Discovering selective antiferroptotic inhibitors of the 15LOX/PEBP1 complex noninterfering with biosynthesis of lipid mediators.

Programmed ferroptotic death eliminates cells in all major organs and tissues with imbalanced redox metabolism due to overwhelming iron-catalyzed lipid peroxidation under insufficient control by thiols (Glutathione (GSH)). Ferroptosis has been associated with the pathogenesis of major chronic degenerative diseases and acute injuries of the brain, cardiovascular system, liver, kidneys, and other organs, and its manipulation offers a promising new strategy for anticancer therapy. This explains the high interest in designing new small-molecule-specific inhibitors against ferroptosis. Given the role of 15-lipoxygenase (15LOX) association with phosphatidylethanolamine (PE)-binding protein 1 (PEBP1) in initiating ferroptosis-specific peroxidation of polyunsaturated PE, we propose a strategy of discovering antiferroptotic agents as inhibitors of the 15LOX/PEBP1 catalytic complex rather than 15LOX alone. Here we designed, synthesized, and tested a customized library of 26 compounds using biochemical, molecular, and cell biology models along with redox lipidomic and computational analyses. We selected two lead compounds, FerroLOXIN-1 and 2, which effectively suppressed ferroptosis in vitro and in vivo without affecting the biosynthesis of pro-/anti-inflammatory lipid mediators in vivo. The effectiveness of these lead compounds is not due to radical scavenging or iron-chelation but results from their specific mechanisms of interaction with the 15LOX-2/PEBP1 complex, which either alters the binding pose of the substrate [eicosatetraenoyl-PE (ETE-PE)] in a nonproductive way or blocks the predominant oxygen channel thus preventing the catalysis of ETE-PE peroxidation. Our successful strategy may be adapted to the design of additional chemical libraries to reveal new ferroptosis-targeting therapeutic modalities.

Deep learning identifies synergistic drug combinations for treating COVID-19.

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single-agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.

Therapeutic candidates for the Zika virus identified by a high-throughput screen for Zika protease inhibitors.

When Zika virus emerged as a public health emergency there were no drugs or vaccines approved for its prevention or treatment. We used a high-throughput screen for Zika virus protease inhibitors to identify several inhibitors of Zika virus infection. We expressed the NS2B-NS3 Zika virus protease and conducted a biochemical screen for small-molecule inhibitors. A quantitative structure-activity relationship model was employed to virtually screen ∼138,000 compounds, which increased the identification of active compounds, while decreasing screening time and resources. Candidate inhibitors were validated in several viral infection assays. Small molecules with favorable clinical profiles, especially the five-lipoxygenase-activating protein inhibitor, MK-591, inhibited the Zika virus protease and infection in neural stem cells. Members of the tetracycline family of antibiotics were more potent inhibitors of Zika virus infection than the protease, suggesting they may have multiple mechanisms of action. The most potent tetracycline, methacycline, reduced the amount of Zika virus present in the brain and the severity of Zika virus-induced motor deficits in an immunocompetent mouse model. As Food and Drug Administration-approved drugs, the tetracyclines could be quickly translated to the clinic. The compounds identified through our screening paradigm have the potential to be used as prophylactics for patients traveling to endemic regions or for the treatment of the neurological complications of Zika virus infection.

The AKT modulator A-443654 reduces α-synuclein expression and normalizes ER stress and autophagy.

Accumulation of α-synuclein is a main underlying pathological feature of Parkinson's disease and α-synucleinopathies, for which lowering expression of the α-synuclein gene (SNCA) is a potential therapeutic avenue. Using a cell-based luciferase reporter of SNCA expression we performed a quantitative high-throughput screen of 155,885 compounds and identified A-443654, an inhibitor of the multiple functional kinase AKT, as a potent inhibitor of SNCA. HEK-293 cells with CAG repeat expanded ATXN2 (ATXN2-Q58 cells) have increased levels of α-synuclein. We found that A-443654 normalized levels of both SNCA mRNA and α-synuclein monomers and oligomers in ATXN2-Q58 cells. A-443654 also normalized levels of α-synuclein in fibroblasts and iPSC-derived dopaminergic neurons from a patient carrying a triplication of the SNCA gene. Analysis of autophagy and endoplasmic reticulum stress markers showed that A-443654 successfully prevented α-synuclein toxicity and restored cell function in ATXN2-Q58 cells, normalizing the levels of mTOR, LC3-II, p62, STAU1, BiP, and CHOP. A-443654 also decreased the expression of DCLK1, an inhibitor of α-synuclein lysosomal degradation. Our study identifies A-443654 and AKT inhibition as a potential strategy for reducing SNCA expression and treating Parkinson's disease pathology.

Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.

Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, the research team developes and experimentally validates a computational model based on the transformer architecture able to predict the immune activities of NANPs. It is anticipated that the freely accessible computational tool that is called an "artificial immune cell," or AI-cell, will aid in addressing the current critical public health challenges related to safety criteria of nucleic acid therapies in a timely manner and promote the development of novel biomedical tools.

Photostability and Antiproliferative Activity of Furan Analogues of Combretastatin A-4.

Cancer is one of the most serious health problems that usually require heavy medical treatment. It is important to ensure that no additional burden is placed on patients due to the modes of administration and/or poor quality of pharmaceuticals. In this regard, understanding, quantifying, and improving the photostability (resistance to UV light or sunlight) of drugs is among the important elements that can improve the patient's quality of life. In this work, the photochemical properties of a wide range of furanone analogues of combretastatin A-4 and their antiproliferative activity against A-431 epidermoid carcinoma cells were studied in a search for compounds with improved photostability and antiproliferative activity. It was found that the incorporation of an arylidene moiety led to a significant improvement in photostability, while the antiproliferative activity strongly depends on the nature of the aryl residue in the arylidene moiety. The high photostability of arylidenes was achieved due to the delocalization of the central double bond of the 1,3,5-hexatriene system, which limited the 6π-electrocyclization. The best results in terms of antiproliferative activity were obtained for thiophene arylidene (IC50 = 0.6 µM) and 3,4-diarylfuran (IC50 = 0.047 µM). The obtained results address the lack of data available now in scientific literature on the photodegradation of combretastatin A-4 analogues and should be taken into account in studies of the side effects of pharmaceuticals based on them.

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2.

Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19.

A critical overview of computational approaches employed for COVID-19 drug discovery.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

EFFECT OF IMUNOFAN INFLUENCE ON THE STRUCTURE OF THE TESTES, HORMONAL AND CYTOKINE PROFILE OF IMMATURE EXPERIMENTAL ANIMALS.

OBJECTIVE: The aim: It was the establishing the features of changes in the structure of the testes of experimental animals, as well as immunological, hormonal and cytokine parameters of blood plasma during stimulation. PATIENTS AND METHODS: Materials and methods: The study was carried out on 60 white male immature rats. Imunofan was used at a dosage of 50 υg. The organs were weighed, the relative mass was calculated, and the linear dimensions were determined. The morphometriv parameters of the epitheliospermatogenic layer were measured. The number of supporting cells and interstitial endocrinocytes was counted, as well as the volume of cell nuclei. The level of reproductive hormones in the plasma and the concentration of cytokines were determined. RESULTS: Results: The results obtained indicate the development of readaptation processes in the testes after the use of the Imunofan against the background of environmental immunosuppression. The ability of the drug to stimulate the production of cytokines and hormones normalizes the function of immunocompetent cells, which is manifested in the stabilization of the immune homeostasis of the testes. CONCLUSION: Conclusions: In response to the immunostimulating effect of Imunofan, a pronounced reaction is observed on the part of the testes of immature animals, which is due to the sensitivity of morphogenetic processes in the organ to external influences and the formation of mechanisms of their regulation, characteristic of this period of ontogenesis.

Compound screening in cell-based models of tau inclusion formation: Comparison of primary neuron and HEK293 cell assays.

The hallmark pathological features of Alzheimer's disease (AD) brains are senile plaques, comprising ß-amyloid (Aß) peptides, and neuronal inclusions formed from tau protein. These plaques form 10-20 years before AD symptom onset, whereas robust tau pathology is more closely associated with symptoms and correlates with cognitive status. This temporal sequence of AD pathology development, coupled with repeated clinical failures of Aß-directed drugs, suggests that molecules that reduce tau inclusions have therapeutic potential. Few tau-directed drugs are presently in clinical testing, in part because of the difficulty in identifying molecules that reduce tau inclusions. We describe here two cell-based assays of tau inclusion formation that we employed to screen for compounds that inhibit tau pathology: a HEK293 cell-based tau overexpression assay, and a primary rat cortical neuron assay with physiological tau expression. Screening a collection of ∼3500 pharmaceutical compounds with the HEK293 cell tau aggregation assay, we obtained only a low number of hit compounds. Moreover, these compounds generally failed to inhibit tau inclusion formation in the cortical neuron assay. We then screened the Prestwick library of mostly approved drugs in the cortical neuron assay, leading to the identification of a greater number of tau inclusion inhibitors. These included four dopamine D2 receptor antagonists, with D2 receptors having previously been suggested to regulate tau inclusions in a Caenorhabditis elegans model. These results suggest that neurons, the cells most affected by tau pathology in AD, are very suitable for screening for tau inclusion inhibitors.

Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis.

MOTIVATION: The transcriptomic data are being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are the data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details about the samples which we would like to control (gender, biological state, treatment, etc.) can be used as style components. RESULTS: The proposed style transfer solution is based on Conditional Variational Autoencoders, Y-Autoencoders and adversarial feature decomposition. To quantitatively measure the quality of the style transfer, neural network classifiers which predict the style and semantics after training on real expression were used. Comparison with several existing style-transfer based approaches shows that proposed model has the highest style prediction accuracy on all considered datasets while having comparable or the best semantics prediction accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/NRshka/stvae-source. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition.

Cytochrome P450 enzymes are responsible for the metabolism of >75% of marketed drugs, making it essential to identify the contributions of individual cytochromes P450 to the total clearance of a new candidate drug. Overreliance on one cytochrome P450 for clearance levies a high risk of drug-drug interactions; and considering that several human cytochrome P450 enzymes are polymorphic, it can also lead to highly variable pharmacokinetics in the clinic. Thus, it would be advantageous to understand the likelihood of new chemical entities to interact with the major cytochrome P450 enzymes at an early stage in the drug discovery process. Typical screening assays using human liver microsomes do not provide sufficient information to distinguish the specific cytochromes P450 responsible for clearance. In this regard, we experimentally assessed the metabolic stability of ∼5000 compounds for the three most prominent xenobiotic metabolizing human cytochromes P450, i.e., CYP2C9, CYP2D6, and CYP3A4, and used the data sets to develop quantitative structure-activity relationship models for the prediction of high-clearance substrates for these enzymes. Screening library included the NCATS Pharmaceutical Collection, comprising clinically approved low-molecular-weight compounds, and an annotated library consisting of drug-like compounds. To identify inhibitors, the library was screened against a luminescence-based cytochrome P450 inhibition assay; and through crossreferencing hits from the two assays, we were able to distinguish substrates and inhibitors of these enzymes. The best substrate and inhibitor models (balanced accuracies ∼0.7), as well as the data used to develop these models, have been made publicly available (https://opendata.ncats.nih.gov/adme) to advance drug discovery across all research groups. SIGNIFICANCE STATEMENT: In drug discovery and development, drug candidates with indiscriminate cytochrome P450 metabolic profiles are considered advantageous, since they provide less risk of potential issues with cytochrome P450 polymorphisms and drug-drug interactions. This study developed robust substrate and inhibitor quantitative structure-activity relationship models for the three major xenobiotic metabolizing cytochromes P450, i.e., CYP2C9, CYP2D6, and CYP3A4. The use of these models early in drug discovery will enable project teams to strategize or pivot when necessary, thereby accelerating drug discovery research.

Photocyclization of Diarylethenes: The Effect of Electron and Proton Acceptors as Additives.

The effect of electron and proton acceptors on the photocyclization of diarylethenes has been studied. Without any additives, the deprotonation reaction is predominant, although other processes, including the sigmatropic shift, are not excluded. A deuterium exchange experiment has shown that a strong base (DABCO) facilitates the deprotonation reaction, thereby limiting the sigmatropic shift. In the presence of an oxidizing agent or additional sources of radicals (O2, I2, TEMPO), the processes of deprotonation and rearrangement (H-shift) are practically not observed, and the reaction proceeds along a radical pathway with the formation of phenanthrene or its heterocyclic analogue.

Photoinduced Skeletal Rearrangement of Diarylethenes: Photorelease of Lewis Acid and Synthetic Applications.

The skeletal photorearrangement including 6π-electrocyclization induced by UV light of ortho-halogen-substituted diarylethenes has been studied. It has been found that the reaction pathways leading to bi- or tricyclic frameworks depend on the kind of halogen substituent and solvent. Photocyclization with halogen abstraction leads to bicyclic fused aromatics, while the tricyclic frameworks are formed due to the tandem 6π-electrocyclization/sigmatropic shift reaction. THF is preferred as the solvent in the former process and chloroform in the latter reaction. It was found for the first time that, owing to the ability of this series of diarylethenes to undergo skeletal photorearrangement with the release of the bromide cation, they can be used both as brominating agents and as Lewis acids for catalyzing electrophilic reactions.

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the "Big Data" Era.

The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human ether-à-go-go-related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here, we perform a comprehensive comparison of hERG effect prediction models based on classical approaches (random forests and gradient boosting) and modern AI methods [deep neural networks (DNNs) and recurrent neural networks (RNNs)]. The training set (∼9000 compounds) was compiled by integrating the hERG bioactivity data from the ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay. We utilized different molecular descriptors including the latent descriptors, which are real-value continuous vectors derived from chemical autoencoders trained on a large chemical space (>1.5 million compounds). The models were prospectively validated on ∼840 in-house compounds screened in the same thallium flux assay. The best results were obtained with the XGBoost method and RDKit descriptors. The comparison of models based only on latent descriptors revealed that the DNNs performed significantly better than the classical methods. The RNNs that operate on SMILES provided the highest model sensitivity. The best models were merged into a consensus model that offered superior performance compared to reference models from academic and commercial domains. Furthermore, we shed light on the potential of AI methods to exploit the big data in chemistry and generate novel chemical representations useful in predictive modeling and tailoring a new chemical space.

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules.

Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC ß-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ∼53% in the ß-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/).

Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors.

Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. To this end, more than 100 QSAR models for the IC50 values of ligands interacting with cyclooxygenase 1,2 (COX) and seed lipoxygenase (LOX), obtained from ChEMBL database were created using the GUSAR software. The QSAR models were tested on the external set, including 26 new thiazolidinone derivatives, which were experimentally tested for COX-1,2/LOX inhibition. The IC50 values of the derivatives varied from 89 µM to 26 µM for LOX, from 200 µM to 0.018 µM for COX-1, and from 210 µM to 1 µM for COX-2. This study showed that the accuracy of the models is dependent on the distribution of IC50 values of low activity compounds in the training sets. In the most cases, QSAR models created based on the combined training sets had advantages in comparison with QSAR models, based on a single publication. We introduced a new method of combination of quantitative data from different experimental studies based on the data of reference compounds, which was called "scaling".

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .

Prebiotic Syntheses Under Shock in the Water - Formamide - Potassium Bicarbonate - Sodium Hydroxide System.

Syntheses under shock in nitrogen bubbled samples of the water - formamide - bicarbonate - sodium hydroxide system at pH 8.63, 9.46 and 10.44 were performed in the stainless steel preservation capsules. The maximum temperature and pressure in the capsules reached 545 K and 12.5 GPa respectively. Using the LC-MS-MS analysis, the 21 synthesis products have been identified, including amines and polyamines, carboxamide, acetamide and urea derivatives, compounds containing aniline, pyrrolidine, pyrrole, imidazole, as well as alcohol groups. It was found that the Fischer-Tropsch-type syntheses with catalysis on the surface of the stainless steel of the conservation capsule associated with the adsorbed hydrogen cyanide reactions and transamidation processes play the main role in the shock syntheses. Formation reactions of all the above-mentioned compounds have been suggested. It was proposed that hydrogen cyanide, ammonia, isocyanic acid, aminonitrile, aminoacetonitrile, as well as adsorbed species H(a), CH(a), CH2(a), CHOH(a), NH2(a) and H2CNH(a) are especially important for the formation of the products. A reduction reaction of adsorbed bicarbonate with hydrogen to formaldehyde has been first postulated. In the studied system also classical reactions take place - Wöhler's synthesis of urea and Butlerov's synthesis of methenamine. It was suggest that material of meteorites may be an effective catalyst in the Fischer-Tropsch-type syntheses at falling of the iron-nickel meteorites in the water - formamide regions on the early Earth. It was concluded that life could have originated due to the impact of meteorites on alkaline water-formamide lakes located near volcanoes on the early Earth.