Time Of Drug Development Research Articles

A APLICAÇÃO DAS FERRAMENTAS IN SILICO NO DESENVOLVIMENTO DE QUIMIOTERÁPICOS

This article explores the use of in silico tools in the development of chemotherapeutics, highlighting their ability to accelerate the discovery and optimization of new cancer treatments. Through techniques such as molecular docking, molecular dynamics, QSAR, and machine learning, researchers can simulate molecular interactions, enabling the identification and refinement of selective kinase inhibitors and other molecules with therapeutic potential. The studies cited demonstrate how these approaches contribute to more effective and personalized treatments, reducing both the costs and the time of drug development. Moreover, the integration of these techniques promises to revolutionize anticancer therapies, moving the field of oncology into an era of precision medicine.

In Silico Screening of FDA‐Approved Anthelmintic Drugs Against LuxR Protein as Quorum‐Sensing Inhibitors and Exploration of Their Binding Mechanism and Electronics Properties

AbstractTreating bacterial infections triggered by multidrug‐resistant bacteria is a global priority. Issue of multidrug resistance can be managed by inhibiting bacterial quorum sensing mechanism. Quorum‐sensing inhibitors (QSIs) are promising alternatives to antibiotics. Herein, a library of FDA‐approved anthelmintic medications was screened using in silico techniques for LuxR protein associated with the quorum‐sensing (QS) mechanism of Staphylococcus aureus. Drug repurposing can reduce the time and financial risk of drug development. Seven FDA‐approved drugs (Fenbendazole, Mebendazole, Flubendazole, Praziquantel, Oxyclozanide, Rafoxanide, and Albendazole) showed strong binding affinity to the LuxR protein with binding free energy less than −6.00 kcal/mol. Molecular dynamic simulation (100 ns) of the docked complexes revealed that Fenbendazole and Rafoxanide remain at the binding pocket of the LuxR protein most of the time. During the whole simulation period, backbone of all seven complexes displayed small fluctuation in the RMSD and Rg. The free energy landscape of LuxR–Rafoxanide and LuxR–Fenbendazole complexes showed promising stability compared to the rest of the complexes. Both the complexes showed excellent MMPBSA binding free energy: −92.882 ± 15.636 kJ/mol (LuxR–Rafoxanide) and −74.873 ± 9.233 kJ/mol (LuxR–Fenbendazole). The reactivity of the ligands was ascertained from their DFT‐based various descriptors.

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional invivo and invitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

Novel drug design and repurposing: An opportunity to improve translational research in cardiovascular diseases?

Drug repurposing is defined as the use of approved therapeutic drugs for indications different from those for which they were originally designed. Repositioning diminishes both the time and cost for drug development by omitting the discovery stage, the analysis of absorption, distribution, metabolism, and excretion routes, as well as the studies of the biochemical and physiological effects of a new compound. Besides, drug repurposing takes advantage of the increased bioinformatics knowledge and availability of big data biology. There are many examples of drugs with repurposed indications evaluated in in vitro studies, and in pharmacological, preclinical, or retrospective clinical analyses. Here, we briefly review some of the experimental strategies and technical advances that may improve translational research in cardiovascular diseases. We also describe exhaustive research from basic science to clinical studies that culminated in the final approval of new drugs and provide examples of successful drug repurposing in the field of cardiology.

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E−05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

Advances in Computational Methods for Drug Design: A Revolution in Pharmaceutical Development

Computational methods of drug design involve the use of various software and algorithms to predict the properties of drug molecules, screen for potential targets, and optimize drug candidates for therapeutic efficacy, it lowering the cost of drug research and development time. The discovery and development of a novel medicine is a lengthy, complex, expensive, and high-risk process that has no commercial counterpart. CADD has previously been used to uncover drugs that have gone through clinical trials and become innovative medicines for many ailments. CADD approaches are widely divided into two categories: structure-based drug design (SBDD) and ligand-based drug design (LBDD). SBDD is used when the three-dimensional structures of target proteins are available, while LBDD design is used in the absence of receptor 3D information and relies on knowledge of molecules that bind to the biological target of interest. Some applications in CADD are Lead Optimization, Virtual screening (VS), ADMET prediction, and toxicity prediction. Medicinal chemists use a variety of computational approaches to modify the chemical structure of a compound to maximize its in vitro activity, drug discovery is driven by the idea that a ligand with higher binding affinity to a target should be more efficacious than that with lower binding affinity to the same target. Target flexibility is one of the key issues that still need to be resolved in drug discovery. The majority of molecular docking tools give the ligand high flexibility, but they fix or give the protein's residues close to or inside the active site only limited flexibility. It is very difficult to provide complete molecular flexibility to the protein as this increases the space and time complexity of the computation. In conclusion, computational methods have revolutionized the field of drug design by enabling faster, more cost-effective, and more efficient drug discovery

FDA Breakthrough Therapy Designation Reduced Late-Stage Drug Development Time.

The Food and Drug Administration's (FDA's) breakthrough therapy designation (BTD) program was created to increase patient access to safe and effective therapies by supporting the efficient clinical development of qualifying, clinically meaningful therapies. Using a new data set of key development milestones for drugs approved between 2006 and 2020, including both BTD drugs and a set of comparator drugs identified by FDA experts, we estimated the BTD program's impact on time spent in late-stage clinical development, measured as the elapsed time between a drug's end-of-Phase-II meeting with regulators and its approval for marketing. Our analysis suggests that the BTD program lowers late-stage clinical development time by 30percent. Our findings provide insight into future regulatory and innovation policies aimed at driving efficiency in medical product development to ensure timely patient access to the most clinically meaningful therapies.

Computational and biological approaches in repurposing ribavirin for lung cancer treatment: Unveiling antitumorigenic strategies

Lung cancer is among leading causes of death worldwide. The five-year survival rate of this disease is extremely low (17.8 %), mainly due to difficult early diagnosis and to the limited efficacy of currently available chemotherapeutics. This underlines the necessity to develop innovative therapies for lung cancer. In this context, drug repurposing represents a viable approach, as it reduces the turnaround time of drug development removing costs associated to safety testing of new molecular entities. Ribavirin, an antiviral molecule used to treat hepatitis C virus infections, is particularly promising as repurposed drug for cancer treatment, having shown therapeutic activity against glioblastoma, acute myeloid leukemia, and nasopharyngeal carcinoma. In the present study, we thoroughly investigated the in vitro anticancer activity of ribavirin against A549 human lung adenocarcinoma cells. From a functional standpoint, ribavirin significantly inhibits cancer hallmarks such as cell proliferation, migration, and colony formation. Mechanistically, ribavirin downregulates the expression of numerous proteins and genes regulating cell migration, proliferation, apoptosis, and cancer angiogenesis. The anticancer potential of ribavirin was further investigated in silico through gene ontology pathway enrichment and protein-protein interaction networks, identifying five putative molecular interactors of ribavirin (Erb-B2 Receptor Tyrosine Kinase 4 (Erb-B4); KRAS; Intercellular Adhesion Molecule 1 (ICAM-1); amphiregulin (AREG); and neuregulin-1 (NRG1)). These interactions were characterized via molecular docking and molecular dynamic simulations. The results of this study highlight the potential of ribavirin as a repurposed chemotherapy against lung cancer, warranting further studies to ascertain the in vivo anticancer activity of this molecule.

Comparing invitro cytotoxic drug sensitivity in colon and pancreatic cancer using 2D and 3D cell models: Contrasting viability and growth inhibition in clinically relevant dose and repeated drug cycles.

In vitro drug screening that is more translatable to the invivo tumor environment can reduce both time and cost of cancer drug development. Here we address some of the shortcomings in screening and show how treatment with 5-fluorouracil (5-FU) in 2D and 3D culture models of colorectal cancer (CRC) and pancreatic ductal adenocarcinomas (PDAC) give different responses regarding growth inhibition. The sensitivity of the cell lines at clinically relevant 5-FU concentrations was monitored over 4 days of treatment in both 2D and 3D cultures for CRC (SW948 and HCT116) and PDAC (Panc-1 and MIA-Pa-Ca-2) cell lines. The 3D cultures were maintained beyond this point to enable a second treatment cycle at Day 14, following the timeline of a standard clinical 5-FU regimen. Evaluation after one cycle did not reveal significant growth inhibition in any of the CRC or PDAC 2D models. By the end of the second cycle of treatment the CRC spheroids reached 50% inhibition at clinically achievable concentrations in the 3D model, but not in the 2D model. The PDAC models were not sensitive to clinical doses even after two cycles. High content viability metrics point to even lower response in the resistant PDAC models. This study reveals the limitations of testing drugs in 2D cancer models and short exposure in 3D models, and the importance of using appropriate growth inhibition analysis. We found that screening with longer exposure and several cycles of treatment in 3D models suggests a more reliable way to assess drug sensitivity.

ADME Studies of Phytochemicals in Stachys pilifera Benth plant species

Medicinal chemistry is a constantly changing and developing field. It is a successful and prosperous branch due to the new technologies, the changing complexities of medicine, and the expansion of medicinal chemistry knowledge. In drug design, many aspects need to be checked. The first step in drug development is discovering when new insights and technologies become available. Discovery is possible to find new treatments for diseases. This can begin with thousands of different computational methods, including under Computer-aided drug design (CADD). It provides a no. of tools and techniques that assist the various stages of drug design which can minimize the cost of drug research and development time. For this particular project, we use the SwissADME web tool it is a free web tool, easily assessable and convenient. It can characterize any chemical compound within a min. it allows you to compute physiochemical descriptors also to predict ADME parameters, pharmacokinetic properties, drug like nature of our compound, and medicinal chemistry friendliness of many small molecules to support drug discovery. For ADME analysis we chose a plant in which the isolation of its chemical compound is already done. i.e. stachys pilifera benth plant species. We did the ADME study of their chemical compounds using SwissADME and concluded its drug-like nature, its bioavailability, its synthetic accessibility, and many medicinal properties. We can say that Swiss ADME is the Asset for medicinal chemistry.

DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.

Practical Recommendations for the Selection of Patients for Individualized Splice-Switching ASO-Based Treatments

Although around 6% of the world’s population is affected by rare diseases, only a small number of disease-modifying therapies are available. In recent years, antisense oligonucleotides (ASOs) have emerged as one option for the development of therapeutics for orphan diseases. In particular, ASOs can be utilized for individualized genetic treatments, addressing patients with a known disease-causing genetic variant, who would otherwise not be able to receive therapy. Careful prioritization of genetic variants amenable to an ASO approach is crucial to increase chances for successful treatments and reduce costs and time for drug development. At present, there is no consensus on how to systematically approach this selection procedure. Here, we present practical guidelines to evaluate disease-causing variants and standardize the process of selecting n-of-1 cases. We focus on variants leading to a loss of function in monogenic disorders and consider which splice-switching ASO-mediated treatments are applicable in each case. To ease the understanding and application of our guidelines, we created a hypothetical transcript covering different pathogenic variants and explained their evaluation in detail. We support our recommendations with real-life examples and add further considerations to be applied to specific cases to provide a comprehensive framework for selecting eligible variants.

DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph

Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN.

The Discovery of the Potential Attractive Compounds of Bactrocera dorsalis (Hendel)

Bactrocera dorsalis (Hendel) (B. dorsalis) is an important agricultural invasive pest that causes significant economic losses in tropical and subtropical fruit and vegetable crops. In this study, the proteins related to the sense of smell and taste of B. dorsalis, such as OBP, PBP, OR, IR, SNMP and CSP, were screened based on B. dorsalis transcriptome data. By integrating the compounds that were reported to be attractive to B. dorsalis, similar compounds of hydrocarbon compounds were obtained. Molecular docking was used to predict the binding between the similar compounds and the OBP, PBP, OR, IR, SNMP and CSP proteins. Network pharmacology was used to screen the potentially attractive compounds, and ecological experiments with B. dorsalis were finally conducted to verify the effect of these potentially attractive compounds on B. dorsalis. The results showed that the G protein-coupled receptor [BR: KO04030] and ion channel [BR: KO04040] pathways were closely related to the odor tropism of B. dorsalis. A total of 84 compounds, such as mitemcinal, exemestane and midecamycin, have potential binding effects on the B. dorsalis odor receptor proteins. The results of the ecological experiments showed that 1 mg/mL and 0.1 mg/mL 19-norandrostenedione, 1 mg/mL progesterone compounds was significantly attractive to B. dorsalis males, while 0.1 mg/mL exemestane was significantly attractive to B. dorsalis females. In this study, network pharmacology technology was used to discover the potential attractive compounds for B. dorsalis, which is important for the development and subsequent prevention and control of B. dorsalis. It can provide a reference in improving the success rates of clinical trials of new pest control products and in reducing the time and cost of drug development.

Exploring the mechanism of contact-dependent cell-cell communication on chemosensitivity based on single-cell high-throughput drug screening platform

High-throughput drug screening (HTDS) has significantly reduced the time and cost of new drug development. Nonetheless, contact-dependent cell-cell communication (CDCCC) may impact the chemosensitivity of tumour cells. There is a pressing need for low-cost single-cell HTDS platforms, alongside a deep comprehension of the mechanisms by which CDCCC affects drug efficacy, to fully unveil the efficacy of anticancer drugs. In this study, we develop a microfluidic chip for single-cell HTDS and evaluate the molecular mechanisms impacted by CDCCC using quantitative mass spectrometry-based proteomics. The chip achieves high-quality drug mixing and single-cell capture, with single-cell drug screening results on the chip showing consistency with those on the 96-well plates under varying concentration gradients. Through quantitative proteomic analysis, we deduce that the absence of CDCCC in single tumour cells can enhance their chemoresistance potential, but simultaneously subject them to stronger proliferation inhibition. Additionally, pathway enrichment analysis suggests that CDCCC could impact several signalling pathways in tumour single cells that regulate vital biological processes such as tumour proliferation, adhesion, and invasion. These results offer valuable insights into the potential connection between CDCCC and the chemosensitivity of tumour cells. This research paves the way for the development of single-cell HTDC platforms and holds the promise of advancing tumour personalized treatment strategies.

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Machine learning-based prediction of chemically modified siRNA (cm-siRNA) efficiency can significantly optimize the design process of siRNA chemical modifications, saving time and cost in siRNA drug development. However, existing in-silico methods suffer from limitations such as small datasets, inadequate data representation capabilities, and lack of interpretability. Therefore, in this study, we developed the Cm-siRPred algorithm based on a multi-view learning strategy. The algorithm employs a multi-view strategy to represent the double-strand sequences, chemical modifications, and physicochemical properties of cm-siRNA. It incorporates a cross-attention model to globally correlate different representation vectors and a two-layer CNN module to learn local correlation features. The algorithm demonstrates exceptional performance in cross-validation experiments, independent dataset, and case studies on approved siRNA drugs, and showcasing its robustness and generalization ability. In addition, we developed a user-friendly webserver that enables efficient prediction of cm-siRNA efficiency and assists in the design of siRNA drug chemical modifications. In summary, Cm-siRPred is a practical tool that offers valuable technical support for siRNA chemical modification and drug efficiency research, while effectively assisting in the development of novel small nucleic acid drugs. Cm-siRPred is freely available at https://cellknowledge.com.cn/sirnapredictor/.

Research on Drug Classification Using Machine Learning Model

In the recent years, more and more fields around the development and application of artificial intelligence have received new improvements and breakthroughs. Among them, drug design and classification are one of the most popular application areas which can provide the greatest help to the society. According to statistics, the number of newly listed drugs in the world is decreasing year by year. At the same time, the risks and costs of drug development are increasing year by year which illustrates a different trend compared with the newly listed drugs. In this case, the application of artificial intelligence technology provides a new idea and opportunity to solve the problem of drug design and classification with higher accuracy than human work. This paper aims to do a research based on using machine learning models to produce suitable outcomes for patients of different drug types in order to reduce the working pressure of doctors in the hospitals.

Targeting ion channels with ultra-large library screening for hit discovery.

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.