Candidate Designs Research Articles

A Viroinformatics Study: B-Cell Polytope Mapping of Envelope Protein to Develop Vaccine Candidate against Four DENV Serotype

Nowadays, dengue virus (DENV) is still become a global problem, even though the virus infection issues have reached half of the population in some countries each year. DENV belongs to the enveloped virus with positive-sense single-stranded RNA (+ssRNA) genus Flavivirus and belongs to the Flaviviridae family. DENV has structural proteins which consist of the envelope protein (E), capsid (C), and membrane (M). There are four serotypes of this virus which are DENV-1, 2, 3, and 4. These four serotypes are transmitted to humans through Aedes sp. The development of this vaccine is still in progress and the challenge of this DENV vaccine candidate design is to overcome the heterotypic infection and the expansion of coverage protection to all virus serotypes. This research uses design simulation for vaccine candidates using B cell epitope in all DENV’s serotypes envelope to trigger the antibody formation through bioinformatics method that consists of protein modeling, immunogenicity, toxicity, and immune stimulation. DENV envelope protein was predicted to have polytope that can be recognized by B cells and act as an antigen, have low similarity with the composing sequence of cell surface receptors on the body, and non-toxic, and then can trigger the population increase of B cell and IgM antibody production with high avidity to neutralize four of the DENV serotypes. We recommend the B cell polytype which consists of A, C, E, and G peptides be examined by the wet-lab approach.

Latent Crossover for Data-Driven Multifidelity Topology Design

Abstract Topology optimization is one of the most flexible structural optimization methodologies. However, in exchange for its high level of design freedom, typical topology optimization cannot avoid multimodality, where multiple local optima exist. This study focuses on developing a gradient-free topology optimization framework to avoid being trapped in undesirable local optima. Its core is a data-driven multifidelity topology design (MFTD) method, in which the design candidates generated by solving low-fidelity topology optimization problems are updated through a deep generative model and high-fidelity evaluation. As its key component, the deep generative model compresses the original data into a low-dimensional manifold, i.e., the latent space, and randomly arranges new design candidates over the space. Although the original framework is gradient free, its randomness may lead to convergence variability and premature convergence. Inspired by a popular crossover operation of evolutionary algorithms (EAs), this study merges the data-driven MFTD framework and proposes a new crossover operation called latent crossover. We apply the proposed method to a maximum stress minimization problem in 2D structural mechanics. The results demonstrate that the latent crossover improves convergence stability compared to the original data-driven MFTD method. Furthermore, the optimized designs exhibit performance comparable to or better than that in conventional gradient-based topology optimization using the P-norm measure.

Abstract 4824: Multi-level intervention to increase minority cancer patient enrollment to clinical treatment trials- Study design considerations and baseline characteristics from the ACTWONDER2S Study

Abstract Introduction: Black/African American (AA) and Hispanic cancer patients are underrepresented in cancer clinical trials (CCTs). Disparities stem from multiple factors, thus, comprehensive, multi-level interventions (MLI) are needed. Presented here is a conceptual framework for selecting the appropriate study design for MLIs using ACTWONDER2S, a multi-level intervention (MLI) aimed to increase Black/AA and Hispanic patient participation into CCTs, as an illustrative example. Baseline characteristics of the ACTWONDER2S target populations are also provided. Methods: ACTWONDER2S integrates community health educator and digital tools into an MLI targeting community residents and physicians in the Moffitt Cancer Center (MCC) catchment area, as well as MCC patients, physicians and CCT coordinators. A literature review of the relative strengths and weakness of 5 candidate designs for MLIs was conducted, and a cluster stratified randomized design was selected. Geospatial analytics were used to identify clusters of census tracts (“priority zones”) with high Black/AA and Hispanic populations for intervention deployment. Baseline characteristics of the priority zones were described using US Census data and other sources. Results: 14 priority zones were identified, which were matched on population characteristics and randomized into intervention (n=7) and control (n=7) zones. There were no statistically significant differences between the intervention and control priority zones in total population size (paired t-test p-value=0.63), proportions of Black/AA (p=0.133) and Hispanic populations p=0.17), and distance (in miles) from MCC (p=0.64). Approximately 35.8% and 16.5% of the intervention priority zones were Hispanic or Black/AA, respectively. Average distance to MCC, cancer cases, referral and CCT enrollment rates were also similar between groups. Conclusion: Decision frameworks for MLI study design selection are lacking. The framework provided here for ACTWONDER2S can be applied to other studies seeking to evaluate MLIs. Furthermore, the randomization of the priority zones produced optimal target populations for testing the efficacy of the MIL within ACTWONDER2S. Citation Format: Dana E. Rollison, Rossybelle P. Amorrortu, Lindsay N. Fuzzell, Melany A. Garcia, Elliott S. Tapia-Kwan, Yayi Zhao, Steven A. Eschrich, Bob R. Gore, Brian S. Mittman, Nathanael B. Stanley, Susan T. Vadaparampil. Multi-level intervention to increase minority cancer patient enrollment to clinical treatment trials- Study design considerations and baseline characteristics from the ACTWONDER2S Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4824.

Latent Diffusion Models for Structural Component Design

Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling high-quality image generation tailored to user needs. This paper proposes a framework for the generative design of structural components. Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions. One of the distinct advantages our approach offers over other generative approaches is the editing of existing designs. We train our model using a dataset of geometries obtained from structural topology optimization utilizing the SIMP algorithm. Consequently, our framework generates inherently near-optimal designs. Our work presents quantitative results that support the structural performance of the generated designs and the variability in potential candidate designs. Furthermore, we provide evidence of the scalability of our framework by operating over voxel domains with resolutions varying from 323 to 1283. Our framework can be used as a starting point for generating novel near-optimal designs similar to topology-optimized designs.

Design and Synthesis of Novel 6,7‐Dihydrobenzo[d]isoxazol‐4(5H)‐one Derivatives Bearing 1,2,3‐Triazole Moiety as Potential Hsp90 Inhibitors and their Evaluation as Antiproliferative Agents

AbstractAn effective approach to 5‐triazolyl‐substituted 6,7‐dihydrobenzo[d]isoxazol‐4(5H)‐ones and 4,5,6,7‐tetrahydrobenzo[d]isoxazoles was developed. The approach included α‐keto bromination of 6,7‐dihydrobenzo[d]isoxazol‐4(5H)‐one followed by nucleophilic substitution of bromine with the azide group. For the preparation of 5‐azido‐6,7‐dihydrobenzo[d]isoxazol‐4(5H)‐one, the carbonyl group at position 4 was reduced to a methylene group under ionic hydrogenation conditions using triethylsilane as a reducing agent. Cu(I)‐catalyzed [2+3] cycloaddition of terminal alkynes to both obtained azides was used for the synthesis of two series of triazolyl derivatives. Compounds, which contain in their structures common for some Hsp90 inhibitors 2,4‐dihydroxy‐5‐isopropylphenyl fragment, were evaluated as antiproliferative agents against two breast cancer cell lines: hormone‐dependent MCF7 and HER2‐positive HCC1954. The lead compound showed half‐maximal inhibitory concentration (IC50) values below 5 μM and induced significant changes in the Hsp90 signaling pathways in HER2‐positive HCC1954 cells. It increased the expression of Hsp70 (used as a pharmacodynamic marker of Hsp90 inhibition) and inhibited the expression of HER2, p‐c‐Met, c‐Met, and p‐AKT. The combination of the selected isoxazole‐triazole hybrid molecule and the earlier described apoptosis inducer LCTA‐3344 demonstrated a significant antiproliferative effect against HCC1954 cells. The lead compound was revealed to be a promising candidate for future anticancer drug design, particularly against aggressive breast cancer positive for HER2.

Multi-Fidelity Design of Porous Microstructures for Thermofluidic Applications

Abstract As modern electronic devices are increasingly miniaturized and integrated, their performance relies more heavily on effective thermal management. In this regard, two-phase cooling methods which capitalize on thin-film evaporation atop structured porous surfaces are emerging as potential solutions. In such porous structures, the optimum heat dissipation capacity relies on two competing objectives that depend on mass and heat transfer. Optimizing these objectives for effective thermal management is challenging due to the simulation costs and the high dimensionality of the design space which is often a voxelated microstructure representation that must also be manufacturable. We address these challenges by developing a data-driven framework for designing optimal porous microstructures for cooling applications. In our framework, we leverage spectral density functions to encode the design space via a handful of interpretable variables and, in turn, efficiently search it. We develop physics-based formulas to simulate the thermofluidic properties and assess the feasibility of candidate designs based on offline image-based analyses. To decrease the reliance on expensive simulations, we generate multi-fidelity data and build emulators to find Pareto-optimal designs. We apply our approach to a canonical problem on evaporator wick design and obtain fin-like topologies in the optimal microstructures which are also characteristics often observed in industrial applications.

Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines

We present Limousine, a self-designing key-value storage engine, that can automatically morph to the near-optimal storage engine architecture shape given a workload, a cloud budget, and target performance. At its core, Limousine identifies the fundamental design principles of storage engines as combinations of learned and classical data structures that collaborate through algorithms for data storage and access. By unifying these principles over diverse hardware and three major cloud providers (AWS, GCP, and Azure), Limousine creates a massive design space of quindecillion (1048) storage engine designs the vast majority of which do not exist in literature or industry. Limousine contains a distribution-aware IO model to accurately evaluate any candidate design. Using these models, Limousine searches within the exhaustive design space to construct a navigable continuum of designs connected along a Pareto frontier of cloud cost and performance. If storage engines contain learned components, Limousine also introduces efficient lazy write algorithms to optimize the holistic read-write performance. Once the near-optimal design is decided for the given context, Limousine automatically materializes the corresponding design in Rust code. Using the YCSB benchmark, we demonstrate that storage engines automatically designed and generated by Limousine scale better by up to 3 orders of magnitude when compared with state-of-the-art industry-leading engines such as RocksDB, WiredTiger, FASTER, and Cosine, over diverse workloads, data sets, and cloud budgets.

An analytical model of oscillating heat pipe performance

Concentrated heat dissipation poses formidable challenges across various domains, such as electronics, power generation, and aerospace applications. Oscillating Heat Pipes (OHPs), also known as Pulsating Heat Pipes (PHPs), have emerged as promising solutions due to their exceptional heat transfer capabilities and adaptability. Although OHPs have received significant research attention, their widespread adoption in industry has been hindered by the absence of reliable and cost-effective design tools. These tools are challenging to develop due to the complex nature of OHP operation, the multitude of design variables to consider, and the substantial computational resources required for computational modeling.In this study, previous OHP models based on first principles and spring–mass–damper analogies are combined with novel methods to create an analytical model that captures a much wider range of OHP phenomena than previous analytical models. The model predicts OHP heat transfer performance orders of magnitude faster than computational techniques and for a wider range of design parameters than experimental correlations. Furthermore, this model provides critical insights into the underlying principles governing OHP operation, including a theory for the onset and stabilization of circulation.The analytical model was compared to 185 experimental data in the literature and predicted the OHP temperature drop with a mean error of 16.6%. Additionally, the temperature drop was predicted within 30% for 84.5% of the data analyzed. The model’s analytical nature facilitates rapid solutions, enabling evaluations across a large design space that could be used to identify a small number of candidate designs for further experimental or computational evaluation.

Enhancing Steel Wheel Ventilation Efficiency Through Multi-Objective Optimization

This study focuses on the optimization of ventilation hole design in steel wheels used for heavy commercial vehicles. The primary objective is to reduce the weight of the wheel while ensuring compliance with radial fatigue and cornering fatigue test requirements. Four distinct ventilation types were parametrized using ANSYS Mechanical, with the von Mises stress on the disk, number of ventilations, and wheel weight serving as design parameters. Stress analysis and weight comparisons were performed between wheels featuring different ventilation types and an ellipse ventilation wheel. Incorporating the design of experiment (DoE) and response surface optimization (RSO) module in ANSYS Workbench 2022 R1 was employed to compare and evaluate the obtained values. Subsequently, the multi-objective genetic algorithm (MOGA-II) method was employed for optimization, aiming to identify the optimal design. The optimization process, utilizing a maximum of 20 iterations, a convergence stability percentage of 2%, and a maximum allowable Pareto percentage of 70%, yielded 1, 3, 3, and 3 candidate design points for round, slot, trapezoid, and halfmoon-type ventilation holes, respectively. Among the various ventilation types considered, the halfmoon-type ventilation hole exhibited the most promising results. Compared to the current design, the optimized wheel achieved a weight reduction of 0.9 kg (2.05%). This outcome demonstrates the effectiveness of the proposed methodology. Although lighter designs were not attainable while maintaining the same stress values for the other three ventilation types, the halfmoon-type ventilation hole was ultimately selected as the preferred design.

All-Perovskite Multicomponent Nanocrystal Superlattices.

Nanocrystal superlattices (NC SLs) have long been sought as promising metamaterials, with nanoscale-engineered properties arising from collective and synergistic effects among the constituent building blocks. Lead halide perovskite (LHP) NCs come across as outstanding candidates for SL design, as they demonstrate collective light emission, known as superfluorescence, in single- and multicomponent SLs. Thus far, LHP NCs have only been assembled in single-component SLs or coassembled with dielectric NC building blocks acting solely as spacers between luminescent NCs. Here, we report the formation of multicomponent LHP NC-only SLs, i.e., using only CsPbBr3 NCs of different sizes as building blocks. The structural diversity of the obtained SLs encompasses the ABO6, ABO3, and NaCl structure types, all of which contain orientationally and positionally locked NCs. For the selected model system, the ABO6-type SL, we observed efficient NC coupling and Förster-like energy transfer from strongly confined 5.3 nm CsPbBr3 NCs to weakly confined 17.6 nm CsPbBr3 NCs, along with characteristic superfluorescence features at cryogenic temperatures. Spatiotemporal exciton dynamics measurements reveal that binary SLs exhibit enhanced exciton diffusivity compared to single-component NC assemblies across the entire temperature range (from 5 to 298 K). The observed coherent and incoherent NC coupling and controllable excitonic transport within the solid NC SLs hold promise for applications in quantum optoelectronic devices.

Understanding collaboration in sub-structured teams through a computational model of set-based concurrent engineering

Coordinating the collaboration within sub-structured teams created for large engineering projects presents several challenges. Set-based concurrent engineering (SBCE), by maintaining several candidate designs for each subproblem throughout the process, may alter the best ways to coordinate subteams in these projects, compared to point-based concurrent engineering (PBCE) processes in which only one candidate design is maintained for each subproblem. Previous studies have introduced SBCE to industry with positive results and have corroborated its stated benefits through computational study, but researchers have yet to systematically study how SBCE interacts with sub-structured teams. This work augments a multi-agent model developed to simulate and study SBCE, the Point/Set-Organized Research Teams (PSORT) platform, to model sub-structured teams with different forms of intra-subteam collaboration, including joint iteration, design sharing, or no collaboration at all. Design projects are then simulated using these collaboration methods with PBCE and SBCE conditions to investigate how SBCE affects team performance under different modes of intra-subteam collaboration. These studies find that SBCE enables nominal subteams to combine the efforts of multiple designers on individual subproblems with minimal losses, while high levels of collaboration during development are found to assist designers in overcoming design barriers, which SBCE facilitates by maintaining many design options.

Janus molybdenum di-chalcogenides based van der Waals bilayers for supercapacitor electrode design- effects of interlayer stacking orientations on quantum capacitance

In this work, for the first time, the quantum capacitance of an artificial two-dimensional (2D) material system based on van der Waals (vdW) bilayer heterostructures of Janus Molybdenum Di-chalcogenides, MoXY (X≠Y, X/YS, Se, Te) have been extensively investigated for electrode design of electrical double-layer (EDL) supercapacitor. The effects of different interlayer stacking orientations on local charge distribution, interlayer charge transfer, energy band structure, and density of states (DOS) have been comprehensively analyzed and correlated with the excess charge density and quantum capacitance variation with local electrode potential. Furthermore, the total capacitance reduction with respect to EDL capacitance has been analyzed over a wide range of EDL capacitance, and the performance of vdW Janus bilayers has been systematically benchmarked against the natural monolayers/bilayers and Janus monolayers of Molybdenum Di-chalcogenides. The results demonstrate that the large interlayer charge transfer induced built-in interlayer electric field significantly reduces the energy band gap of vdW Janus bilayers for S–Te and Se–Te interlayer stacking, thus leading to a significantly superior quantum capacitance. Finally, the favorable valley-dependent effective mass and band-degeneracy in MoSTe/MoSeTe have greatly enhanced quantum capacitance in S–Te and Se–Te stacking, rendering these bilayers a highly attractive candidate for supercapacitor electrode design.

Impact of ageing on homologous and human-coronavirus-reactive antibodies after SARS-CoV-2 vaccination or infection

The endemic human coronaviruses (HCoVs) circulate worldwide yet remain understudied and unmitigated. The observation of elevated levels of HCoV reactive antibodies in COVID-19 patients highlights the urgent necessity of better understanding of HCoV specific immunity. Here, we characterized in-depth the de novo SARS-CoV-2 specific antibody responses and the boosting of HCoV-reactive antibodies after SARS-CoV-2 vaccination or infection in individuals up to 98 years old. All the vaccinees were home-dwelling with no documented SARS-CoV-2 infection before receiving the COVID-19 mRNA vaccine (BNT162b2). The first two vaccine doses elicited potent SARS-CoV-2 spike binding antibodies in individuals up to 80 years. The third dose largely boosted the previously low S2 domain binding and neutralizing antibodies in elderly 80–90 years old, but less so in those above 90 years. The endemic betacoronavirus (HKU1 and OC43) reactive antibodies were boosted in all vaccinees, although to a lesser extent in those above 80 years old. COVID-19 patients had potent elevation of alpha- and betacoronavirus (229E, NL63, HKU1 and OC43) reactive antibodies. In both patients and vaccinees, S2 domain specific antibody increases correlated with SARS-CoV-2 neutralizing and HCoV-reactive antibody responses in all ages, indicating S2 domain as a candidate for future universal coronavirus vaccine design.

Collaborative Learning of Ph.D. Candidates in Design on Emerging Scenarios in Scientific Publication

The article describes a collaborative learning process aimed at introducing early stage researchers in design to a non-hegemonic approach to design knowledge production and publications. Four co-creation sessions has been held in the context of Prode project, engaging phd students in design to take in account emerging issues in scientific publishing. In specific an innovative Living publication lifecycle has been introduced (supporting an iterative model for publications) and the relative Living publications scenario (including Augmented publications; Collective authoring; Evolving publications; Publications reuse) has been used to discuss and disrupt traditional publication patterns and envision beyond the “article” format, new typologies of (not only textual) research products and new forms of dissemination, to meet the challenges of an impactful design education. The context of change of scientific publication and hypothesis underlying the paper have been discussed elsewhere (Lupo, 2022; Lupo, 2023). For the purpose of this article, the focus is the training of early stage researchers, in order to make them aware of the potentiality of plurality and diversity of design knowledge publication. The collaborative approach and tools adopted eased the empowering of the PhD students through brainstorms, role plays and hands-on tests.

Pre-Endcapping of Hyperbranched Polymers toward Intrinsically Stretchable Semiconductors with Good Ductility and Carrier Mobility.

The advancement of semiconducting polymers stands as a pivotal milestone in the quest to realize wearable electronics. Nonetheless, endowing semiconductor polymers with stretchability without compromising their carrier mobility remains a formidable challenge. This study proposes a "pre-endcapping" strategy for synthesizing hyperbranched semiconducting polymers (HBSPs), aiming to achieve the balance between carrier mobility and stretchability for organic electronics. The findings unveil that the aggregates formed by the endcapped hyperbranched network structure not only ensure efficient charge transport but also demonstrate superior tensile resistance. In comparison to linear conjugated polymers, HBSPs exhibit substantially larger crack onset strains and notably diminished tensile moduli. It is evident that the HBSPs surpass their linear counterparts in terms of both their semiconducting and mechanical properties. Among HBSPs, HBSP-72h-2.5 stands out as the preeminent candidate within the field of inherently stretchable semiconducting polymers, maintaining 93% of its initial mobility even when subjected to 100% strain (1.41±0.206cm2V-1s-1). Furthermore, thin film devices of HBSP-72h-2.5 remain stable after undergoing repeated stretching and releasing cycles. Notably, the mobilities are independent of the stretching directions, showing isotropic charge transport behavior. The preliminary study makes this "pre-endcapping" strategy a potential candidate for the future design of organic materials for flexible electronic devices.

High Field Confinement Enhancement in Silicon-based Photonic Crystal with Different Point Defects

Two-dimensional silicon-based photonic crystals as optical analogs to electronic semiconductors are presented in this paper, emphasizing the design and analysis of two-dimensional structures. Employing the Lumerical FDTD solver, we initially crafted defect-free photonic crystals within a hexagonal lattice of air holes on a silicon slab (6.57 μm along x, 6.57 μm along y, 0.75 μm along z). The lattice parameters (a = 0.365 μm, r = 0.1095 μm, with r = 0.3a) established a target resonant wavelength of 1.5 μm. Utilizing Finite Difference Time-Domain (FDTD), we evaluated electric field decay, resonant modes, and the Q factor. The defect-free structure exhibited a peak Q factor of 736.004 at a resonant wavelength of 1262.87 nm. Removal of a central hole enhanced the Q factor to 1286.96 at 1391.07 nm. Introducing various line defects, the structure with three holes removed from vertical positions emerged as the optimal candidate for silicon-based photonic crystal cavity resonators, displaying the highest Q factor near the target resonant wavelength. Additionally, the structure with seven holes removed showed stable linear decays at both resonant wavelengths, suggesting its potential as a cavity resonator design candidate.

Structural and thermodynamic properties of conserved water molecules in Mpro native: A combined approach by MD simulation and Grid Inhomogeneous Solvation Theory.

The new viral strains of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are continuously rising, becoming more virulent, and transmissible. Therefore, the development of new antiviral drugs is essential. Due to its significant role in the viral life cycle of SARS-CoV-2, the main protease (Mpro) enzyme is a leading target for antiviral drug design. The Mpro monomer consists of domain DI, DII, and DI-DII interface. Twenty-one conserved water molecules (W4-W24) are occupied at these domains according to multiple crystal structure analyses. The crystal and MD structures reveal the presence of eight conserved water sites in domain DI, DII and remaining in the DI-DII interface. Grid-based inhomogeneous fluid solvation theory (GIST) was employed on MD structures of Mpro native to predict structural and thermodynamic properties of each conserved water site for focusing to identify the specific conserved water molecules that can easily be displaced by proposed ligands. Finally, MD water W13 is emerged as a promising candidate for water mimic drug design due to its low mean interaction energy, loose binding character with the protein, and its involvement in a water-mediated H-bond with catalytic His41 via the interaction Thr25(OG)---W13---W---His41(NE2). In this context, water occupancy, relative interaction energy, entropy, and topologies of W13 are thermodynamically acceptable for the water displacement method. Therefore, the strategic use of W13's geometrical position in the DI domain may be implemented for drug discovery against COVID disease by designing new ligands with appropriately oriented chemical groups to mimic its structural, electronic, and thermodynamic properties.

Design of a Modular Energy Production–Storage System for a Sustainable Bicycle

This paper presents a new concept of a modular system for the production and storage of energy in a bicycle at any speed above 9 km/h. User-Centered Design methodology was applied to establish the design premises, and then each component of the modular system was selected, developed, and refined separately, carrying out all component integration (hub dynamo, USB charger, batteries, and solar panel) by means of a simple extension cable. Then, simulations were made with different software tools to create a design candidate. A new design of an integrated modular energy production–storage system was obtained, aiming to cover the needs of long-distance bikers and daily bike commuters. The designed system can charge its own batteries and power devices connected to the USB charger from a speed of 9 km/h. The system entails a modular integration solution that is not only cost-effective but also highly efficient. Its ergonomic design allows users to effortlessly replace batteries as and when needed. Current models on the market do not possess this integration.

Assessment of the classical theory validity through Cu50Zr50 nucleation and growth molecular dynamics simulations

One of the most promising candidates for novel glass-forming alloy design is atomic-scale simulations, primarily due to their capability to compute properties that are usually experimentally difficult to measure. This work aims to use classical molecular dynamics (MD) simulations to obtain the key nucleation and growth variables for the glass-forming Cu50Zr50 alloy while verifying the validity of the Classical Nucleation Theory (CNT). Through CNT equations and simulation data, nucleation and growth rates could be determined, revealing good agreement between CNT expressions and simulation results. The data were compared with other simulation and experimental data, providing reasonable evidence that supports the validity of CNT for describing the solidification behavior of glassy alloys. Simulation data seems to overestimate the experimental growth rate by one order of magnitude, a trend that requires further investigation and correlation with the system/potential to enhance the method’s robustness and reliability for other alloy compositions.

In-silico evaluation of natural alkaloids against the main protease and spike glycoprotein as potential therapeutic agents for SARS-CoV-2.

Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) is the causative agent of COVID-19 pandemic, which has resulted in global fatalities since late December 2019. Alkaloids play a significant role in drug design for various antiviral diseases, which makes them viable candidates for treating COVID-19. To identify potential antiviral agents, 102 known alkaloids were subjected to docking studies against the two key targets of SARS-CoV-2, namely the spike glycoprotein and main protease. The spike glycoprotein is vital for mediating viral entry into host cells, and main protease plays a crucial role in viral replication; therefore, they serve as compelling targets for therapeutic intervention in combating the disease. From the selection of alkaloids, the top 6 dual inhibitory compounds, namely liensinine, neferine, isoliensinine, fangchinoline, emetine, and acrimarine F, emerged as lead compounds with favorable docked scores. Interestingly, most of them shared the bisbenzylisoquinoline alkaloid framework and belong to Nelumbo nucifera, commonly known as the lotus plant. Docking analysis was conducted by considering the key active site residues of the selected proteins. The stability of the top three ligands with the receptor proteins was further validated through dynamic simulation analysis. The leads underwent ADMET profiling, bioactivity score analysis, and evaluation of drug-likeness and physicochemical properties. Neferine demonstrated a particularly strong affinity for binding, with a docking score of -7.5025 kcal/mol for main protease and -10.0245 kcal/mol for spike glycoprotein, and therefore a strong interaction with both target proteins. Of the lead alkaloids, emetine and fangchinoline demonstrated the lowest toxicity and high LD50 values. These top alkaloids, may support the body's defense and reduce the symptoms by their numerous biological potentials, even though some properties naturally point to their direct antiviral nature. These findings demonstrate the promising anti-COVID-19 properties of the six selected alkaloids, making them potential candidates for drug design. This study will be beneficial in effective drug discovery and design against COVID-19 with negligible side effects.