Screening Design Research Articles

RAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering.

Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, in silico design of diverse antibody libraries, screening and identification of potential candidates for in vitro validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.

Modeling Study on Heat Capacity, Viscosity, and Density of Ionic Liquid–Organic Solvent–Organic Solvent Ternary Mixtures via Machine Learning

Physicochemical properties of ionic liquids (ILs) are essential in solvent screening and process design. However, due to their vast diversity, acquiring IL properties through experimentation alone is both time-consuming and costly. For this reason, the creation of prediction models that can accurately forecast the characteristics of IL and its mixtures is crucial to their application. This study proposes a model for predicting the three important parameters of the IL-organic solvent–organic solvent ternary system: density, viscosity, and heat capacity. The model incorporates group contribution (GC) and machine learning (ML) methods. A link between variables such as temperature, pressure, and molecular structure is established by the model. We gathered 2775 viscosity, 6515 density, and 1057 heat capacity data points to compare the prediction accuracy of three machine learning methods, namely, artificial neural networks (ANNs), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). As can be observed from the findings, the ANN model produced the best results out of the three GC-based ML methods, even though all three produced dependable predictions. For heat capacity, the mean absolute error (MAE) of the ANN model is 1.7320 and the squared correlation coefficient (R2) is 0.9929. Regarding viscosity, the MAE of the ANN model is 0.0225 and the R2 is 0.9973. For density, the MAE of the ANN model is 7.3760 and the R2 is 0.9943. The Shapley additive explanatory (SHAP) approach was applied to the study to comprehend the significance of each feature in the prediction findings. The analysis results indicated that the R-CH3 group of the ILs, followed by the imidazolium (Im) group, had the highest impact on the heat capacity property of the ternary system. On the other hand, the Im group and the R-H group of ILs had the most effects on viscosity. In terms of density, the Im group of the ILs had the greatest effect on the ternary system, followed by the molar fraction of the organic solvent.

Eco-friendly purification process of chitin contained in shrimp shells by application of the Definitive Screening Design experiment plan

Eco-friendly purification process of chitin contained in shrimp shells by application of the Definitive Screening Design experiment plan

Pharmacological Strategies for Enzyme Inhibition in Disease Therapeutics: A Comprehensive Review

Abstract: Enzyme inhibition is a crucial pharmacological approach for treating various diseases as it targets enzymes involved in disease pathogenesis. This review explores the fundamental concepts of enzyme inhibition, including reversible and irreversible mechanisms, and the various types of enzymes, such as proteases, kinases, and polymerases, and their contributions to different disease states. The review discusses the design and production of enzyme inhibitors using methods like structurebased drug design, high-throughput screening, and rational drug design. The review also discusses the challenges and successes encountered in discovering and optimizing potent and selective enzyme inhibitors. Examples of enzyme inhibition's therapeutic benefits include protease inhibitors in HIV/AIDS therapy, kinase inhibitors in cancer treatment, and acetylcholinesterase inhibitors in Alzheimer's disease management. The review also examines possible side effects and limits of enzyme inhibition, focusing on ways to reduce off-target effects and make drugs more specific. At the end of the review, new trends and future possibilities in enzyme inhibition for treating diseases are talked about. These include personalized medicine, combination therapies, and new ways to get drugs into the body. By shedding light on the latest developments, challenges, and future directions, the review aims to contribute to the advancement of this vital field and revolutionize disease treatment modalities.

Olaparib Process Development Employing Quality by Design (QbD) Principles.

This study focuses on multivariate experimental design and statistical analysis to optimize the process of Olaparib 1. Quality by design (QbD) methodology was adopted for optimization of the Olaparib process consisting of three reaction steps: (1) amidation, (2) deprotection, and (3) acylation. Every chemical conversion was studied in isolation, employing risk assessment to identify key material attributes and key process parameters that may have the potential to impact the reaction. Thereafter, the screening design of experiment (DoE) was employed to scrutinize the factors that significantly impacted yield. Moving forward, the scrutinized factors which were found to impact the responses, the set of critical material attributes (CMAs) and critical process parameters (CPPs), were considered for optimization by applying I-Optimal design to define design space arriving at a robust setting wherein the predefined targets were supposedly optimal. To our delight, we got 95, 91, and 75% yield with more than 99% purity in amidation, deprotection, and acetylation, respectively, which enabled us to systematically identify design space to meet the desired quality target of the product consistently. More importantly, to distinguish the CMAs and CPPs, these elements ought to be monitored to have control of the quality parameter throughout the active pharmaceutical ingredient (API) value chain until commercial manufacturing followed by marketing. Eventually, we have developed a greener process in comparison to precedented one for Olaparib 1.

LITERATURE REVIEW: SENSITIVITAS OKSIMETRI NADI DALAM MENGENALI GEJALA PENYAKIT JANTUNG BAWAAN KRITIS

Early detection of Critical Congenital Heart Disease (CCHD) in newborns is crucial to the success of treatment. This underlies the need for pulse oximetry screening as the basis for establishing the diagnosis of CCHD that is not recognized on physical examination. This study aimed to analyze the accuracy of pulse oximetry as a CCHD screening method in newborns. The research method was literature review, data is obtained from the PubMed database with the keywords Critical Congenital Heart Disease (CCHD), Newborn, Screening. The inclusion criteria are that the article is published in 2018-2023, discussing the accuracy of pulse oximetry screening, speaking English, and observational analysis research design. Articles according to the criteria were obtained 80 articles. After going through the selection process, 15 articles were selected. An analysis of 15 articles concluded that pulse oximetry has a high accuracy in assisting in the establishment of the diagnosis of CCHD, indicating the need to include it in routine screening procedures for newborns. However, it must be carried out according to regular procedures, by competent health workers, equipped with adequate facilities and it is necessary to educate prospective parents about CCHD screening during pregnancy to anticipate refusal to undergo treatment if abnormalities are found in the results of pulse oximetry.

Investigation of the Electrokinetic Potential of Granules and Optimization of the Pelletization Method Using the Quality by Design Approach.

The preparation of pellets using a high-shear granulator in a rapid single-step is considered a good economic alternative to the extrusion spheronization process. As process parameters and material attributes greatly affect pellet qualities, successful process optimization plays a vital role in producing pellet dosage forms with the required critical quality attributes. This study was aimed at the development and optimization of the pelletization technique with the Pro-CepT granulator. According to the quality by design (QbD) and screening design results, chopper speed, the volume of the granulating liquid, binder amount, and impeller speed were selected as the highest risk variables for a two-level full factorial design and central composite design, which were applied to the formula of microcrystalline cellulose, mannitol, and with a binding aqueous polyvinylpyrrolidone solution. The design space was estimated based on physical response results, including the total yield of the required size, hardness, and aspect ratio. The optimized point was tested with two different types of active ingredients. Amlodipine and hydrochlorothiazide were selected as model drugs and were loaded into an optimized formulation. The kinetics of the release of the active agent was examined and found that the results show a correlation with the electrokinetic potential because amlodipine besylate can be adsorbed on the surface of the MCC, while hydrochlorothiazide less so; therefore, in this case, the release of the active agent increases. The research results revealed no significant differences between plain and model drug pellets, except for hydrochlorothiazide yield percent, in addition to acceptable content uniformity and dissolution enhancement.

Discovery of JNJ-74856665: A Novel Isoquinolinone DHODH Inhibitor for the Treatment of AML.

Acute myelogenous leukemia (AML), a heterogeneous disease of the blood and bone marrow, is characterized by the inability of myeloblasts to differentiate into mature cell types. Dihydroorotate dehydrogenase (DHODH) is an enzyme well-known in the pyrimidine biosynthesis pathway and preclinical findings demonstrated that DHODH is a metabolic vulnerability in AML as inhibitors can induce differentiation across multiple AML subtypes. As a result of virtual screening and structure-based drug design approaches, a novel series of isoquinolinone DHODH inhibitors was identified. Further lead optimization afforded JNJ-74856665 as an orally bioavailable, potent, and selective DHODH inhibitor with favorable physicochemical properties selected for clinical development in patients with AML and myelodysplastic syndromes (MDS).

Epitope screening and self-assembled nanovaccine molecule design of PDCoV-S protein based on immunoinformatics.

Based on the whole virus or spike protein of pigs, δ coronavirus (PDCoV) as an immunogen may have unrelated antigenic epitope interference. Therefore, it is essential for screening and identifying advantageous protective antigen epitopes. In addition, immunoinformatic tools are described as an important aid in determining protective antigenic epitopes. In this study, the primary, secondary, and tertiary structures of vaccines were measured using ExPASy, PSIPRED 4.0, and trRosetta servers. Meanwhile, the molecular docking analysis and vector of the candidate nanovaccine were constructed. The immune response of the candidate vaccine was simulated and predicted using the C-ImmSim server. This experiment screened B cell epitopes with strong immunogenicity and high conservation, CTL epitopes, and Th epitopes with IFN-γ and IL-4 positive spike proteins. Ferritin is used as a self-assembled nanoparticle element for designing candidate nanovaccine. After analysis, it has been found to be soluble, stable, non-allergenic, and has a high affinity for its target receptor, TLR-3. The preliminary simulation analysis results show that the candidate nanovaccine has the ability to induce a humoral and cellular immune response. Therefore, it may provide a new theoretical basis for research on coronavirus self-assembled nanovaccines. It may be an effective candidate vaccine for controlling and preventing PDCoV.

QbD-Based Development and Evaluation of Pazopanib Hydrochloride Extrudates Prepared by Hot-Melt Extrusion Technique: In Vitro and In Vivo Evaluation.

Pazopanib hydrochloride (PZB) is a protein kinase inhibitor approved by the United States Food and Drug Administration and European agencies for the treatment of renal cell carcinoma and other renal malignancies. However, it exhibits poor aqueous solubility and inconsistent oral drug absorption. In this regard, the current research work entails the development and evaluation of the extrudates of pazopanib hydrochloride by the hot-melt extrusion (HME) technique for solubility enhancement and augmenting oral bioavailability. Solid dispersion of the drug was prepared using polymers such as Kollidon VA64, hydroxypropylmethylcellulose (HPMC), Eudragit EPO, and Affinisol 15LV in a 1:2 ratio by the HME process through a lab-scale 18 mm extruder. Systematic optimization of the formulation variables was carried out with the help of custom screening design (JMP Software by SAS, Version 14.0) to study the impact of polymer type and plasticizer level on the quality of extrudate processability by measuring the torque value, appearance, and disintegration time as the responses. The polymer blends containing Kollidon VA64 and Affinisol 15LV resulted in respective clear transparent extrudates, while Eudragit EPO and HPMC extrudates were found to be opaque white and brownish, respectively. Furthermore, evaluation of the impact of process parameters such as screw rpm and barrel temperature was measured using a definitive screening design on the extrude appearance, torque, disintegration time, and dissolution profile. Based on the statistical outcomes, it can be concluded that barrel temperature has a significant impact on torque, disintegration time, and dissolution at 30 min, while screw speed has an insignificant impact on the response variables. Affinisol extrudates showed less moisture uptake and faster dissolution in comparison to Kollidon VA64 extrudates. Affinisol extrudates were evaluated for polymorphic stability up to a 3-month accelerated condition and found no recrystallization. PZB-Extrudates using the Affinisol polymer (Test formulation A) revealed significantly higher bioavailability (AUC) in comparison to the free Pazopanib drug and marketed formulation.

A biomarker-directed, multi-center phase II/III study of ctDNA molecular response adaptive immuno-chemotherapy in patients with non-small cell lung cancer.

TPS8669 Background: Liquid biopsy analyses of circulating cell-free tumor DNA (ctDNA) have shown promise as an early endpoint of immunotherapy response, allowing patients with primary resistance to be rapidly and accurately identified. BR.36 is a ctDNA-directed, phase II/III trial of molecular response-adaptive immuno-chemotherapy; in the first of two independent stages, the BR.36 trial met its primary endpoint, with a sensitivity of ctDNA response for radiographic RECIST response of 82% and a specificity of 75%, while demonstrating a median time to ctDNA response of 2.1 months. Additionally, patients with ctDNA molecular response attained longer progression-free (PFS) and overall survival (OS). Methods: The second stage of BR.36 is a multi-center, biomarker-directed, phase II/III clinical trial of ctDNA molecular response adaptive immuno-chemotherapy in patients with immunotherapy and chemotherapy-naïve metastatic NSCLC. The main objective is to evaluate if adding chemotherapy to pembrolizumab for patients who have persistent ctDNA on liquid biopsy drawn after 6 weeks of treatment will result in better PFS and OS compared to patients who remain on pembrolizumab until clinical progression. Key eligibility criteria include: age ≥18 years, ECOG performance status 0-2, metastatic NSCLC, EGFR and ALK mutation negative and PD-L1 Tumor Proportion Score (TPS) ≥ 50%, at least and not more than 2 cycles of the 200 mg or 2 mg/kg IV Q3W of pembrolizumab as first line systemic immunotherapy at the time of screening and RECIST non-PD or clinically stable PD documented prior to enrolment that can continue on immunotherapy. The phase II primary endpoint is PFS and has secondary endpoints of feasibility, overall response rate and safety/tolerability. Sex, RECIST response and ECOG status represent stratification criteria. With 110 randomized patients evaluable for progression (55 patients per arm and 71 PFS events observed in this phase), we would be able to detect a hazard ratio difference of 0.67 with a 1-sided alpha of 0.2 and power of 0.80 using a phase II screening design. The trial will not stop accrual for the phase II analysis of PFS if feasibility endpoints are achieved. In the phase III portion, a total of 210 randomized patients recruited over 3 years and followed for an additional 24 months are required to detect an OS hazard ratio difference of 0.67 with 1-sided alpha of 0.05 and power of 0.8. The total number of events for the final analysis is 156, and assuming 10% of patients are lost to follow-up, we are targeting 230 patients to be included overall. The primary endpoint of the phase III portion is OS, with secondary endpoints of best overall response, response duration, PFS and safety/tolerability. Exploratory endpoints include longitudinal ctDNA analyses. The BR.36 clinical trial is open to enrollment (ClinicalTrials.gov ID: NCT04093167). Clinical trial information: NCT04093167 .

Green infant formula analysis: Optimizing headspace solid-phase microextraction of carbonyl compounds associated with lipid peroxidation using GC-MS and pentafluorophenylhydrazine derivatization

The refinement and optimization of a method combining headspace solid-phase microextraction (HS-SPME) with gas chromatography-mass spectrometry (GC-MS) was successfully performed for the first time to determine seven carbonyl and dicarbonyl compounds, including glyoxal, methylglyoxal, dimethylglyoxal, and malondialdehyde in infant formulae, related to lipid peroxidation. HS-SPME was utilized for simultaneous extraction and derivatization with pentafluorophenylhydrazine (PFPH). Critical parameters such as temperature, pH, extractive phase, and salting-out were meticulously investigated and fine-tuned by an asymmetrical 2232//9 screening design to ensure the method's efficacy and reliability. Optimal conditions included a PFPH concentration of 5 g/L, pH 5.0, head-space extraction at 60 °C within 10 min, utilizing a DVB/CAR/PDMS coating, and a 20% w/w salting-out. The analytical validation of this method, compliant with FDA guidelines, demonstrated exceptional linearity, sensitivity, specificity, precision (RSD ≤13.8%), and accuracy (84.8% ≤ recovery ≤111.5%). The metric approach AGREEprep confirms its eco-friendliness, marking a significant step towards an environmentally conscious approach in infant formula analysis. An occurrence study conducted on 25 infant formula samples revealed widespread carbonyl and dicarbonyl compounds in both powdered and liquid variants. ANOVA results exhibited variations in compound concentrations among different sample groups. Clustering analyses delineated distinct groups based on carbonyl content, indicating the potential of these compounds as markers for lipid peroxidation and food quality assessment. This method serves as a valuable tool for evaluating infant formula quality, stability towards oxidation, and safety.

Screening and identification of microbes from polluted environment for azodye (Turquoise blue) decolorization

Turquoise blue dye is frequently used for industrial dyeing applications. But the release of untreated colored wastewater became an environmental and public health hazard. Microbial remediation of Azodye is environmentally safe and an alternative to a physicochemical approach. The aim of this research is to isolate and characterize turquoise blue dye degrading microbes from polluted environment. Microbial isolation and purification from soil and effluent sample was done on PDA and NA. Turquoise blue dye degrading test was investigated under optimized conditions using -the definitive screening design method. UV–Vis spectrophotometer used to measure the degradation percentage at 620 nm and 25 °C. The results revealed that 24 fungi and 6 bacterial species were identified from the contaminated site using Biolog Microstation and MALDI-TOF. Among all identified microbial species Pencilium citrinum Thom BCA &Penicillium heriquei show the highest percentage decolorization of turquoise blue dye up to 300 ppm with 90 % removal at pH4 and 87 % at pH 7 up to 400 ppm respectively. The azodye degradation ability of these fungi species used in the development of mycoremediation technologies provide an alternative option for Azodye removal after HPLC analysis, molecular characterization, and toxic analysis.

Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)). The kinetically validated random forest models are deployed to locate optimal conditions that maximize C2 yield for each of the catalysts. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions in addition to the reaction conditions, while the predictions of methane conversion are largely governed by the reaction conditions. The machine learning (ML) regressor is used as a kinetic surrogate to find a locus of optimal reaction conditions that maximize both selectivity-conversion for each of the catalysts via a multi-objective optimization routine. The maximum C2 yields for catalysts are projected to be improved by 15% on average. Analyzing the catalysts with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, using the optimal locus eliminates variability in the process conditions to reveal distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and reaction conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts. The framework has the potential to lend itself to materials acceleration platforms where it is crucial to consider multi-scale phenomena that impact downstream KPIs.

Electrochemical advanced oxidation of glucosinolates derived from rapeseed meal: Parameter optimization, degradation mechanism and toxicity assessment

Glucosinolates (GLS) are a group of antinutritional compounds present in rapeseed meal that limit its use as feed ingredients. Considering their potentially hazardous effects on both the aquatic environment and animal health, it is necessary to develop green and efficient degradation options for GLS. In the current work, the electrochemical advanced oxidation technology with a boron-doped diamond (BDD) electrode was initially employed for the degradation of GLS. To facilitate parameter optimization, a sequential strategy comprising a definitive screening design and a central composite rotatable design was implemented. Particular attention was paid to the differing roles of five electrolyte salts (NaCl, NaNO3, Na2SO4, NaHCO3 and Na3PO4) during the electrolytic oxidations. The optimal conditions for GLS degradation were found to be a flow rate of 400 mL min−1, a applied current density of 7.75 mA cm−2 and a 2.0 mM NaNO3 medium, yielding mineralization of 82.2 % at 120 min of electrolysis. By combining the results of computational simulation and LC/MS analysis, four possible degradation pathways of allyl GLS at the BDD anode were proposed. And the ecotoxicity of each detected degradation product was determined using the ECOSAR program. Further biological toxicity assay revealed that crude GLS electrolysis reduced the toxicity on model organism Caenorhabditis elegans, reducing its death percentage by 26.80 % and its lipofuscin level by 37.59 % at 48 h. The results showed great promise of BDD technology in engineering applications as a powerful option for the treatment of GLS derived from rapeseed meal.

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization.

Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO's ability to discover refined solutions, reducing average surface roughness to 4.63 μm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

Determining input factor importance in a compartmental model using screening methods

Models are used in a wide variety of scientific disciplines to mathematically and simply represent real phenomenon, to understand and explain observed behaviors, and to make predictions of future behaviors. A compartmental model is a type of model that is used to evaluate an experimental investigation concerning transport of material in a system of the real world. Sensitivity analysis (SA) of models is required to investigate the effects of changes in the inputs on the model output(s). When dealing with large, complex, computationally expensive models, screening methods are essential to reduce the dimensionality of the input space for a subsequent SA which can help to determine and quantify the change in model behavior as model factors change. In this study, our focus is on the application of screening designs to a 25-compartment global carbon cycle model, which help us identify the most important input factors that control most of the output uncertainty. We explore similarities and dissimilarities in the results from these screening methods.

A graphical comparison of screening designs using support recovery probabilities

A screening experiment attempts to identify a subset of important effects using a relatively small number of experimental runs. Given the limited run size and a large number of possible effects, penalized regression is a popular tool used to analyze screening designs. In particular, an automated implementation of the Gauss-Dantzig selector has been widely recommended to compare screening design construction methods. Here, we illustrate potential reproducibility issues that arise when comparing two-level screening designs via simulation, and recommend a graphical method, based on screening probabilities, which compares designs by evaluating them along the penalized regression solution path. This method can be implemented using simulation, or, in the case of lasso, by using exact local lasso sign recovery probabilities. Our approach circumvents the need to specify tuning parameters associated with regularization methods, leading to more reliable design comparisons. This article contains Supplementary Materials including code to implement the proposed methods.

Accelerating the Screening of Modified MA2Z4 Catalysts for Hydrogen Evolution Reaction by Deep Learning‐Based Local Geometric Analysis

Machine learning (ML) integrated with density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of single‐atom catalysts (SACs) by establishing deep structure–activity relationships. The traditional ML models are always difficult to identify the structural differences among the single‐atom systems with different modification methods, leading to the limitation of the potential application range. Aiming to the structural properties of several typical two‐dimensional MA2Z4‐based single‐atom systems (bare MA2Z4 and metal single‐atom doped/supported MA2Z4), an improved crystal graph convolutional neural network (CGCNN) classification model was employed, instead of the traditional machine learning regression model, to address the challenge of incompatibility in the studied systems. The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer, surface layer, and bulk layer (ASB‐GCNN). Through ML and DFT calculations, five potential single‐atom hydrogen evolution reaction (HER) catalysts were screened from chemical space of 600 MA2Z4‐based materials, especially V1/HfSn2N4(S) with high stability and activity (ΔGH* is 0.06 eV). Further projected density of states (pDOS) analysis in combination with the wave function analysis of the SAC‐H bond revealed that the SAC‐dz2 orbital coincided with the H‐s orbital around the energy level of −2.50 eV, and orbital analysis confirmed the formation of σ bonds. This study provides an efficient multistep screening design framework of metal single‐atom catalyst for HER systems with similar two‐dimensional supports but different geometric configurations.

Retraction Note: Optimized design and application research of smart interactive screen for wireless networks based on federated learning

Retraction Note: Optimized design and application research of smart interactive screen for wireless networks based on federated learning