest patches and networks as green infrastructure may be affected negatively both by expanding road networks and forestry intensification. We assessed the effects of (1) the current and planned road infrastructure, and (2) forest loss and gain, on the remaining large forest landscape massifs as green infrastructure at the EU's eastern border region in post-socialistic transition. First, habitat patch and network functionality in 1996-98 was assessed using habitat suitability index modelling. Second, we made expert interviews about road development with planners in 10 administrative regions in Poland, Belarus and Ukraine. Third, forest loss and gain inside the forest massifs, and gain outside them during the period 2001-14 were measured. This EU cross-border region hosts four remaining forest massifs as regional green infrastructure hotspots. While Poland's road network is developing fast in terms of new freeways, city bypasses and upgrades of road quality, in Belarus and Ukraine the focus is on maintenance of existing roads, and no new corridors. We conclude that economic support from the EU, and thus rapid development of roads in Poland, is likely to reduce the permeability for wildlife of the urban and agricultural matrix around existing forest massifs. However, the four identified forest massifs themselves, forming the forest landscape green infrastructure at the EU's east border, were little affected by road development plans. In contrast, forest loss inside massifs was high, especially in Ukraine. Only in Poland forest loss was balanced by gain. Forest gain outside forest massifs was low. To conclude, pro-active and collaborative spatial planning across different sectors and countries is needed to secure functional forest green infrastructure as base for biodiversity conservation and human well-being.

Antioxidant potential of curcumin-related compounds studied by chemiluminescence kinetics, chain-breaking efficiencies, scavenging activity (ORAC) and DFT calculations.

This study compares the ability to scavenge different peroxyl radicals and to act as chain-breaking antioxidants of monomers related to curcumin (1): dehydrozingerone (2), zingerone (3), (2Z,5E)-ethyl 2-hydroxy-6-(4-hydroxy-3-methoxyphenyl)-4-oxohexa-2,5-dienoate (4), ferulic acid (5) and their corresponding C 2-symmetric dimers 6-9. Four models were applied: model 1 - chemiluminescence (CL) of a hydrocarbon substrate used for determination of the rate constants (k A) of the reactions of the antioxidants with peroxyl radicals; model 2 - lipid autoxidation (lipidAO) used for assessing the chain-breaking antioxidant efficiency and reactivity; model 3 - oxygen radical absorbance capacity (ORAC), which yields the activity against peroxyl radicals generated by an azoinitiator; model 4 - density functional theory (DFT) calculations at UB3LYP/6-31+G(d,p) level, applied to explain the structure-activity relationship. Dimers showed 2-2.5-fold higher values of k A than their monomers. Model 2 gives information about the effects of the side chains and revealed much higher antioxidant activity for monomers and dimers with α,ß-unsaturated side chains. Curcumin and 6 in fact are dimers of the same monomer 2. We conclude that the type of linkage between the two "halves" by which the molecule is made up does not exert influence on the antioxidant efficiency and reactivity of these two dimers. The dimers and the monomers demonstrated higher activity than Trolox (10) in aqueous medium (model 3). A comparison of the studied compounds with DL-α-tocopherol (11), Trolox and curcumin is made. All dimers are characterized through lower bond dissociation enthalpies (BDEs) than their monomers (model 4), which qualitatively supports the experimental results.

AGTR2 gene polymorphism is associated with muscle fibre composition, athletic status and aerobic performance.

Muscle fibre type is a heritable trait and can partly predict athletic success. It has been proposed that polymorphisms of genes involved in the regulation of muscle fibre characteristics may predispose the muscle precursor cells of a given individual to be predominantly fast or slow. In the present study, we examined the association between 15 candidate gene polymorphisms and muscle fibre type composition of the vastus lateralis muscle in 55 physically active, healthy men. We found that rs11091046 C allele carriers of the angiotensin II type 2 receptor gene (AGTR2; involved in skeletal muscle development, metabolism and circulatory homeostasis) had a significantly higher percentage of slow-twitch fibres than A allele carriers [54.2 (11.1) versus 45.2 (10.2)%; P = 0.003]. These data indicate that 15.2% of the variation in muscle fibre composition of the vastus lateralis muscle can be explained by the AGTR2 genotype. Next, we investigated the frequencies of the AGTR2 alleles in 2178 Caucasian athletes and 1220 control subjects. The frequency of the AGTR2 C allele was significantly higher in male and female endurance athletes compared with power athletes (males, 62.7 versus 51.7%, P = 0.0038; females, 56.6 versus 48.1%, P = 0.0169) and control subjects (males, 62.7 versus 51.0%, P = 0.0006; elite female athletes, 65.1 versus 55.2%, P = 0.0488). Furthermore, the frequency of the AGTR2 A allele was significantly over-represented in female power athletes (51.9%) in comparison to control subjects (44.8%, P = 0.0069). We also found that relative maximal oxygen consumption was significantly greater in male endurance athletes with the AGTR2 C allele compared with AGTR2 A allele carriers [n = 28; 62.3 (4.4) versus 57.4 (6.0) ml min(-1) kg(-1); P = 0.0197]. Taken together, these results demonstrate that the AGTR2 gene C allele is associated with an increased proportion of slow-twitch muscle fibres, endurance athlete status and aerobic performance, while the A allele is associated with a higher percentage of fast-twitch fibres and power-oriented disciplines.

Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation.

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.

Measurement, collaborative learning and research for sustainable use of ecosystem services: landscape concepts and Europe as laboratory.

Policies at multiple levels pronounce the need to encompass both social and ecological systems in governance and management of natural capital in terms of resources and ecosystems. One approach to knowledge production and learning about landscapes as social-ecological systems is to compare multiple case studies consisting of large spaces and places. We first review the landscape concepts' biophysical, anthropogenic, and intangible dimensions. Second, we exemplify how the different landscape concepts can be used to derive measurable variables for different sustainability indicators. Third, we review gradients in the three dimensions of the term landscape on the European continent, and propose to use them for the stratification of multiple case studies of social-ecological systems. We stress the benefits of the landscape concepts to measure sustainability, and how this can improve collaborative learning about development toward sustainability in social-ecological systems. Finally, analyses of multiple landscapes improve the understanding of context for governance and management.

Biomedical generative pre-trained based transformer language model for age-related disease target discovery.

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery.

Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor.

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 µM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.

Optimization of small RNA extraction and comparative study of NGS library preparation from low count sperm samples.

Recent studies demonstrate that sperm epigenome is a vehicle that conveys paternal experiences to offspring phenotype. That evidence triggers interest of both experimental and epidemiological studies of epigenetic markers in sperm. Since samples are often unique in epidemiological studies, a careful and efficient use of the material is a critical requirement. The goal of this study was to provide optimization of methods for the isolation of small RNAs from spermatozoa and library preparation for sequencing. A total 67 fractionated sperm samples from the Russian Children's Study biobank prospectively collected at 18-20 years of age were used to isolate small RNAs with median (IQR) input total sperm count 17.0 (7.4-35.9) million. Twenty-four pairs of libraries were prepared using the NEBNext and NEXTFlex kits, 19 libraries using NEBNext and 6 using NEXTFlex. All libraries were sequenced on NextSeq 500, and the results were evaluated as a function of the number of small non-coding RNA (sncRNA) detected, quality parameters of sequencing libraries, as well as technical features of sample preparation. Although the same amount of miRNA input was used for NEBNext and NEXTFlex libraries, the concentration of DNA in NEBNext libraries was significantly higher in comparison with NEXTFlex libraries. In high input (sperm count >28 million and more than 25 ng miRNA in library) NEXTFlex Small RNA-Seq kit detected more microRNAs. In low input, the NEBNext proved more effective. The tricks and traps to protocol optimization are presented, including an efficient and effector gel-based system for the removal of sequencing library adaptors.

Comparative analysis of novel MGISEQ-2000 sequencing platform vs Illumina HiSeq 2500 for whole-genome sequencing.

The MGISEQ-2000 developed by MGI Tech Co. Ltd. (a subsidiary of the BGI Group) is a new competitor of such next-generation sequencing platforms as NovaSeq and HiSeq (Illumina). Its sequencing principle is based on the DNB and the cPAS technologies, which were also used in the previous version of the BGISEQ-500 device. However, the reagents for MGISEQ-2000 have been refined and the platform utilizes updated software. The cPAS method is an advanced technology based on the cPAL previously created by Complete Genomics. In this paper, the authors compare the results of the whole-genome sequencing of a DNA sample from a Russian female donor performed on MGISEQ-2000 and Illumina HiSeq 2500 (both PE150). Two platforms were compared in terms of sequencing quality, number of errors and performance. Additionally, we performed variant calling using four different software packages: Samtools mpileaup, Strelka2, Sentieon, and GATK. The accuracy of SNP detection was similar in the data generated by MGISEQ-2000 and HiSeq 2500, which was used as a reference. At the same time, a separate indel analysis of the overall error rate revealed similar FPR values and lower sensitivity. It may be concluded with confidence that the data generated by the analyzed sequencing systems is characterized by comparable magnitudes of error and that MGISEQ-2000 and HiSeq 2500 can be used interchangeably for similar tasks like whole genome sequencing.

Rat liver epigenome programing by perinatal exposure to 2,2',4'4'-tetrabromodiphenyl ether.

Perinatal exposures to polybrominated diphenyl ethers permanently reprogram liver metabolism and induce a nonalcoholic fatty liver disease-like phenotype and insulin resistance in rodents. Aim: To test if these changes are associated with altered liver epigenome. Materials & methods: Expression of small RNA and changes in DNA methylation in livers of adult rats were analyzed following perinatal exposure to 2,2',4,4'-tetrabromodiphenyl ether, the polybrominated diphenyl ether congener most prevalent in human tissues. Results: We identified 33 differentially methylated DNA regions and 15 differentially expressed miRNAs. These changes were enriched for terms related to lipid and carbohydrate metabolism, insulin signaling, Type-2 diabetes and nonalcoholic fatty liver disease. Conclusion: Changes in the liver epigenome are a likely candidate mechanism of long-term maintenance of an aberrant metabolic phenotype.

Dissolution of chitosan nanocrystals in aqueous media of different acidity. Molecular dynamic study.

The process of dissolution of chitosan nanocrystals with molecular mass of polymer up to 12.8 kDa in aqueous media of various pH was studied by molecular dynamic simulations with the use of the improved force field GROMOS 56ACARBO_CHT specially developed for the chitosan polymers description. The effect of the media acidity and polymer molecular weight on the dissolution process kinetics has been studied and the regression expressions for kinetic parameters were established. The calculated solution viscosity, Mark-Houwink-Sakurada equation parameters, and pH values of the dissolution beginning are in good agreement with the available experimental data. The uniform/non-uniform distribution of protonated amino groups and hydrogen bonds along the polymeric chains is found to be of key importance parameter for the dissolution process which can be considered as a criterion of dissolution ability.

Updating the Chemiluminescence Oxygen-Aftereffect Method for Determining the Rate Constant of the Peroxy-Radical Self-Reaction: Oxidation of Cyclohexene.

Updating the facile chemiluminescence oxygen-aftereffect method, most suitable for determining the rate constant (kt ) of the peroxy-radical self-reaction (main chemiluminescence channel), pertained to considering the sensitivity of such a method toward a disturbing influence of the peroxy radicals of the initiator of the chain oxidation process. Such a disturbance may derive from the side chemiluminescent reaction, which involves peroxy radicals of both hydrocarbon and initiator. To examine the applicability and limitations of the chemiluminescence method under present scrutiny, cyclohexene was used as the model oxidizable hydrocarbon substrate. Computer simulations of the reaction and chemiluminescence kinetics have demonstrated the validity of the considered methodology at the value of the rate constant of the propagation of the overall chain process by peroxy radicals of the initiator higher than 1 m-1 s-1 . Despite that the chemiluminescence time profile and the stationary level of the total chemiluminescence intensity depend on the kinetics of the side chemiluminescence channel and on the ratio of the excited-state generation yields in the mentioned reaction channel and in the main chemiluminescence process, the value of kt assessed by the oxygen-aftereffect method has been found independent of variation of these characteristics.

Bromination of bis-(pyridin-2-yl) diselenide in methyl-ene chloride: the reaction mechanism and crystal structures of 1H-pyridine-2-selenenyl dibromide and its cyclo-adduct with cyclo-pentene (3aSR,9aRS)-2,3,3a,9a-tetra-hydro-1H-cyclo-penta-[4,5][1,3]selenazolo[3,2-a]pyridinium bromide.

1H-Pyridine-2-selenenyl dibromide, C5H5NSeBr2, 1, is a product of the bromination of bis-(pyridin-2-yl) diselenide in methyl-ene chloride recrystallization from methanol. Compound 1 is essentially zwitterionic: the negative charge resides on the SeBr2 moiety and the positive charge is delocalized over the pyridinium fragment. The C-Se distance of 1.927 (3) Šis typical of a single bond. The virtually linear Br-Se-Br moiety of 178.428 (15)° has symmetrical geometry, with Se-Br bonds of 2.5761 (4) and 2.5920 (4) Å, and is twisted by 63.79 (8)° relative to the pyridinium plane. The Se atom forms an inter-molecular Se⋯Br contact of 3.4326 (4) Å, adopting a distorted square-planar coordination. In the crystal, mol-ecules of 1 are linked by inter-molecular N-H⋯Br and C-H⋯Br hydrogen bonds, as well as by non-covalent Se⋯Br inter-actions, into a three-dimensional framework. (3aSR,(9aRS)-2,3,3a,9a-Tetra-hydro-1H-cyclo-penta[4,5][1,3]selenazolo[3,2-a]pyridinium-9 bromide, C10H12NSe+·Br-, 2, is a product of the cyclo-addition reaction of 1 with cyclo-pentene. Compound 2 is a salt containing a selenazolopyridinium cation and a bromide anion. Both five-membered rings of the cation adopt envelope conformations. The dihedral angle between the basal planes of these rings is 62.45 (11)°. The Se atom of the cation forms two additional non-covalent inter-actions with the bromide anions at distances of 3.2715 (4) and 3.5683 (3) Å, attaining a distorted square-planar coordination. In the crystal, the cations and anions of 2 form centrosymmetric dimers by non-covalent Se⋯Br inter-actions. The dimers are linked by weak C-H⋯Br hydrogen bonds into double layers parallel to (001).