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Novichoks represent the fourth generation of chemical warfare agents with paralytic and convulsive effects, produced clandestinely during the Cold War by the Soviet Union. This novel class of organophosphate compounds is characterised by severe toxicity, which, for example, we have already experienced three times (Salisbury, Amesbury, and Navalny's case) as a society. Then the public debate about the true nature of Novichoks began, realising the importance of examining the properties, especially the toxicological aspects of these compounds. The updated Chemical Warfare Agents list registers over 10,000 compounds as candidate structures for Novichoks. Consequently, conducting experimental research for each of them would be a huge challenge. Additionally, due to the enormous risk of contact with hazardous Novichoks, in silico assessments were applied to estimate their toxicity safely. In silico toxicology provides a means of identifying hazards of compounds before synthesis, helping to fill gaps and guide risk minimisation strategies. A new approach to toxicology testing first considers the prediction of toxicological parameters, eliminating unnecessary animal studies. This new generation risk assessment (NGRA) can meet the modern requirements of toxicological research. The present study explains, using QSAR models, the acute toxicity of the Novichoks studied ( n  = 17). The results indicate that the toxicity of Novichoks varies. The deadliest turned out to be A-232, followed by A-230 and A-234. On the other hand, the \"Iranian\" Novichok and C01-A038 compounds turned out to be the least toxic. Developing reliable in silico methods to predict various parameters is essential to prepare for the upcoming use of Novichoks.

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

Ptychotis verticillata Duby, referred to as Nûnkha in the local language, is a medicinal plant that is native to Morocco. This particular plant is a member of the Apiaceae family and has a longstanding history in traditional medicine and has been utilized for therapeutic purposes by practitioners for generations. The goal of this research is to uncover the phytochemical makeup of the essential oil extracted from P. verticillata, which is indigenous to the Touissite region in Eastern Morocco. The extraction of the essential oil of P. verticillata (PVEO) was accomplished through the use of hydro-distillation via a Clevenger apparatus. The chemical profile of the essential oil was then determined through analysis utilizing gas chromatography–mass spectrometry (GC/MS). The study findings indicated that the essential oil of P. verticillata is composed primarily of Carvacrol (37.05%), D-Limonene (22.97%), γ-Terpinene (15.97%), m-Cymene (12.14%) and Thymol (8.49%). The in vitro antioxidant potential of PVEO was evaluated using two methods: the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical trapping assay and the ferric reducing antioxidant power (FRAP) method. The data demonstrated considerable radical scavenging and relative antioxidative power. Escherichia coli, Staphylococcus aureus, Listeria innocua, and Pseudomonas aeruginosa were the most susceptible bacterial strains tested, while Geotrichum candidum, Candida albicans, and Rhodotorula glutinis were the most resilient fungi strains. PVEO had broad-spectrum antifungal and antibacterial properties. To elucidate the antioxidative and antibacterial characteristics of the identified molecules, we applied the methodology of molecular docking, a computational approach that forecasts the binding of a small molecule to a protein. Additionally, we utilized the Prediction of Activity Spectra for Substances (PASS) algorithm; Absorption, Distribution, Metabolism, and Excretion (ADME); and Pro-Tox II (to predict the toxicity in silico) tests to demonstrate PVEO’s identified compounds’ drug-likeness, pharmacokinetic properties, the anticipated safety features after ingestion, and the potential pharmacological activity. Finally, our findings scientifically confirm the ethnomedicinal usage and usefulness of this plant, which may be a promising source for future pharmaceutical development.

Introduction: Toxoplasma gondii ( T. gondii ) is a widely prevalent parasite from the phylum apicomplexan and is the causative agent of toxoplasmosis, which affects almost all warm‐blooded animals, including humans. Presently, conventional treatments for toxoplasmosis have limited effectiveness against the cystic forms of the parasite. Thus, developing an efficient and safe vaccine for control and prevention of toxoplasmosis is crucial. Calcium‐dependent protein kinases (CDPKs) are essential in governing crucial biological processes like anchoring to host cell, cellular infiltration, dynamic locomotion, and escape mechanisms. Because there are no reports on immunization with CDPK8 to date, this study evaluated the fundamental biochemical traits and immunogenic epitopes of the CDPK8 protein through diverse bioinformatics tools. Materials and Methods: We examined the physicochemical attributes, antigenicity, potential B‐ and T‐cell epitopes, tertiary and secondary structures, transmembrane domains, subcellular localization, allergenicity, and other characteristics of the CDPK8 protein. Results: CDPK8 exhibited notable surface accessibility, flexibility, antigenicity, and hydrophilicity indices. Epitope prediction results from diverse bioinformatics databases revealed multiple premiums T‐cell and B‐cell within the CDPK8 protein shows its viability as an essential component in a T. gondii vaccine formulation. Our findings suggest that to minimize the risk of errors and failures in the laboratory, utilizing in silico software for predicting the functional and structural properties of the CDPK8 protein could be a crucial and essential step in preventing cost wastage. Conclusion: To confirm the immunogenicity of the anticipated sequences, validation in an appropriate mouse model using various bioinformatics tools is recommended. Therefore, it is highly recommended that this protein be evaluated in silico and biological platforms settings to characterize its structural and immunological roles for potential prophylactic agent.

Nerve agents are organophosphate chemical warfare agents that exert their toxic effects by irreversibly inhibiting acetylcholinesterase, affecting the breakdown of the neurotransmitter acetylcholine in the synaptic cleft. Due to the risk of exposure to dangerous nerve agents and for animal welfare reasons, in silico methods have been used to assess acute toxicity safely. The next-generation risk assessment (NGRA) is a new approach for predicting toxicological parameters that can meet modern requirements for toxicological research. The present study explains the acute toxicity of the examined V-series nerve agents (n = 9) using QSAR models. Toxicity Estimation Software Tool (ver. 4.2.1 and ver. 5.1.2), QSAR Toolbox (ver. 4.6), and ProTox-II browser application were used to predict the median lethal dose. The Simplified Molecular Input Line Entry Specification (SMILES) was the input data source. The results indicate that the most deadly V-agents were VX and VM, followed by structural VX analogues: RVX and CVX. The least toxic turned out to be V-sub x and Substance 100A. In silico methods for predicting various parameters are crucial for filling data gaps ahead of experimental research and preparing for the upcoming use of nerve agents.

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski’s rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ). Graphic Abstract

With the advancements of science, antibiotics have emerged as an amazing gift to the human and animal healthcare sectors for the treatment of bacterial infections and other diseases. However, the evolution of new bacterial strains, along with excessive use and reckless consumption of antibiotics have led to the unfolding of antibiotic resistances to an excessive level. Multidrug resistance is a potential threat worldwide, and is escalating at an extremely high rate. Information related to drug resistance, and its regulation and control are still very little. To interpret the onset of antibiotic resistances, investigation on molecular analysis of resistance genes, their distribution and mechanisms are urgently required. Fine-tuned research and resistance profile regarding ESKAPE pathogen is also necessary along with other multidrug resistant bacteria. In the present scenario, the interaction of bacterial infections with SARS-CoV-2 is also crucial. Tracking and in-silico analysis of various resistance mechanisms or gene/s are crucial for overcoming the problem, and thus, the maintenance of relevant databases and wise use of antibiotics should be promoted. Creating awareness of this critical situation among individuals at every level is important to strengthen the fight against this fast-growing calamity. The review aimed to provide detailed information on antibiotic resistance, its regulatory molecular mechanisms responsible for the resistance, and other relevant information. In this article, we tried to focus on the correlation between antimicrobial resistance and the COVID-19 pandemic. This study will help in developing new interventions, potential approaches, and strategies to handle the complexity of antibiotic resistance and prevent the incidences of life-threatening infections.

The study of the brain's dynamical activity is opening a window to help the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and the dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in‐silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting‐state fMRI data, fitted to a linear model of activity propagation. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to severe reduction of glucose metabolism as measured with [18F]FDG‐PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results shed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior. Propagation of neural events and network responses are disrupted in patients with disorders of consciousness. Loss of consciousness is related to the malfunctioning of two neural circuits: The posterior cortical regions fail to integrate information in altered consciousness and a breakdown of information broadcasting of subcortical cortical areas. Loss of network responses in these patients is related to glucose metabolism.