Computational Approach Research Articles

Pyridazinone-substituted Benzenesulfonamides Demonstrate Inhibition of Monoamine Oxidase

Background: The monoamine oxidase (MAO) enzymes are important drug targets. Inhibitors of MAO-A and MAO-B have been used to treat the symptoms of depression and Parkinson’s disease. Methods: A series of seventeen pyridazinone-substituted benzenesulfonamides was synthesized and evaluated as potential inhibitors of human MAO-A and MAO-B. This study is a continuation of our interest in the pharmacological activities of sulfonamide compounds. Results: Among the compounds evaluated, only 10 and 18 demonstrated appreciable inhibition of MAOB with IC50 values of 2.90 and 4.36 μM, respectively. None of the benzenesulfonamides inhibited the MAO-A isoform. Potential binding orientations and interactions of 10 and 18 with the active site of MAO-B were investigated by computational approaches. Conclusion: Although these potencies are modest, this study is the first report on MAO inhibition by this class of compounds. Active MAO-B inhibitors may serve as leads for the future discovery of therapeutic agents for neurodegenerative disorders, such as Parkinson’s disease.

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Exploration and Validation of Lead Molecules against Yellow Fever through High Throughput Virtual Screening and Molecular Dynamics Simulation

Background: Yellow fever (YF) is a mosquito-borne flaviviral hemorrhagic fever (VHF) that causes severe hepatitis, renal failure, bleeding, and quick terminal events such as shock and multi-organ failure. There are currently no particular anti-viral medications for the management of the YF virus (YFV). Despite the availability of a commercial YFV vaccination, there are roughly 30,000 fatalities globally each year, with instances rising over the previous 20 years. After being translocated into the endoplasmic reticulum lumen, glycosylated NS1 resides as a membrane-associated dimer, where it is required for viral genome replication. The secreted hexamer NS1 has a role in immune evasion and pathogenesis and has been discovered as a possible diagnostic marker for the early identification of viral infections. Objective: The main aim of this study is to analyze the small molecule as a potent drug candidate against the target NS1 protein. Methods: In this study, Computational approaches, including high throughput virtual screening, molecular docking, and dynamics simulation, were carried out against the target NS1 protein using three different chemical libraries Enamine, Asinex, and NCI. The selected lead compounds were validated through HOMO-LUMO analysis, ADME prediction, and Toxicity parameters to analyze the biological and pharmacological properties of the lead small molecules. Results: From the result, it was concluded that the leads possessed the highest docking scores, interacting with the binding residues, and were stable in the simulation period. Conclusion: Overall findings revealed that the lead three small molecules could act as the potential drug candidate for the target NS1 protein to inhibit the diseasing efficacy of Yellow fever.

Application of Cell Penetrating Peptides for Intracellular Delivery of Endostatin: A Computational Approach.

Endostatin is an antiangiogenic compound with anticancer activity. The poor stability and low half-life of endostatin are the main barriers to the clinical use of this protein. Cell-penetrating peptides (CPPs) are extensively applied as carrier in the delivery of drugs and different therapeutic agents. Therefore, they can be proper candidates to improve endostatin delivery to the target cells. In this study, we aim to computationally predict appropriate CPPs for the delivery of endostatin. Potential appropriate CPPs for protein delivery were selected based on the literature. The main parameters for detection of best CPP-endostatin fusions, including stability, hydrophobicity, antigenicity, and subcellular localization, were predicted using ProtParam, VaxiJen, and DeepLoc-1.0 servers, respectively. The 3D structures of the best CPP-Endostatin fusions were modeled by the I-TASSER server. The predicted models were validated using PROCHECK, ERRAT, Verify3D and ProSA-Web servers. The best models were visualized by the PyMol molecular graphics system. Considering the principal parameters in the selection of best CPPs for endostatin delivery, endostatin fusions with four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, high stability and hydrophobicity, no antigenicity and extracellular localization were predicted as the best potential fusions for endostatin delivery. Four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, were predicted as the best potential candidates to improve endostatin delivery. Application of these CPPs may overcome the limitation of endostatin therapeutic applications, including poor stability and low half-life. Subsequent experimental studies will contribute to verifying these computational results.

Decision tree-based approach to extrapolate life cycle inventory data of manufacturing processes

Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R2) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases.

COVID-19 Pandemic and its Economic Impact on the Global Economy: A Computable General Equilibrium Approach

Introduction: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV- 2, has been a global health crisis since its emergence in late 2019, impacting all aspects of life worldwide. It has revealed vulnerabilities in healthcare systems while also inspiring scientific advancements and cooperation in developing vaccines, highlighting the importance of preparedness and equitable healthcare for future challenges. Method: This paper evaluates the economic impact of the COVID-19 pandemic on the global economy. The study employed the Global Trade Analysis Project model using GTAP database version 10A with 2014 as the base year. The database's 141 regions and 65 sectors are aggregated into 20 regions and 29 sectors. The model used involves a baseline and two policy scenarios. The baseline scenario projected the global economy to 2020 from 2014 using macroeconomic variables, such as real GDP, population, capital stock, and labor supply. Policy scenarios 1 and 2 accounted for the impact of the COVID-19 pandemic and fiscal stimulus package, respectively. Result: The impacts of the pandemic are measured in real GDP, welfare, export and import volumes, trade balance, and output by sector. Simulation results indicate that the real GDP for all regions declined. The impact of COVID-19 on the welfare of countries, including Korea, Japan, the US, Mexico, China, Italy, and Great Britain, varied. In all these countries, the pandemic highlighted existing inequalities and vulnerabilities, affecting countries disproportionately. Government interventions, such as stimulus packages, income support programs, and vaccination campaigns, have aimed to mitigate the impact and support the welfare of the population. The pandemic disrupted global supply chains and increased trade costs, thereby negatively impacting both import and export volumes. The output of some sectors, such as tourism-related sectors, was heavily affected. Conclusion: The COVID-19 pandemic has impacted real GDP, welfare, export and import volumes, trade balance, and output by sector. By examining the specific implications of the pandemic on each of these economic factors, policymakers and researchers can gain valuable insights into the challenges faced and potential avenues for recovery.

Computational investigation of transportation and thermal characteristics in a bi-modal slurry flow through a horizontally placed pipe bend

In this study, we develop a three-dimensional computational model to explore the transportation and thermal characteristics of a bi-modal slurry flowing through a horizontally placed 90° pipe bend. The slurry comprises silica sand (SS) and fly ash (FA) granular, with varying combinations (65:35, 75:25, 85:15, 95:05, and 100:0). The computational investigation has been carried out for different carrier fluid's characteristics, including Prandtl number, and flow velocity (5 m/s), for efflux concentrations of Cw = 40–60% for each mixture. Validated with experimental data, the computational approach employs a two-phase Eulerian-Eulerian RNG k-ε turbulence model and the kinetic theory of granular flow. Results reveal that the 65:35 combination exhibits minimal pressure drop, and the 100:0 combination shows maximum heat transfer characteristics, particularly with low-viscosity fluid (Pr = 2.88) and high particle efflux concentration (Cw = 60%). The study also examines bend loss coefficients (Kt), concentration distribution, convective heat transfer coefficient (h), and Nusselt number (Nu) across different Prandtl fluids and efflux concentrations.

A Computational Investigation on Chitosan Derivatives using Pharmacophore- based Screening, Molecular Docking, and Molecular Dynamics Simulations against Kaposi Sarcoma.

Cancer is one of the most dangerous illnesses to the human body due to its severity and progressive nature. Kaposi's Sarcoma (KS) tumor can appear as painless purple spots on the legs, foot, or face. This cancer develops in the lining of lymph arteries and blood vessels. Along with the enlargement of lymph nodes, the vaginal region and the mouth portion are the additional target areas of KS. DNA-binding proteins known as Sox proteins are found in all mammals and belong to the HMG box superfamily. They controlled a wide range of developmental procedures, such as the formation of the germ layer, the growth of organs, and the selection of the cell type. Human developmental abnormalities and congenital illnesses are frequently caused by the deletion or mutation of the Sox protein. The purpose of this study is to determine the promising Kaposi's sarcoma inhibitors through computational studies. In this present study computational approaches were used to evaluate the anti- carcinogenic efficacy against Kaposi's sarcoma. Ligand-based pharmacophore screening was performed utilising four different chemical libraries (Asinex, Chembridge, Specs, and NCI Natural products (NSC)) depending on the top hypothesis. The top hits were examined using molecular docking, absorption, distribution, metabolism and excretion. Highest occupied molecular orbital and lowest unoccupied molecular orbital were analysed to determine the lead compounds' biological and pharmacological efficacy. The results of the study indicated that the leading candidates were possible SOX protein inhibitors. A pharmacophore model to inhibit the production of SOX protein in Kaposi Sarcoma was generated in this computational experiment using a set of 19 Chitosan compounds. The results revealed that the top hits responded to all of the pharmacological druglikening criteria and had the best interaction residues, fitness scores, and docking scores. The resulting leads might be potential Kaposi's Sarcoma alternative treatments.

Exploring the evolution of machine scheduling through a computational approach

Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling.

Computational modeling of aging-related gene networks: a review

The aging process is a complex and multifaceted phenomenon affecting all living organisms. It involves a gradual deterioration of tissue and cellular function, leading to a higher risk of developing various age-related diseases (ARDs), including cancer, neurodegenerative, and cardiovascular diseases. The gene regulatory networks (GRNs) and their respective niches are crucial in determining the aging rate. Unveiling these GRNs holds promise for developing novel therapies and diagnostic tools to enhance healthspan and longevity. This review examines GRN modeling approaches in aging, encompassing differential equations, Boolean/fuzzy logic decision trees, Bayesian networks, mutual information, and regression clustering. These approaches provide nuanced insights into the intricate gene-protein interactions in aging, unveiling potential therapeutic targets and ARD biomarkers. Nevertheless, outstanding challenges persist, demanding more comprehensive datasets and advanced algorithms to comprehend and predict GRN behavior accurately. Despite these hurdles, identifying GRNs associated with aging bears immense potential and is poised to transform our comprehension of human health and aging. This review aspires to stimulate further research in aging, fostering the innovation of computational approaches for promoting healthspan and longevity.

Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach

Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach

Exploring Efficiency Improvement by Introducing Ge in CsSnI3 Perovskite Solar Cells: Computational Approach

Exploring Efficiency Improvement by Introducing Ge in CsSnI3 Perovskite Solar Cells: Computational Approach

3D Computational Modeling of Defective Early Endosome Distribution in Human iPSC-Based Cardiomyopathy Models

Intracellular cargo delivery via distinct transport routes relies on vesicle carriers. A key trafficking route distributes cargo taken up by clathrin-mediated endocytosis (CME) via early endosomes. The highly dynamic nature of the endosome network presents a challenge for its quantitative analysis, and theoretical modelling approaches can assist in elucidating the organization of the endosome trafficking system. Here, we introduce a new computational modelling approach for assessment of endosome distributions. We employed a model of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with inherited mutations causing dilated cardiomyopathy (DCM). In this model, vesicle distribution is defective due to impaired CME-dependent signaling, resulting in plasma membrane-localized early endosomes. We recapitulated this in iPSC-CMs carrying two different mutations, TPM1-L185F and TnT-R141W (MUT), using 3D confocal imaging as well as super-resolution STED microscopy. We computed scaled distance distributions of EEA1-positive vesicles based on a spherical approximation of the cell. Employing this approach, 3D spherical modelling identified a bi-modal segregation of early endosome populations in MUT iPSC-CMs, compared to WT controls. Moreover, spherical modelling confirmed reversion of the bi-modal vesicle localization in RhoA II-treated MUT iPSC-CMs. This reflects restored, homogeneous distribution of early endosomes within MUT iPSC-CMs following rescue of CME-dependent signaling via RhoA II-dependent RhoA activation. Overall, our approach enables assessment of early endosome distribution in cell-based disease models. This new method may provide further insight into the dynamics of endosome networks in different physiological scenarios.

Identification of Conserved Pathways in Bacillus Strains Known for Plant Growth-Promoting Behavior Using a Multifaceted Computational Approach

Identification of Conserved Pathways in Bacillus Strains Known for Plant Growth-Promoting Behavior Using a Multifaceted Computational Approach

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Impact of Ebola virus nucleoprotein on VP40 virus-like particle production: a computational approach

Ebola virus (EBOV) matrix protein VP40 can assemble and bud as virus-like particles (VLPs) when expressed alone in mammalian cells. Nucleoprotein (NP) could be recruited to VLPs as inclusion body (IB) when co-expressed, and increase VLP production. However, the mechanism behind it remains unclear. Here, we use a computational approach to study NP-VP40 interactions. Our simulations indicate that NP may enhance VLP production through stabilizing VP40 filaments and accelerating the VLP budding step. Further, both the relative timing and amount of NP expression compared to VP40 are important for the effective production of IB-containing VLPs. We predict that relative NP/VP40 expression ratio and time are important for efficient production of IB-containing VLPs. We conclude that disrupting the expression timing and amount of NP and VP40 could provide new avenues to treat EBOV infection. This work provides quantitative insights into EBOV proteins interactions and how virion generation and drug efficacy could be influenced.

In silico identification and targeting of key genes involved in renal cell carcinoma metastasis

Metastasis is a challenge in the management of renal cell carcinoma (RCC). The clinical implications of RCC metastasis are significant, with patients experiencing a poorer response to treatment and a reduced life expectancy. Currently, there is no curative treatment for RCC metastasis, and systemic chemotherapy and immunotherapy remain mainstay therapies. However, emerging evidence suggests that targeted therapies may provide promising treatment options for improving the prognosis and quality of life of patients with metastatic RCC. The current study aimed to investigate the underlying molecular mechanisms of RCC metastasis and identify key therapeutic targets using computational approaches. Also, a library of FDA approved drugs was screened against the target genes to obtain potential inhibitors that can be used in therapies. Differential gene expression analysis followed by functional enrichment and protein-protein interaction analyses led to the identification of CCND2 and MMP9 as key genes involved in multiple process in the metastatic tumors in case of both clear cell and papillary cell renal carcinoma. Docking studies revealed good binding of drugs mysoline and ethisterone with CCND2 and canrenone and evodiamine with MMP9. Molecular Dynamics simulations showed stable and strong binding of these drugs with CCND2, but the identified drugs failed to have stable interactions with MMP9. Hence, our study reveals mysoline and ethisterone to be potential inhibitors that target CCND2 to control metastasis of RCC.

Transforming abandoned petroleum wells in the Nigerian sector of the Chad basin into triplet borehole heat exchangers for sustainable power generation

This study pioneers a transformative approach to sustainable energy recovery, repurposing abandoned petroleum wells in the Nigerian Chad Basin as a triplet-deep closed-loop heat exchanger. We employed a computational numerical approach to designed two deep closed loop systems, which share a common production well, and to evaluate the thermal performance as well as its sustainability over a period of 25 years. The geometrical configuration consists of two injection wells, which converge to the common production one doubling the flow rate at surface and minimising the heat loss during the ascent of the fluid. The availability of detailed abandoned well specifications enabled us to constrain three-dimensional geological and thermal models of the undisturbed underground and, subsequently, to simulate numerically the production temperature and the thermal disturbance in the surrounding rocks. A comprehensive sensitivity analysis optimized the best operational design for total recovered thermal energy and uninterrupted heat recovery. When the deep close loop heat system operates with a fluid circulation rate of 0.03 m³s⁻1 and an injection temperature of 20 °C, it sustains a production temperature of over 100 °C, which generates total recovered thermal energy of 9730 TWth and 817 TWe over 25 years (9000 days). The average annual thermal and electricity productions of 389 TWth and 33 TWe, respectively, are more than the demand of the entire population of Magumeri district. The study's findings offer practical implications for policymakers, industry stakeholders, and local communities, emphasising the potential for socio-economic development while fostering environmental stewardship in the Nigerian Chad Basin.

Development of scoring-assisted generative exploration (SAGE) and its application to dual inhibitor design for acetylcholinesterase and monoamine oxidase B

De novo molecular design is the process of searching chemical space for drug-like molecules with desired properties, and deep learning has been recognized as a promising solution. In this study, I developed an effective computational method called Scoring-Assisted Generative Exploration (SAGE) to enhance chemical diversity and property optimization through virtual synthesis simulation, the generation of bridged bicyclic rings, and multiple scoring models for drug-likeness. In six protein targets, SAGE generated molecules with high scores within reasonable numbers of steps by optimizing target specificity without a constraint and even with multiple constraints such as synthetic accessibility, solubility, and metabolic stability. Furthermore, I suggested a top-ranked molecule with SAGE as dual inhibitors of acetylcholinesterase and monoamine oxidase B through multiple desired property optimization. Therefore, SAGE can generate molecules with desired properties by optimizing multiple properties simultaneously, indicating the importance of de novo design strategies in the future of drug discovery and development.Scientific contribution The scientific contribution of this study lies in the development of the Scoring-Assisted Generative Exploration (SAGE) method, a novel computational approach that significantly enhances de novo molecular design. SAGE uniquely integrates virtual synthesis simulation, the generation of complex bridged bicyclic rings, and multiple scoring models to optimize drug-like properties comprehensively. By efficiently generating molecules that meet a broad spectrum of pharmacological criteria—including target specificity, synthetic accessibility, solubility, and metabolic stability—within a reasonable number of steps, SAGE represents a substantial advancement over traditional methods. Additionally, the application of SAGE to discover dual inhibitors for acetylcholinesterase and monoamine oxidase B not only demonstrates its potential to streamline and enhance the drug development process but also highlights its capacity to create more effective and precisely targeted therapies. This study emphasizes the critical and evolving role of de novo design strategies in reshaping the future of drug discovery and development, providing promising avenues for innovative therapeutic discoveries.

Unplugging lateral fenestrations of NALCN reveals a hidden drug binding site within the pore region

The sodium (Na+) leak channel (NALCN) is a member of the four-domain voltage-gated cation channel family that includes the prototypical voltage-gated sodium and calcium channels (NaVs and CaVs, respectively). Unlike NaVs and CaVs, which have four lateral fenestrations that serve as routes for lipophilic compounds to enter the central cavity to modulate channel function, NALCN has bulky residues (W311, L588, M1145, and Y1436) that block these openings. Structural data suggest that occluded fenestrations underlie the pharmacological resistance of NALCN, but functional evidence is lacking. To test this hypothesis, we unplugged the fenestrations of NALCN by substituting the four aforementioned residues with alanine (AAAA) and compared the effects of NaV, CaV, and NALCN blockers on both wild-type (WT) and AAAA channels. Most compounds behaved in a similar manner on both channels, but phenytoin and 2-aminoethoxydiphenyl borate (2-APB) elicited additional, distinct responses on AAAA channels. Further experiments using single alanine mutants revealed that phenytoin and 2-APB enter the inner cavity through distinct fenestrations, implying structural specificity to their modes of access. Using a combination of computational and functional approaches, we identified amino acid residues critical for 2-APB activity, supporting the existence of drug binding site(s) within the pore region. Intrigued by the activity of 2-APB and its analogues, we tested compounds containing the diphenylmethane/amine moiety on WT channels. We identified clinically used drugs that exhibited diverse activity, thus expanding the pharmacological toolbox for NALCN. While the low potencies of active compounds reiterate the pharmacological resistance of NALCN, our findings lay the foundation for rational drug design to develop NALCN modulators with refined properties.