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The interpretation of ligand–target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand–target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design. [ABSTRACT FROM AUTHOR]

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle–mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license. [ABSTRACT FROM AUTHOR]

Behavioral larval zebrafish screens leverage a high-throughput small molecule discovery format to find neuroactive molecules relevant to mammalian physiology. We screen a library of 650 central nervous system active compounds in high replicate to train deep metric learning models on zebrafish behavioral profiles. The machine learning initially exploited subtle artifacts in the phenotypic screen, necessitating a complete experimental re-run with rigorous physical well-wise randomization. These large matched phenotypic screening datasets (initial and well-randomized) provide a unique opportunity to quantify and understand shortcut learning in a full-scale, real-world drug discovery dataset. The final deep metric learning model substantially outperforms correlation distance-the canonical way of computing distances between profiles-and generalizes to an orthogonal dataset of diverse drug-like compounds. We validate predictions by prospective in vitro radio-ligand binding assays against human protein targets, achieving a hit rate of 58% despite crossing species and chemical scaffold boundaries. These neuroactive compounds exhibit diverse chemical scaffolds, demonstrating that zebrafish phenotypic screens combined with metric learning achieve robust scaffold hopping capabilities.

The tauopathies are defined by pathological tau protein aggregates within a spectrum of clinically heterogeneous neurodegenerative diseases. The primary tauopathies meet the definition of rare diseases in the United States. There is no approved treatment for primary tauopathies. In this context, designing the most efficient development programs to translate promising targets and treatments from preclinical studies to early-phase clinical trials is vital. In September 2022, the Rainwater Charitable Foundation convened an international expert workshop focused on the translation of tauopathy therapeutics through early-phase trials. Our report on the workshop recommends a framework for principled drug development and a companion lexicon to facilitate communication focusing on reproducibility and achieving common elements. Topics include the selection of targets, drugs, biomarkers, participants, and study designs. The maturation of pharmacodynamic biomarkers to demonstrate target engagement and surrogate disease biomarkers is a crucial unmet need. HIGHLIGHTS: Experts provided a framework to translate therapeutics (discovery to clinical trials). Experts focused on the "5 Rights" (target, drug, biomarker, participants, trial). Current research on frontotemporal degeneration, progressive supranuclear palsy, and corticobasal syndrome therapeutics includes 32 trials (37% on biologics) Tau therapeutics are being tested in Alzheimer's disease; primary tauopathies have a large unmet need.

Datafication is driving organizations to invest in data commons, not only to share the costs of data generation, analysis, and curation but, more importantly, to realize synergies in precompetitive research collaborations where private and public motives interact (i.e., semicommons). The fanfare surrounding datafication often hails the sophisticated algorithms used to develop large quantities of data toward greater insight, naively assuming that more data equals better data. Yet for datafication in general and precompetitive research specifically, less attention is awarded to what actually constitutes data and evidence in the first place—that is, to its genesis, construction, and interpretation by heterogeneous scientific and commercial entities. We present the case of Open Targets, a precompetitive collaboration in the life sciences, where publicly funded research, nonprofit foundations, and for-profit pharma collaborate to generate and share data in genomics, proteomics, and bioinformatics. We theorize about the process of data commoning, a political activity in the semicommons where data are created, evidential value is assembled, and scientific meaning converges as data travels, or journeys, across creators, validators, and users. Our findings highlight the effects of relational dynamics and the political nature of data journeys: why these dynamics form, how they are manifested in a precompetitive semicommons, and what implications this can have for the mobility of data as a shared, public good. [ABSTRACT FROM AUTHOR]

In pharmaceutical discovery, the "magic methyl" effect describes a substantial improvement in the pharmacological properties of a drug candidate with the incorporation of methyl groups. Therefore, to expedite the synthesis of methylated drug analogs, late-stage, undirected methylations of C(sp3)-H bonds in complex molecules would be valuable. However, current methods for site-selective methylations are limited to activated C(sp3)-H bonds. Here we describe a site-selective, undirected methylation of unactivated C(sp3)-H bonds, enabled by photochemically activated peroxides and a nickel(II) complex whose turnover is enhanced by an ancillary ligand. The methodology displays compatibility with a wide range of functional groups and a high selectivity for tertiary C-H bonds, making it suitable for the late-stage methylation of complex organic compounds that contain multiple alkyl C-H bonds, such as terpene natural products, peptides, and active pharmaceutical ingredients. Overall, this method provides a synthetic tool to explore the "magic methyl" effect in drug discovery.

Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis (kVR), capture structure-activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF-aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF-aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states.

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

BackgroundRestraining or slowing ageing hallmarks at the cellular level have been proposed as a route to increased organismal lifespan and healthspan. Consequently, there is great interest in anti-ageing drug discovery. However, this currently requires laborious and lengthy longevity analysis. Here, we present a novel screening readout for the expedited discovery of compounds that restrain ageing of cell populations in vitro and enable extension of in vivo lifespan.MethodsUsing Illumina methylation arrays, we monitored DNA methylation changes accompanying long-term passaging of adult primary human cells in culture. This enabled us to develop, test, and validate the CellPopAge Clock, an epigenetic clock with underlying algorithm, unique among existing epigenetic clocks for its design to detect anti-ageing compounds in vitro. Additionally, we measured markers of senescence and performed longevity experiments in vivo in Drosophila, to further validate our approach to discover novel anti-ageing compounds. Finally, we bench mark our epigenetic clock with other available epigenetic clocks to consolidate its usefulness and specialisation for primary cells in culture.ResultsWe developed a novel epigenetic clock, the CellPopAge Clock, to accurately monitor the age of a population of adult human primary cells. We find that the CellPopAge Clock can detect decelerated passage-based ageing of human primary cells treated with rapamycin or trametinib, well-established longevity drugs. We then utilise the CellPopAge Clock as a screening tool for the identification of compounds which decelerate ageing of cell populations, uncovering novel anti-ageing drugs, torin2 and dactolisib (BEZ-235). We demonstrate that delayed epigenetic ageing in human primary cells treated with anti-ageing compounds is accompanied by a reduction in senescence and ageing biomarkers. Finally, we extend our screening platform in vivo by taking advantage of a specially formulated holidic medium for increased drug bioavailability in Drosophila. We show that the novel anti-ageing drugs, torin2 and dactolisib (BEZ-235), increase longevity in vivo.ConclusionsOur method expands the scope of CpG methylation profiling to accurately and rapidly detecting anti-ageing potential of drugs using human cells in vitro, and in vivo, providing a novel accelerated discovery platform to test sought after anti-ageing compounds and geroprotectors.

We previously reported trisubstituted pyrimidine lead compounds, namely, ARN22089 and ARN25062, which block the interaction between CDC42 with its specific downstream effector, a PAK protein. This interaction is crucial for the progression of multiple tumor types. Such inhibitors showed anticancer efficacy in vivo. Here, we describe a second class of CDC42 inhibitors with favorable drug-like properties. Out of the 25 compounds here reported, compound 15 (ARN25499) stands out as the best lead compound with an improved pharmacokinetic profile, increased bioavailability, and efficacy in an in vivo PDX tumor mouse model. Our results indicate that these CDC42 inhibitors represent a promising chemical class toward the discovery of anticancer drugs, with ARN25499 as an additional lead candidate for preclinical development.

DNA-encoded library technology grants access to nearly infinite opportunities to explore the chemical structure space for drug discovery. Successful navigation depends on the design and synthesis of libraries with appropriate physicochemical properties (PCPs) and structural diversity while aligning with practical considerations. To this end, we analyze combinatorial library design constraints including the number of chemistry cycles, bond construction strategies, and building block (BB) class selection in pursuit of ideal library designs. We compare two-cycle library designs (amino acid + carboxylic acid, primary amine + carboxylic acid) in the context of PCPs and chemical space coverage, given different BB selection strategies and constraints. We find that broad availability of amines and acids is essential for enabling the widest exploration of chemical space. Surprisingly, cost is not a driving factor, and virtually, the same chemical space can be explored with budget BBs.

The global surge in multi-drug resistant bacteria, including extended-spectrum β-lactamase (ESBL)-producing Escherichia coli has led to a growing need for new antibacterial compounds. Despite being promising, the potential of fish-derived antimicrobial peptides (AMPs) in combating ESBL-producing E. coli is largely unexplored. In this study, native African catfish antimicrobial peptides (NACAPs) were extracted from the skin mucus of farmed African catfish, Clarias gariepinus, using a combination of 10% acetic acid solvent hydrolysis, 5 kDa ultrafiltration, and C18 hydrophobic interaction chromatography. Peptides were then sequenced using Orbitrap Fusion Lumos Tribrid Mass Spectrometry. The identified peptides were screened for potential antibacterial activity using Random Forest and AdaBoost machine learning algorithms. The most promising peptide was chemically synthesized and evaluated in vitro for safety on rabbit red blood cells and activity against ESBL-producing E. coli (ATCC 35218) utilizing spot-on-lawn and broth dilution methods. Eight peptides ranging from 13 to 22 amino acids with molecular weights between 968.42 and 2434.11 Da were identified. Peptide NACAP-II was non-hemolytic to rabbit erythrocytes (p > 0.05) with a zone of inhibition (ZOI) of 22.7 ± 0.9 mm and a minimum inhibitory concentration (MIC) of 91.3 ± 1.2 μg/mL. The peptide is thus a candidate antibacterial compound with enormous potential applications in the pharmaceutical industry. However, further studies are still required to establish an upscale production strategy and optimize its activity and safety in vivo.

We present a novel small molecule antiviral chemotype that was identified by an unconventional cell-free protein synthesis and assembly-based phenotypic screen for modulation of viral capsid assembly. Activity of PAV-431, a representative compound from the series, has been validated against infectious viruses in multiple cell culture models for all six families of viruses causing most respiratory diseases in humans. In animals, this chemotype has been demonstrated efficacious for porcine epidemic diarrhoea virus (a coronavirus) and respiratory syncytial virus (a paramyxovirus). PAV-431 is shown to bind to the protein 14-3-3, a known allosteric modulator. However, it only appears to target the small subset of 14-3-3 which is present in a dynamic multi-protein complex whose components include proteins implicated in viral life cycles and in innate immunity. The composition of this target multi-protein complex appears to be modified upon viral infection and largely restored by PAV-431 treatment. An advanced analog, PAV-104, is shown to be selective for the virally modified target, thereby avoiding host toxicity. Our findings suggest a new paradigm for understanding, and drugging, the host-virus interface, which leads to a new clinical therapeutic strategy for treatment of respiratory viral disease.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Auranofin, an FDA-approved drug for rheumatoid arthritis, has emerged as a promising antiparasitic medication in recent years. The gold(I) ion in auranofin is postulated to be responsible for its antiparasitic activity. Notably, aurothiomalate and aurothioglucose also contain gold(I), and, like auranofin, they were previously used to treat rheumatoid arthritis. Whether they have antiparasitic activity remains to be elucidated. Herein, we demonstrated that auranofin and similar derivatives, but not aurothiomalate and aurothioglucose, inhibited the growth of Toxoplasma gondii in vitro. We found that auranofin affected the T. gondii biological cycle (lytic cycle) by inhibiting T. gondiis invasion and triggering its egress from the host cell. However, auranofin could not prevent parasite replication once T. gondii resided within the host. Auranofin treatment induced apoptosis in T. gondii parasites, as demonstrated by its reduced size and elevated phosphatidylserine externalization (PS). Notably, the gold from auranofin enters the cytoplasm of T. gondii, as demonstrated by scanning transmission electron microscopy-energy dispersive X-ray spectroscopy (STEM-EDS) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).IMPORTANCEToxoplasmosis, caused by Toxoplasma gondii, is a devastating disease affecting the brain and the eyes, frequently affecting immunocompromised individuals. Approximately 60 million people in the United States are already infected with T. gondii, representing a population at-risk of developing toxoplasmosis. Recent advances in treating cancer, autoimmune diseases, and organ transplants have contributed to this at-risk populations exponential growth. Paradoxically, treatments for toxoplasmosis have remained the same for more than 60 years, relying on medications well-known for their bone marrow toxicity and allergic reactions. Discovering new therapies is a priority, and repurposing FDA-approved drugs is an alternative approach to speed up drug discovery. Herein, we report the effect of auranofin, an FDA-approved drug, on the biological cycle of T. gondii and how both the phosphine ligand and the gold molecule determine the anti-parasitic activity of auranofin and other gold compounds. Our studies would contribute to the pipeline of candidate anti-T. gondii agents.

Preclinical models are ubiquitous and essential for drug discovery, yet our understanding of how well they translate to clinical outcomes is limited. In this study, we investigate the translational success of treatments for Clostridium difficile infection from animal models to human patients. Our analysis shows that only 36% of the preclinical and clinical experiment pairs result in translation success. Univariate analysis shows that the sustained response endpoint is correlated with translation failure (SRC = -0.20, p-value = 1.53 × 10-54), and explainability analysis of multi-variate random forest models shows that both sustained response endpoint and subject age are negative predictors of translation success. We have developed a recommendation system to help plan the right preclinical study given factors such as drug dosage, bacterial dosage, and preclinical/clinical endpoint. With an accuracy of 0.76 (F1 score of 0.71) and by using only 7 features (out of 68 total), the proposed system boosts translational efficiency by 25%. The method presented can extend to any disease and can serve as a preclinical to clinical translation decision support system to accelerate drug discovery and de-risk clinical outcomes.

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth's microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.

In recent years, advances in biomedicine have revealed an important role for post-transcriptional mechanisms of gene expression regulation in pathologic conditions. In cancer in general and leukaemia specifically, RNA binding proteins have emerged as important regulator of RNA homoeostasis that are often dysregulated in the disease state. Having established the importance of these pathogenetic mechanisms, there have been a number of efforts to target RNA binding proteins using oligonucleotide-based strategies, as well as with small organic molecules. The field is at an exciting inflection point with the convergence of biomedical knowledge, small molecule screening strategies and improved chemical methods for synthesis and construction of sophisticated small molecules. Here, we review the mechanisms of post-transcriptional gene regulation, specifically in leukaemia, current small-molecule based efforts to target RNA binding proteins, and future prospects.

Predicting protein‐ligand binding affinity is a crucial and challenging task in structure‐based drug discovery. With the accumulation of complex structures and binding affinity data, various machine‐learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein‐ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF‐2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended‐connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies. [ABSTRACT FROM AUTHOR]

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti‐schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti‐schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer‐aided methods. To address this issue, we proposed GNN‐DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti‐schistosome (DDAS) small molecules. Initially, a multi‐layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti‐schistosome small molecules. Experimental results showed that GNN‐DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real‐world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN‐DDAS. Overall, GNN‐DDAS provided a promising solution for discovering new and active anti‐schistosome small molecules. [ABSTRACT FROM AUTHOR]

The synthesis of compounds containing partially deuterated groups such as CD2H lacks general methods. These compounds could be important for fine control of metabolic processes in drug discovery, or in the development of multifunctional probes for analysis by complementary spectroscopic techniques. Here, a convenient route to CD2H-methyl ketones is reported through coupling of esters with bis[(pinacolato)boryl]methane and trapping with D2O. [ABSTRACT FROM AUTHOR]

Advancements in fine chemical synthesis and drug discovery continuously demand the development of new and more efficient catalytic systems. In this regard, numerous transition metal-based catalysts have been developed and successfully applied in industrial processes. However, the need for innovative catalyst systems to further enhance the efficiency of chemical transformations and industrial applications persists. Metal nanoclusters (NCs) represent a distinct class of ultra-small nanoparticles (<3 nm) characterized by a precise number of metal atoms coordinated with a defined number of ligands. This structure confers abundant unsaturated active sites and unique electronic and optical properties, setting them apart from conventional nanoparticles or bulk metals. The well-defined structure and monodisperse nature of NCs make them particularly attractive for catalytic applications. Among these, copper-based nanoclusters have emerged as versatile and sustainable catalysts for challenging organic bond-forming reactions. Their unique properties, including natural abundance, accessible oxidation states, diverse ligand architectures, and strong photophysical characteristics, contribute to their growing prominence in this field. In this review, we discuss the photocatalytic activities of Cu-based nanoclusters, focusing on their applications in cross-coupling reactions (C–C and C–N), click reactions, multicomponent couplings, and oxidation reactions. [ABSTRACT FROM AUTHOR]

Tissue chip and organs-on-chip technologies have emerged as promising tools in preclinical studies. In oncology, this is driven by the high failure rates of candidate drugs in clinical trials mainly due to inadequate efficacy or intolerable toxicity and the need for better predictive preclinical models than those traditionally used. However, the intricate design, fabrication, operation, and limited compatibility with automation limit the utility of tissue chips. To tackle these issues, we designed a novel 32-unit tissue chip in the format of standard 96-well plates to streamline automation, fabricated it using 3D printing, and leveraged gravity-driven flow to bypass the need for external flow devices. Each unit includes three interconnected tissue compartments that model liver, tumor, and bone marrow stroma. The focus on liver and bone marrow stroma was due to their respective roles in drug metabolism and disturbances to the bone marrow niche from off-target toxicity of chemotherapies. We analyzed flow patterns, mixing, and oxygen transport among and within the compartments through finite element simulations and demonstrated the utility of the tissue chip to study the efficacy of commonly-used cytotoxic cancer drugs against tumor cells and their toxicity toward liver and bone marrow cells. The ability to simultaneously study drug efficacy and toxicity in high throughput can help select promising therapeutics in early stages of drug discovery in preclinical studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • TZ4C treatments alleviated methamphetamine-induced taupathy in SH-SY5Y cells. • TZ4C improved learning and memory deficits in scopolamine-induced rats. • TZ4C decreased AChE activity in the hippocampus of scopolamine-induced rats. • TZ4C significantly reduced the level of p-Tau in the hippocampus of male Wistar rats. • TZ4C reduced cleaved caspase-3/procaspase-3 protein levels both in vitro and in vivo. Alzheimer's disease (AD) is the most common form of neurodegeneration that results in memory disorders and cognitive impairment. The present study investigated the neuroprotective effects of the synthesized thiazolidine-2,4-dione derivative, (E)-5-(4-chlorobenzylidene)-3-(2-oxo-2-phenylethyl)thiazolidine-2,4-dione (TZ4C), an inhibitor of p-Tau and memory impairment, using a SH-SY5Y cell model of methamphetamine-induced tauopathy and a scopolamine-induced memory impairment model in Wistar rats. In the present study, the neuroprotective effect of TZ4C was studied in a SH-SY5Y cellular model of methamphetamine-induced (2 mM) tauopathy and a scopolamine-induced (1.5 mg/kg/day) memory impairment model in male Wistar rats (n = 48). The memory functions and learning abilities of the rats were evaluated using the Morris water maze (MWM) and passive avoidance tests. Additionally, AChE activity in the rat hippocampus was quantified, and the expression of p-Tau, HSP70, and caspase-3 in both in vitro and in vivo samples was evaluated through Western blot analysis. TZ4C (0.1–1000 µM) did not exhibit significantly toxic effects on SH-SY5Y cell viability. Western blot results indicated that TZ4C led to reduced expression of p-Tau, HSP70, and cleaved caspase-3 in SH-SY5Y cells (3 and 10 µM) and the rat hippocampus (2 and 4 mg/kg). Additionally, the findings suggested that TZ4C enhanced memory function in rats with scopolamine-induced impairment and decreased acetylcholinesterase (AChE) specific activity. The comprehensive analysis of in vitro and in vivo experiments underscores the neuroprotective potential (improved neuropathology and reduced memory impairment) of TZ4C. These findings highlight the promise of TZ4C as a candidate for drug discovery programs to identify effective therapies for AD. [ABSTRACT FROM AUTHOR]

Using embedding methods, compounds with similar properties will be closely located in latent space, and these embedding vectors can be used to find other compounds with similar properties based on the distance between compounds. However, they often require computational resources and programming skills. Here we develop Dr.Emb Appyter, a user‐friendly web‐based chemical compound search platform for drug discovery without any technical barriers. It uses embedding vectors to identify compounds similar to a given query in the embedding space. Dr.Emb Appyter provides various types of embedding methods, such as fingerprinting, SMILES, and transcriptional response‐based methods, and embeds numerous compounds using them. The Faiss‐based search system efficiently finds the closest compounds of query in the library. Additionally, Dr.Emb Appyter offers information on the top compounds; visualizes the results with 3D scatter plots, heatmaps, and UpSet plots; and analyses the results using a drug‐set enrichment analysis. Dr.Emb Appyter is freely available at https://dremb.korea.ac.kr. [ABSTRACT FROM AUTHOR]

Conventional forms of noninvasive cardiovascular imaging that evaluate morphology, function, flow, and metabolism play a vital role in individual treatment decisions, often based on guidelines. Innovations in molecular imaging have enhanced our ability to spatially quantify the expression of a wider array of disease-related proteins, genes, or cell types, or the activity of specific pathogenic pathways. These techniques, which usually rely on design of targeted imaging probes, have already been used extensively in cancer medicine and have now become part of cardiovascular care in conditions such as amyloidosis and sarcoidosis. The recognition that common cardiovascular conditions are caused by a substantial diversity of pathobiologic pathways and the diversity of therapies available for use have rekindled interest in expanding the role of molecular imaging of tissue phenotype to improve precision in diagnosis and therapeutic decision-making. The intent of this article is to raise awareness and understanding of approaches to molecular or cellular imaging of phenotype with targeted probes, and their potential to promote the principles of precision medicine. Also addressed are the diverse roles of molecular imaging to improve precision and efficiency of new drug development at the stages of candidate identification, preclinical testing, and clinical trials. [ABSTRACT FROM AUTHOR]

The different topics and synthetic approaches in an isotope chemistry laboratory of a pharma company are described. Besides the challenges in the synthesis of long-lived isotopes such as3 H or14 C, short-lived isotopes such as68 Ga and stable isotopes such as15 N,13 C or2 H approaches for the isotopic labeling are also demonstrated. Furthermore, method development with emphasis on collaborations with academic groups to tackle the future challenges are discussed. 1 Introduction 2 Isotopic Labeling with Hydrogen Isotopes Deuterium (2 H, D) and Tritium (3 H, T) 2.1 Deuterium Labeling for MS Standards 2.1.1 Labeled Nitrosamines – The Hunt to Quantify Hazardous Impurities 2.1.2 Deuterated Drugs, an Approach To Improve Existing Drugs or To Find Opportunities in Drug Discovery 2.2 Tritium-Labeling Methods – The Fast Approach to Radioactively Labeled Compounds 2.2.1 Hydrogen Isotope Exchange by Iridium Catalysis 2.2.2 Ruthenium-Catalyzed HIE 2.2.3 Nanoparticles as Catalysts in HIE 2.2.4 Photoredox-Catalyzed HIE 2.2.5 HIE via Classical Radical Mechanism 2.2.6 Beyond HIE – Halogen–Tritium Exchange 3 Challenges in14 C-Synthesis Projects 4 Short-Lived Isotopes – The Need for Speed 5 Beyond Isotope Science – Late-Stage Functionalization 5.1 Examples of Late-Stage Functionalization for Peptides 5.2 Examples of Catalyst-Controlled Late-Stage Functionalization 6 Conclusion [ABSTRACT FROM AUTHOR]

Mechanopharmacology is an emerging interdisciplinary field that investigates drug action using biomechanically appropriate in vitro systems to the relevant (patho)physiology. This review outlines emerging technologies and techniques which aim to bridge the gap between mechanical cues influencing cellular biology and conventional pharmacology. We delve into the impact of mechanopharmacology on drug development in cancers and fibrotic diseases. Mechanical cues such as stretch, stiffness, circadian rhythms, fluid flow, intercellular signalling cascades and cytoskeletal structures can modulate drug interactions with molecular targets with implications for drug discovery and development. Models incorporating mechanopharmacological cues to investigate pharmacokinetics, pharmacodynamics and therapeutic outcomes are outlined. Furthermore, this review discusses innovations in the use of biomaterials and microfluidics to further enable the emulation of the mechanical microenvironment. We advocate for the application of mechanopharmacological considerations to improve the physiological relevance of methods used in the drug discovery pipeline. [ABSTRACT FROM AUTHOR]

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2,004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, whereas aspartic acid, methionine, histidine, asparagine, and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models, which outperformed cutting-edge machine learning-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, characterize the chemical space, and discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity. [ABSTRACT FROM AUTHOR]

Human mesenchymal stromal cells (hMSCs) are a naturally adherent cell type and one of the most studied cellular agents used in cell therapy over the last 20 years. Their mechanism of action has been primarily associated with paracrine signaling, which has contributed to an increase in the number of studies focused on hMSC-related extracellular vesicles (EVs). In this study, we demonstrate for the first time that human telomerase reverse transcriptase (hTERT) immortalized hMSCs can be adapted to suspension culture, eliminating the need for microcarriers or other matrixes to support cell growth. This novel cell line, named suspension hMSCs (S-hMSCs), has a doubling time of approximately 55 hours, with a growth rate of 0.423/d. Regarding its immunophenotype characteristics, S-hMSCs retained close to 90% of CD73 and CD105 expression levels, with the CD90 receptor being downregulated during the adherent to suspension adaptation process. An RNA sequencing analysis showed an upregulation of the transcripts coding for CD44, CD46 and CD47 compared to the expression levels in AT-hMSCs and hTERT-hMSCs. The cell line herein established was able to generate EVs using a chemically defined medium formulation with these nanoparticles averaging 150 nm in size and displaying the markers CD63, CD81, and TSG101, while not expressing the negative marker calnexin. This body of evidence, combined with the visual confirmation of EV presence using transmission electron microscopy, demonstrates the EV-producing capabilities of the novel S-hMSCs. This cell line provides a platform for process development, drug discovery and translational studies in the EV field. [ABSTRACT FROM AUTHOR]

Thiazole is a sulfur and nitrogen-containing five-membered heteroaromatic ring in many FDA-approved drugs and numerous experimental leads. To facilitate access to this structure, several novel methodologies have been developed using easily accessible starting materials and more environmentally friendly protocols. This review focuses on all recently developed methods (2013–2024) that utilized homogeneous or heterogeneous catalyzed methods to synthesize 1,3-thiazole and provides thorough information on the diverse therapeutic applicability of thiazole derivatives in drug discovery and development. Furthermore, this review will ameliorate the understanding of synthetic and medicinal chemists in selecting green and ecological methodologies to synthesize thiazoles judicially. [ABSTRACT FROM AUTHOR]

Background: The simplest form of carbohydrate that the human body can utilize efficiently to fuel various functions in our body is glucose. The food that is consumed by us in any form is acted upon by pancreatic juices, bile, and several other digestive enzymes and is converted into its simplest form, which is absorbed by the small intestine and is diffused into the circulatory system; from there, it is transported to other organs via GLUT-2 and GLUT-4 transporters. This process is very important, and so are the hormones involved in it. One such major hormone responsible for glucose homeostasis in the body is insulin. Its malfunction or that of either of its signaling components leads to a metabolic disorder termed "diabetes." Hyperglycemia is a grave condition associated with this complex disease and is of global concern. The impact of this disease can be life threatening if adequate precaution, medication, and therapy are overlooked. A plethora of synthetic drugs are available in the market, and the main goals of these antidiabetic medicines are to improve insulin sensitivity and/or production as well as to lower blood glucose levels. In all cases, frequent use of antidiabetic medication causes several after-effects. Results: Numerous studies in this field demonstrate that antidiabetic phytomolecules are highly effective against diabetes patients and are almost completely without side effects. Almost all classes of phytomolecules have been thoroughly evaluated to develop the noble plant-based drug. Conclusion: The current review discusses diabetes, its classification and management, worldwide occurrence, and the most crucial class of antidiabetic phytomolecules, for example, alkaloids, and terpenenoids as well as the parent molecule responsible for the bioactivity, subclasses, their mechanisms of action, plant sources and part, their superiority over synthetic drug, and new advances in this area. [ABSTRACT FROM AUTHOR]

Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for any in silico drug design workflow. Multiple experimental and computational methods have been developed to better understand these intermolecular interactions. With the availability of a large number of structural datasets, there is a need for developing statistical frameworks that improve upon existing physics-based solutions. Here, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. A technique to generate graphical representations of proteins was developed to exploit the topological and electrostatic properties of the binding region. The developed framework, based on graph neural networks, learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand–target pair. This potential can be coupled with global optimization algorithms such as differential evolution to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design. [ABSTRACT FROM AUTHOR]

Phosphate is essential for numerous biological processes, and serum levels are tightly regulated to accomplish these functions. The regulation of serum phosphate in a narrow physiological range is a well-orchestrated process and involves the gastrointestinal (GI) tract, bone, kidneys, and several hormones, namely, parathyroid hormone, fibroblast growth factor 23 (FGF23), and 1,25-dihydroxyvitamin D (1,25 Vitamin D). Although primarily synthesized in the bone, FGF23, an endocrine FGF, acts on the kidney to regulate phosphate and Vitamin D homeostasis by causing phosphaturia and reduced levels of 1,25 Vitamin D. Recent studies have highlighted the complex regulation of FGF23 including transcriptional and post-translational modification and kidney-bone cross talk. Understanding FGF23 biology has led to the identification of novel therapeutic agents to treat diseases that disrupt phosphate metabolism secondary to FGF23. The focus of this review is to provide an overview of phosphate homeostasis, FGF23 biology, and the role of FGF23 in phosphate balance. [ABSTRACT FROM AUTHOR]

Purpose of Review: This review aims to elucidate the mechanisms employed by Host Defense Peptides (HDPs) in propagating their effector functions, associated bottlenecks complicating their therapeutic applications, and how these peptides can be optimized or modified as potential next-generation lead agents in targeted therapy in managing infectious diseases and cancer. Recent Findings: Emerging studies are affirming that due to dwindling therapeutic options, infectious diseases and cancers undoubtedly remain major threats to public health. This is mostly attributable to the toxicity and resistance of classical drugs in combating these health challenges, thus, novel bio-inspired treatment strategies are being sought. As the first line of defense in all organisms, the innate immune system produces many immune effector molecules including a group of biologically active endogenous peptides collectively known as Host Defense Peptides (HDPs), which can be further sub-classified into Antimicrobial Peptides (AMPs), or Anticancer Peptides (ACPs) based on their effector functions. These highly diverse, ubiquitous, and low-molecular-weight biomolecules are being identified and proposed as models for new drug/lead candidates. This is owing to their unique mechanism of action, sequence, and structural diversities which are integral to their downstream biological functions. Therefore, continuous efforts to elucidate the exploitability of these quintessential peptides in drug discovery research against these pathologies are worth exploring. Summary: The ubiquity, significant diversities, and neoteric novel biological activities of HDPs as one of the effectors of the innate immune system establish them as a unique and valuable therapeutic strategy for combating infectious diseases and cancer. They are especially becoming prominent in overcoming the limitations posed by conventional therapeutic agents in managing these diseases, making them promising candidates for future drug development. Furthermore, their diverse functions and numerous molecular targets will play a crucial role in their discovery as new therapeutics. [ABSTRACT FROM AUTHOR]

Purpose of Review: Snakebite envenomation, classified as a neglected tropical disease, represents a significant global health threat, particularly in Asia and Africa, where it causes between 57,000 and 100,000 deaths annually. Envenomation by cobras, especially those of the genus Naja, contributes substantially to this burden. This review explores the complexities of cobra venom composition, its associated complications, and current treatment strategies, with a focus on venom-derived therapeutic prospects. Recent Findings: Cobra venom is a complex mixture primarily composed of proteins and peptides that exert various toxic effects, including neurotoxicity, tissue damage, and systemic complications. Antivenom therapy remains the primary treatment for envenomation, successfully mitigating critical effects such as bleeding and paralysis. Recent research, however, has revealed that certain venom-derived molecules possess therapeutic potential, opening up new possibilities for drug development. Summary: Despite the lethal nature of cobra envenomation, advances in antivenom therapy and emerging research into the medicinal properties of venom-derived molecules offer hope. This review highlights the dual nature of cobra venom—both as a source of severe pathology and as a potential reservoir for therapeutic agents—fostering the development of novel drugs. [ABSTRACT FROM AUTHOR]

The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre‐trained Transformer (ChatGPT), across various disciplines. We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments. [ABSTRACT FROM AUTHOR]

Accurate classification of gene expression data is crucial for disease diagnosis and drug discovery. However, gene expression data usually has a large number of features, which poses a challenge for accurate classification. In this paper, a novel feature selection method based on minimal redundancy maximal relevance (mRMR) and aquila optimizer is proposed, which introduces the mRMR method in the initialization stage of the population to generate excellent initial populations, effectively improve the quality of the population, and then, the using random opposition-based learning strategy to improve the diversity of aquila population and accelerate the convergence speed of the algorithm, and finally, introducing inertia weight in the position update formula in the late iteration of the aquila optimizer to avoid the algorithm falling into the local optimum and improve the algorithm's capability to find the optimum. In order to verify the effectiveness of the proposed method, ten real gene expression datasets are selected in this paper and compared with several meta-heuristic algorithms. Experimental results show that the proposed method is significantly superior to other meta-heuristic algorithms in terms of fitness value, classification accuracy and the number of selected features. Compared with the original aquila optimizer, the average classification accuracy of the proposed method on KNN and SVM classifiers is improved by 3.48–12.41% and 0.53–18.63% respectively. The proposed method significantly reduces the feature dimension of gene expression data, retains important features, and obtains higher classification accuracy, providing a new method and idea for feature selection of gene expression data. [ABSTRACT FROM AUTHOR]

The protozoan parasite Trichomonas vaginalis (Tv) causes trichomoniasis, the most common non‐viral sexually transmitted infection in the world. Although Tv has been linked to significant health complications, only two closely related 5‐nitroimidazole drugs are approved for its treatment. The emergence of resistance to these drugs and lack of alternative treatment options poses an increasing threat to public health, making development of novel anti‐Trichomonas compounds an urgent need. The proteasome, a critical enzyme complex found in all eukaryotes has three catalytic subunits, β1, β2, and β5 and has been validated as a drug target to treat trichomoniasis. With the goal of developing tools to study the Tv proteasome, we isolated the enzyme complex and identified inhibitors that preferentially inactivate either one or two of the three catalytic subunits. Using a mass spectrometry‐based peptide digestion assay, these inhibitors were used to define the substrate preferences of the β1, β2 and β5 subunits. Subsequently, three model fluorogenic substrates were designed, each specific for one of the catalytic subunits. This novel substrate profiling methodology will allow for individual subunit characterization of other proteasomes of interest. Using the new substrates, we screened a library of 284 peptide epoxyketone inhibitors against Tv and determined the subunits targeted by the most active compounds. The data show that inhibition of the Tv β5 subunit alone is toxic to the parasite. Taken together, the optimized proteasome subunit substrates will be instrumental for understanding the molecular determinants of proteasome specificity and for accelerating drug development against trichomoniasis. [ABSTRACT FROM AUTHOR]

Clinical research is a key factor in healthcare progress, as it contributes toward improving our knowledge on the prevention, etiology, and treatment of different conditions. Healthcare professionals and researchers should be familiar with this specific terminology and procedures of clinical research to understand and be able to evaluate clinical trial results and make decisions using up-to-date recommendations. To do so, they must be familiar with different methodological aspects: from establishing the type of design, the study population, and the groups to be studied, to understanding the randomization and blinding processes. Additionally, when it comes to communicating the results and publishing them, it is also necessary to know how to do it adequately to ensure transparency. This work includes a description of different concepts commonly used in clinical research, particularly in the clinical trials field, in an attempt to compile different topics by providing a brief and accessible overview. [ABSTRACT FROM AUTHOR]

Peptide toxins in animals as part of the chemical arsenal for predation and/or protection that can also safeguard the host from pathogenic infections. Insects including the hymenopterans generate a battery of toxic bactericidal or bacteriostatic molecules which are small multifunctional, linear peptides that cause pain, have antimicrobial effects, and inflammatory processes. The toxins under the experiment are active against gram-positive, gram-negative bacteria and fungi. The present in silico study aims to predict the physicochemical attributes like molecular weight, theoretical pI, amino acid composition, extinction co-efficient, estimated half-life, instability index, aliphatic index and grand average of hydropathy (gravy) of 11 wasps' (class: Insecta; order: Hymenoptera) venom allergens through Expasy Protparam & Pepstat software tools. The secondary structures of the toxins were predicted using psi-Blast-based secondary structure prediction GOR4 tools revealing the % a helix, extended ß strand, random coil and ambiguous state reflecting a comparative picture of physicochemical parameters of these venom allergens. 3D Homology modelling of these toxins was accomplished through Swiss-model webserver tool and validated through various in silico tools like ANOLEA, ProSA-web, QMean4 determining Z score, PROCHEK establishing the 3D models of these toxins. Use of Inter Pro, CDD, PROSITE, P fam, Tox DL, PrDOS software predicted the protein family, protein toxicity, protein disorder respectively. Scratch Protein Predictor software tools predicted cysteine - cysteine bonds. Docking of the eleven (11) wasp venom allergens individually with bacterial cell wall component N-acetylglucosamine was done by CB DOCK webserver resulting negative affinity scores reflecting towards the strong binding between the mentioned 11 toxins and Nacetylglucosamine indicating that the mentioned wasps' toxin molecules might be used as potential antibacterial therapeutic molecules binding to Nacetylglucosamine leading to an avenue to the probable bacterial drug discovery. [ABSTRACT FROM AUTHOR]