MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

Relevance of G protein-coupled receptor (GPCR) dynamics for receptor activation, signalling bias and allosteric modulation.

G protein-coupled receptors (GPCRs) are one of the major drug targets. In recent years, computational drug design for GPCRs has mainly focused on static structures obtained through X-ray crystallography, cryogenic electron microscopy (cryo-EM) or in silico modelling as a starting point for virtual screening campaigns. However, GPCRs are highly flexible entities with the ability to adopt different conformational states that elicit different physiological responses. Including this knowledge in the drug discovery pipeline can help to tailor novel conformation-specific drugs with an improved therapeutic profile. In this review, we outline our current knowledge about GPCR dynamics that is relevant for receptor activation, signalling bias and allosteric modulation. Ultimately, we highlight new technological implementations such as time-resolved X-ray crystallography and cryo-EM as well as computational algorithms that can contribute to a more comprehensive understanding of receptor dynamics and its relevance for GPCR functionality.

Design, in silico Evaluation, and Determination of Antitumor Activity of Potential Inhibitors Against Protein Kinases: Application to BCR-ABL Tyrosine Kinase.

Despite significant progress made over the past two decades in the treatment of chronic myeloid leukemia (CML), there is still an unmet need for effective and safe agents to treat patients with resistance and intolerance to the drugs used in clinic. In this work, we designed 2-arylaminopyrimidine amides of isoxazole-3-carboxylic acid, assessed in silico their inhibitory potential against Bcr-Abl tyrosine kinase, and determined their antitumor activity in K562 (CML), HL-60 (acute promyelocytic leukemia), and HeLa (cervical cancer) cells. Based on the analysis of computational and experimental data, three compounds with the antitumor activity against K562 and HL-60 cells were identified. The lead compound efficiently suppressed the growth of these cells, as evidenced by the low IC50 values of 2.8 ± 0.8 µM (K562) and 3.5 ± 0.2 µM (HL-60). The obtained compounds represent promising basic structures for the design of novel, effective, and safe anticancer drugs able to inhibit the catalytic activity of Bcr-Abl kinase by blocking the ATP-binding site of the enzyme.

A bioactive sprite: Recent advances in the application of vinyl sulfones in drug design and organic synthesis.

Vinyl sulfones, with their exceptional chemical properties, are known as the "chameleons" of organic synthesis and are widely used in the preparation of various sulfur-containing structures. However, their most alluring feature lies in their biological activity. The vinyl sulfone skeleton is ubiquitous in natural products and drug molecules and boasts a unique molecular structure and drug activity when compared to conventional drug molecules. As a result, vinyl sulfones have been extensively studied, playing a critical role in organic synthesis and pharmaceutical chemistry. In this review, we present a comprehensive analysis of the recent applications of vinyl sulfone structures in drug design, biology, and chemical synthesis. Furthermore, we explore the prospects of vinyl sulfones in diverse fields, offering insight into their potential future applications.

PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models.

SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.Scientific contributionThis novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn't require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.

Shedding Light on Dark Chemical Matter: The Discovery of a SARS-CoV-2 Mpro Main Protease Inhibitor through Intensive Virtual Screening and In Vitro Evaluation.

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 µM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

A critical assessment of bioactive compounds databases.

Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

[Box: see text].

Efficient molecular conformation generation with quantum-inspired algorithm.

CONTEXT: Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature. METHODS: The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics.

Intracellular tau fibrils are sources of neurotoxicity and oxidative stress in Alzheimer's. Current drug discovery efforts have focused on molecules with tau fibril disaggregation and antioxidation functions. However, recent studies suggest that membrane-bound tau-containing oligomers (mTCOs), smaller and less ordered than tau fibrils, are neurotoxic in the early stage of Alzheimer's. Whether tau fibril-targeting molecules are effective against mTCOs is unknown. The binding of epigallocatechin-3-gallate (EGCG), CNS-11, and BHT-CNS-11 to in silico mTCOs and experimental tau fibrils was investigated using machine learning-enhanced docking and molecular dynamics simulations. EGCG and CNS-11 have tau fibril disaggregation functions, while the proposed BHT-CNS-11 has potential tau fibril disaggregation and antioxidation functions like EGCG. Our results suggest that the three molecules studied may also bind to mTCOs. The predicted binding probability of EGCG to mTCOs increases with the protein aggregate size. In contrast, the predicted probability of CNS-11 and BHT-CNS-11 binding to the dimeric mTCOs is higher than binding to the tetrameric mTCOs for the homo tau but not for the hetero tau-amylin oligomers. Our results also support the idea that anionic lipids may promote the binding of molecules to mTCOs. We conclude that tau fibril-disaggregating and antioxidating molecules may bind to mTCOs, and that mTCOs may also be useful targets for Alzheimer's drug design.

Advancing drug discovery with deep attention neural networks.

In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug-target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.

Design of Inhibitors That Target the Menin-Mixed-Lineage Leukemia Interaction.

The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of -11.0, -10.7, -10.6, -10.2, -10.2, -9.9, -9.9, -9.9, -9.9, and -9.9 kcal/mol, respectively. To confirm the stability of the menin-ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

Pharmaceutical Studies on Piperazine-Based Compounds Targeting Serotonin Receptors and Serotonin Reuptake Transporters.

Depression is a debilitating mental illness that has a significant impact on an individual's psychological, social, and physical life. Multiple factors, such as genetic factors and abnormalities in neurotransmitter levels, contribute to the development of depression. Monoamine oxidase inhibitors, tricyclic antidepressants, serotonin reuptake inhibitors (SSRIs), serotonin-noradrenaline reuptake inhibitors, and atypical and new-generation antidepressants are well-known drug classes. SSRIs are the commonly prescribed antidepressant medications in the clinic. Genetic variations impacting serotonergic activity in people can influence susceptibility to diseases and response to antidepressant therapy. Gene polymorphisms related to 5-hydroxytryptamine (5-HT) signaling and subtypes of 5-HT receptors may play a role in the development of depression and the response to antidepressants. SSRIs binding to 5-HT reuptake transporters help relieve depression symptoms. Research has been conducted to identify a biomarker for detecting depressive disorders to identify new treatment targets and maybe offer novel therapy approaches. The pharmacological potentials of the piperazine-based compounds led researchers to design new piperazine derivatives and to examine their pharmacological activities. Structure-activity relationships indicated that the first aspect is the flexibility in the molecules, where a linker of typically a 2-4 carbon chain joins two aromatic sides, one of which is attached to a piperazine/phenylpiperazine/benzyl piperazine moiety. Newly investigated compounds having a piperazine core show a superior antidepressant effect compared to SSRIs in vitro/in vivo.

An update on lipophilic efficiency as an important metric in drug design.

INTRODUCTION: Lipophilic efficiency (LipE) and lipophilic metabolic efficiency (LipMetE) are valuable tools that can be utilized as part of a multiparameter optimization process to advance a hit to a clinical quality compound. AREAS COVERED: This review covers recent, effective use cases of LipE and LipMetE that have been published in the literature over the past 5 years. These use cases resulted in the delivery of high-quality molecules that were brought forward to in vivo work and/or to clinical studies. The authors discuss best-practices for using LipE and LipMetE analysis, combined with lipophilicity-focused compound design strategies, to increase the speed and effectiveness of the hit to clinical quality compound optimization process. EXPERT OPINION: It has become well established that increasing LipE and LipMetE within a series of analogs facilitates the improvement of broad selectivity, clearance, solubility, and permeability and, through this optimization, also facilitates the achievement of desired pharmacokinetic properties, efficacy, and tolerability. Within this article, we discuss lipophilic efficiency-focused optimization as a tool to yield high-quality potential clinical candidates. It is suggested that LipE/LipMetE-focused optimization can facilitate and accelerate the drug-discovery process.

Revolutionizing Playing with Skeleton Atoms: Molecular Editing Surgery in Medicinal Chemistry.

Finding the most perfect drug candidates in the fields of drug discovery and medicinal chemistry will remain the main interest of drug designers. This concern necessitates organic and medicinal chemists, in most examples, to precisely design and search for drug candidates that are very analogous to the present effective drugs for solving, mainly, their proven critical pharmacological and clinical issues through slightly changing one or two atoms of the principal functional skeletons of the molecules of present therapeutics by atom swapping, removal, and/or addition procedures in organic chemical synthesis. This accurate modern chemicosimilarity tactic in drug discovery surely saves time while keeping us very close, or sometimes highly superior, to the parent pharmacophoric bioactivity (i.e., keeping considerable analogy to the parent therapeutic molecule). From this perspective and logic, the science of skeletal editing of molecules (i.e., skeletal molecular editing) arose in the era of artificial intelligence (AI) and its dramatic predictions. As a pioneer in this modern branch in pharmaceutical and therapeutic organic chemistry, in this up-to-date minireview and perspective article, an attempt was made to introduce skeletal editing and its synthetic surgeries (over molecules) to the audience (including irrelevant readers) in a simpler and more attractive way as a novel chemical technology, highlighting the previous synthetic trials (in general), demonstrating the three main techniques, and, finally, discussing the future therapeutic needs and scenarios from a medicinal chemist's viewpoint.

Hydrophobic tagging of small molecules: an overview of the literature and future outlook.

INTRODUCTION: Hydrophobic tagging (HyT) technology presents a distinct therapeutic strategy diverging from conventional small molecule drugs, providing an innovative approach to drug design. This review aims to provide an overview of the HyT literature and future outlook to offer guidance for drug design. AREAS COVERED: In this review, the authors introduce the composition, mechanisms and advantages of HyT technology, as well as summarize the detailed applications of HyT technology in anti-cancer, neurodegenerative diseases (NDs), autoimmune disorders, cardiovascular diseases (CVDs), and other fields. Furthermore, this review discusses key aspects of the future development of HyT molecules. EXPERT OPINION: HyT emerges as a highly promising targeted protein degradation (TPD) strategy, following the successful development of proteolysis targeting chimeras (PROTAC) and molecular glue. Based on exploring new avenues, modification of the HyT molecule itself potentially enhances the technology. Improved synthetic pathways and emphasis on pharmacokinetic (PK) properties will facilitate the development of HyT. Furthermore, elucidating the biochemical basis by which the compound's hydrophobic moiety recruits the protein homeostasis network will enable the development of more precise assays that can guide the optimization of the linker and hydrophobic moiety.

In Silico Approaches to Polyherbal Synergy: Protocol for a Scoping Review.

BACKGROUND: According to the World Health Organization, more than 80% of the world's population relies on traditional medicine. Traditional medicine is typically based on the use of single herbal drugs or polyherbal formulations (PHFs) to manage diseases. However, the probable mode of action of these formulations is not well studied or documented. Over the past few decades, computational methods have been used to study the molecular mechanism of phytochemicals in single herbal drugs. However, the in silico methods applied to study PHFs remain unclear. OBJECTIVE: The aim of this protocol is to develop a search strategy for a scoping review to map the in silico approaches applied in understanding the activity of PHFs used as traditional medicines worldwide. METHODS: The scoping review will be conducted based on the methodology developed by Arksey and O'Malley and the recommendations of the Joanna Briggs Institute (JBI). A set of predetermined keywords will be used to identify the relevant studies from five databases: PubMed, Embase, Science Direct, Web of Science, and Google Scholar. Two independent reviewers will conduct the search to yield a list of relevant studies based on the inclusion and exclusion criteria. Mendeley version 1.19.8 will be used to remove duplicate citations, and title and abstract screening will be performed with Rayyan software. The JBI System for the Unified Management, Assessment, and Review of Information tool will be used for data extraction. The scoping review will be reported based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. RESULTS: Based on the core areas of the scoping review, a 3-step search strategy was developed. The initial search produced 3865 studies. After applying filters, 875 studies were short-listed for further review. Keywords were further refined to yield more relevant studies on the topic. CONCLUSIONS: The findings are expected to determine the extent of the knowledge gap in the applications of computational methods in PHFs for any traditional medicine across the world. The study can provide answers to open research questions related to the phytochemical identification of PHFs, criteria for target identification, strategies applied for in silico studies, software used, and challenges in adopting in silico methods for understanding the mechanisms of action of PHFs. This study can thus provide a better understanding of the application and types of in silico methods for investigating PHFs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/56646.

Combinatorial Biosynthesis Creates a Novel Aglycone Polyether with High Potency and Low Side Effects Against Bladder Cancer.

Polyethers play a crucial role in the development of anticancer drugs. To enhance the anticancer efficacy and reduce the toxicity of these compounds, thereby advancing their application in cancer treatment, herein, guided by the structure-activity relationships of aglycone polyethers, novel aglycone polyethers are rationally redesigned with potentially improved efficacy and reduced toxicity against tumors. To realize the biosynthesis of the novel aglycone polyethers, the gene clusters and the post-polyketide synthase tailoring pathways for aglycone polyethers endusamycin and lenoremycin are identified and subjected to combinatorial biosynthesis studies, resulting in the creation of a novel aglycone polyether termed End-16, which demonstrates significant potential for treating bladder cancer (BLCA). End-16 demonstrates the ability to suppress the proliferation, migration, invasion, and cellular protrusions formation of BLCA cells, as well as induce cell cycle arrest in the G1 phase in vitro. Notably, End-16 exhibits superior inhibitory activity and fewer side effects against BLCA compared to the frontline anti-BLCA drug cisplatin in vivo, thereby warranting further preclinical studies. This study highlights the significant potential of integrating combinatorial biosynthesis strategies with rational design to create unnatural products with enhanced pharmacological properties.

Structure-activity relationship of pharmacophores and toxicophores: the need for clinical strategy.

OBJECTIVES: Sometimes clinical efficacy and potential risk of therapeutic and toxic agents are difficult to predict over a long period of time. Hence there is need for literature search with a view to assessing cause of toxicity and less efficacy of drugs used in clinical practice. METHOD: Hence literatures were searched for physicochemical properties, chemical formulas, molecular masses, pH values, ionization, receptor type, agonist and antagonist, therapeutic, toxic and structure-activity relationship of chemical compounds with pharmacophore and toxicophore, with a view to identifying high efficacious and relative low toxic agents. Inclusion criteria were manuscripts published on PubMed, Scopus, Web of Science, PubMed Central, Google Scholar among others, between 1960 and 2023. Keywords such as pharmacophore, toxicophore, structure-activity-relationship and disease where also searched. The exclusion criteria were the chemicals that lack pharmacophore, toxicophore and manuscripts published before 1960. RESULTS: Findings have shown that pharmacophore and toxicophore functional groups determine clinical efficacy and safety of therapeutics, but if they overlap therapeutic and toxicity effects go concurrently. Hence the functional groups, dose, co-administration and concentration of drugs at receptor, drug-receptor binding and duration of receptor binding are the determining factors of pharmacophore and toxicophore activity. Molecular mass, chemical configuration, pH value, receptor affinity and binding capacity, multiple pharmacophores, hydrophilic/lipophilic nature of the chemical contribute greatly to functionality of pharmacophore and toxicophore. CONCLUSION: Daily single therapy, avoidance of reversible pharmacology, drugs with covalent adduct, maintenance of therapeutic dose, and the use of multiple pharmacophores for terminal diseases will minimize toxicity and improve efficacy.

Oropouche virus - The "Newest" invisible public enemy?

This perspective underscores the rising challenge posed by emerging diseases against the backdrop of modern advancements in global public health understanding. It particularly highlights the emergence of the Oropouche virus (OROV) as a significant global threat, detailing its transmission dynamics, symptoms, and epidemiological impact, with a focus on its historical and current manifestations. It further delves into the molecular aspects of OROV, elucidating its unique characteristics, lack of structural similarity with other arboviruses, and the limited progress in medicinal chemistry research. Still, it highlights notable studies on potential antiviral agents and the challenges in drug development, emphasizing the need for innovative approaches such as structure-based drug design (SBDD) and drug repurposing. Finally, it concludes with a call to action, urging increased attention and research focus on OROV to prevent potential future pandemics fueled by viral mutations.