Hybrid Computational Approach Research Articles

Pinpointing the CN Stretch Frequency of Neutral Cyano-Polycyclic Aromatic Hydrocarbons: A Laboratory and Quantum Chemical Spectroscopic Study of 9-Cyanoanthracene.

The CN stretch frequency of neutral, gas-phase 9-cyanoanthracene is 2207 cm-1 (4.531 μm) based on high-resolution infrared absorption experiments coupled with a new hybrid anharmonic quantum chemical methodology. A broad band (full-width at half-maximum of 47 cm-1) is observed and assigned to multiple transitions, including the CN stretch fundamental and various combination bands that gather intensity from strong anharmonic coupling with the bright CN stretch. The new hybrid computational approach utilizes the harmonic force constants from the double-hybrid rev-DSDPBEP86 functional that includes MP2 electron correlation, and the cubic and quartic force constants from the B3LYP density functional. In combination, this method computes a band center of 2207 cm-1 for 9-cyanoanthracene, a direct match with experiment. Further, the hybrid method produces a difference of less than 1 cm-1 for the two isomers of cyanonaphthalene and cyanobenzene. As shown from comparison with CCSD(T)-F12b anharmonic frequency computations of cyanobenzene, inclusion of electron correlation is required to properly characterize the electronic structure of the highly electron withdrawing CN group on polycyclic aromatic hydrocarbons. In agreement with earlier studies, computation of the CN stretch of 14 small CN-PAHs produces a narrow (∼20 cm-1) band from 2207-2229 cm-1 (4.53-4.48 μm). The remainder of the spectrum below 2000 cm-1 and from 3000-3120 cm-1 shows good agreement between experiment and the hybrid theory with a mean absolute error of 16 and 14 cm-1, respectively.

Impact of frequent ARID1A mutations on protein stability provides insights into cancer pathogenesis

The ARID1A gene, frequently mutated in cancer, encodes the AT-rich interactive domain-containing protein 1 A, a key component of the chromatin remodeling SWI/SNF complex. The ARID1A protein features a conserved DNA-binding domain (ARID domain) of approximately 100 residues crucial for its function. Despite the frequency of mutations, the impact on ARID1A’s stability and contribution to cancer progression remains unclear. We analyzed five frequent missense mutations R1020S, M1022K, K1047Q, G1063V, and A1089T identified in The Cancer Genome Atlas (TCGA) to assess their effects on the stability of the ARID domain using a hybrid experimental and computational approach. By combining computational stability from web server tools, the structural dynamics from replica exchange discrete molecular simulation (rexDMD), and thermal and chemical denaturation experiments, we found that the R1020S mutation severely decreases structural stability, making it the most impactful, while M1022K has minimal effect, and others lie in between. These findings enhance our understanding of the structural-functional relationship of ARID1A missense mutations at the molecular levels and their role in cancer pathogenesis. This research paves the way for identifying and categorizing which ARID1A mutations are most pathogenic, potentially guiding the development of targeted therapies tailored to specific mutation profiles in cancer treatment.

Impact of Frequent ARID1A Mutations on Protein Stability: Insights into Cancer Pathogenesis.

The ARID1A gene, frequently mutated in cancer, encodes the AT-rich interactive domain-containing protein 1A, a key component of the chromatin remodeling SWI/SNF complex. The ARID1A protein features a conserved DNA-binding domain (ARID domain) of approximately 100 residues crucial for its function. Despite the frequency of mutations, the impact on ARID1A's stability and contribution to cancer progression remains unclear. We analyzed five frequent missense mutations R1020S, M1022K, K1047Q, G1063V, and A1089T identified in The Cancer Genome Atlas (TCGA) to assess their effects on the stability of the ARID domain using a hybrid experimental and computational approach. By combining computational stability from web server tools, the structural dynamics from replica exchange discrete molecular simulation (rexDMD), and thermal and chemical denaturation experiments, we found that the R1020S mutation severely decreases structural stability, making it the most impactful, while M1022K has minimal effect, and others lie in between. These findings enhance our understanding of the structural-functional relationship of ARID1A missense mutations at the molecular levels and their role in cancer pathogenesis. This research paves the way for identifying and categorizing which ARID1A mutations are most pathogenic, potentially guiding the development of targeted therapies tailored to specific mutation profiles in cancer treatment.

Modeling and Simulation of Micron Particle Agglomeration in a Turbulent Flow: Impact of Cylindrical Disturbance and Particle Properties.

The fly ash generated by coal combustion is one of the main sources of PM2.5, so the particulate matter removal technology of coal-fired boilers is receiving increasing attention. Turbulent agglomeration has emerged as a powerful tool for improving the efficiency of removing fine particulates from environments, sparking interest in its study. Our research meticulously investigated the influence of cylindrical vortex wakes on particle flow, agglomeration patterns, and the dynamics between fluids and particles. By employing a novel hybrid computational approach that integrates the discrete element method (DEM) with large Eddy simulation (LES), we were able to accurately simulate particle-particle interactions. The study focused on understanding how particles with different diameters (2, 5, 10, and 20 μm), densities (2,500, 5,000, 7,500, and 10,000 kg·m-3), and surface energies (0.01, 0.1, and 1 J·m-2) behaved within transitioning shear layer flow conditions. Our findings revealed that particles tended to congregate in areas of lower vorticity, with larger and denser particles demonstrating greater agglomeration efficiency due to their resilience against turbulent forces. Conversely, particles of lower density formed smaller agglomerates as their susceptibility to shear forces increased. Additionally, the study discovered that higher surface energies enhance adhesion, leading to the formation of larger agglomerates.

Dynamic conformational response of von Willebrand factor to varying shear stress: A hybrid computational approach

This computational study examines the mechanobiological responses of von Willebrand Factor (vWF) to different shear stress levels, emphasizing the unique structural dynamics of its various domains. We introduce a novel coarse-grained model, representing each cluster of six amino acids as a bead, to synthesize the Lattice Boltzmann method with Brownian dynamics. This approach captures the vWF molecule’s conformational adaptability across a spectrum of shear rates, ranging from 5 to 5e + 7s−1, encompassing normal physiological flows to those resembling the high-shear environment of arterial stenosis.Our results reveal domain-specific responses of vWF, particularly in the A2 and A3 domains, which demonstrate significant elongation under elevated shear—reflecting their pivotal roles in clotting processes. Moreover, the study underscores the critical influence of random thermal forces on the molecule’s conformation, with simulations including these forces showing substantially greater conformational variability compared to those without.As shear rates increase, we observe a notable reorganization in the spatial arrangement of vWF domains. This reconfiguration is most evident in the increased separation between the A1 and D’D3 domains under high shear conditions, potentially enhancing the accessibility of platelets to key binding sites on vWF. These findings are paramount for a deeper understanding of vWF’s mechanical behavior in blood clotting and thrombosis, particularly the delicate balance between facilitating platelet adhesion and avoiding excessive stretching that could lead to thrombosis.Our work provides a foundation for future studies investigating vWF behavior in complex flow geometries and its interactions with other blood components. Such insights pave the way for more informed therapeutic strategies targeting vWF function in vascular health and disease, potentially leading to improved treatments for thrombotic disorders.

Hybrid finite element and laplace transform method for efficient numerical solutions of fractional PDEs on graphics processing units

Fractional Partial Differential equations (FPDEs) are essential for modeling complex systems across various scientific and engineering areas. However, efficiently solving FPDEs presents significant computational challenges due to their inherent memory effects, often leading to increased execution times for numerical solutions. This study proposes a highly parallelizable hybrid computational approach that combines the Finite Element Method (FEM) for spatial discretization with Numerical Inversion of the Laplace Transform (NILT) for time-domain solutions, optimized for execution on Graphics Processing Units (GPUs). The NILT method’s high parallelizability, stemming from the independence of its series terms, combined with the robust spatial discretization provided by FEM, enables the efficient and accurate solution of FPDEs on GPUs, demonstrating substantial performance improvements over traditional CPU-based implementations. We observe a generalized pattern in execution time behavior that accounts for both the number of nodes and the number of NILT terms. Specifically, execution time scales quadratically with the number of nodes, while showing only a logarithmic marginal increase with the number of NILT terms These behaviors not only enables consistent performance assessment but also highlights potential areas for algorithm optimization. Validation against exact solutions of fractional diffusion and wave equations, employing Caputo, modified Caputo-Fabrizio, and modified Atangana-Baleanu derivatives, demonstrates the accuracy and convergence of the hybrid FEM-NILT method. Notably, the exact solutions of wave equation are novel in literature. The results highlight the method’s potential for enabling high-precision, large-scale simulations in fractional calculus applications, thereby advancing computational capabilities and efficiency in the field.

Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.

Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure-activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.

Computational modeling of dorsal root ganglion stimulation using an Injectrode

Objective. Minimally invasive neuromodulation therapies like the Injectrode, which is composed of a tightly wound polymer-coated Platinum/Iridium microcoil, offer a low-risk approach for administering electrical stimulation to the dorsal root ganglion (DRG). This flexible electrode is aimed to conform to the DRG. The stimulation occurs through a transcutaneous electrical stimulation (TES) patch, which subsequently transmits the stimulation to the Injectrode via a subcutaneous metal collector. However, it is important to note that the effectiveness of stimulation through TES relies on the specific geometrical configurations of the Injectrode-collector-patch system. Hence, there is a need to investigate which design parameters influence the activation of targeted neural structures. Approach. We employed a hybrid computational modeling approach to analyze the impact of Injectrode system design parameters on charge delivery and neural response to stimulation. We constructed multiple finite element method models of DRG stimulation, followed by the implementation of multi-compartment models of DRG neurons. By calculating potential distribution during monopolar stimulation, we simulated neural responses using various parameters based on prior acute experiments. Additionally, we developed a canonical monopolar stimulation and full-scale model of bipolar bilateral L5 DRG stimulation, allowing us to investigate how design parameters like Injectrode size and orientation influenced neural activation thresholds. Main results. Our findings were in accordance with acute experimental measurements and indicate that the minimally invasive Injectrode system predominantly engages large-diameter afferents (Aβ-fibers). These activation thresholds were contingent upon the surface area of the Injectrode. As the charge density decreased due to increasing surface area, there was a corresponding expansion in the stimulation amplitude range before triggering any pain-related mechanoreceptor (Aδ-fibers) activity. Significance. The Injectrode demonstrates potential as a viable technology for minimally invasive stimulation of the DRG. Our findings indicate that utilizing a larger surface area Injectrode enhances the therapeutic margin, effectively distinguishing the desired Aβ activation from the undesired Aδ-fiber activation.

The Role of Forebody Topology on Aerodynamics and Aeroacoustics Characteristics of Squareback Vehicles using Computational Aeroacoustics (CAA)

This study investigates the influence of forebody configuration on aerodynamic noise generation and radiation in standard squareback vehicles, employing a hybrid computational aeroacoustics approach. Initially, a widely used standard squareback body is employed to establish grid-independent solutions and validate the applied methodology against previously published experimental data. Six distinct configurations are examined, consisting of three bodies with A-pillars and three without A-pillars. Throughout these configurations, the reference area, length, and height remain consistent, while systematic alterations to the forebody are implemented. The findings reveal that changes in the forebody design exert a substantial influence on both the overall aerodynamics and aeroacoustics performance of the vehicle. Notably, bodies without A-pillars exhibit a significant reduction in downforce compared to their A-pillar counterparts. For all configurations, the flow characteristics around the side-view mirror and the side window exhibit an asymmetrical horseshoe vortex with high-intensity pressure fluctuations, primarily within the confines of this vortex and the mirror wake. Side windows on bodies with A-pillars experience more pronounced pressure fluctuations, rendering these configurations distinctly impactful in terms of radiated noise. However, despite forebody-induced variations in pressure fluctuations impacting the side window and side-view mirror, the fundamental structure of the radiated noise remains relatively consistent. The noise pattern transitions from a cardioid-like shape to a monopole-like pattern as the probing distance from the vehicle increases.

Computational Identification of Methionyl-tRNA-Synthetase Inhibitors for Brucella melitensis: A Hybrid of Ligand-based Classic 3-Point Pharmacophore Screening and Structure Cavity Guided Blind Docking Approach

This study employs a hybrid computational approach to identify potential methionyl-tRNA synthetase inhibitors for Brucella melitensis. Utilizing ligand-based pharmacophore screening and structure-based blind docking, we selected a lead compound, CHEMBL349379, from the ChEMBL 2D database. Docking simulations revealed high binding affinity and favorable interactions. Lead optimization using ADMETlab 2.0 demonstrated promising drug-like properties, but a detailed toxicity analysis highlighted concerns. Experimental validation is needed to confirm inhibitory potential and address toxicity issues. This approach streamlines the identification of potential therapeutic agents for B. melitensis treatment.

A hybrid computational approach to anticipate individuals in sequential problem solving.

Human-awareness is an ever more important requirement for AI systems that are designed to assist humans with daily physical interactions and problem solving. This is especially true for patients that need support to stay as independent as possible. To be human-aware, an AI should be able to anticipate the intentions of the individual humans it interacts with, in order to understand the difficulties and limitations they are facing and to adapt accordingly. While data-driven AI approaches have recently gained a lot of attention, more research is needed on assistive AI systems that can develop models of their partners' goals to offer proactive support without needing a lot of training trials for new problems. We propose an integrated AI system that can anticipate actions of individual humans to contribute to the foundations of trustworthy human-robot interaction. We test this in Tangram, which is an exemplary sequential problem solving task that requires dynamic decision making. In this task the sequences of steps to the goal might be variable and not known by the system. These are aspects that are also recognized as real world challenges for robotic systems. A hybrid approach based on the cognitive architecture ACT-R is presented that is not purely data-driven but includes cognitive principles, meaning heuristics that guide human decisions. Core of this Cognitive Tangram Solver (CTS) framework is an ACT-R cognitive model that simulates human problem solving behavior in action, recognizes possible dead ends and identifies ways forward. Based on this model, the CTS anticipates and adapts its predictions about the next action to take in any given situation. We executed an empirical study and collected data from 40 participants. The predictions made by CTS were evaluated with the participants' behavior, including comparative statistics as well as prediction accuracy. The model's anticipations compared to the human test data provide support for justifying further steps built upon our conceptual approach.

A hybrid computational approach for modeling cold spray deposition

Cold spray (CS) is a cold-state technology that uses high-velocity supersonic gas flow to propel and deposit powder particles onto a substrate surface. The design and optimization of the cold spray deposition process were recently achieved via the experimental trial-and-error approach, which is laborious and costly. The present study presents a computationally efficient hybrid method for simulating cold spray coating deposition, applied explicitly to Ni coating used for wear applications. The model effectively synergizes meshless and meshed computational schemes in predicting the thermo-mechanical deformation of the impacting Ni particles and SS304 steel substrate. During the simulations, the point cloud (PC) of deformed particle domains is converted into a high-quality finite element (FE) mesh with novel PC processing algorithms. The simulations are carried out for various particle characteristics and spraying conditions. The numerical predictions are validated by comparing them with other numerical schemes and previous experimental studies. The main results indicate that the kinetic energy and morphology of the impacting Ni particles strongly influence plastic deformation and temperature rise in the substrate and predeposited coating particles. Plastic deformation is more prominent at the particle edges and mating material interfaces. At the same time, the temperature rise does not reach the melting point but can allow for recrystallization near highly localized regions of the coating microstructure, even at lower deposition rates. Compressive residual stresses are also observed across the coating and substrate layers, with a non-uniform and nonlinear residual stress field due to complex interactions among neighboring particles. The study aims to provide a robust numerical framework for designing and optimizing cold spray deposition parameters for industrial coatings, bridging the knowledge gap and enabling efficient and cost-effective simulations for process optimization.

An optimization framework for targeted spinal cord stimulation

Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s). Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs). Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs. Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.

Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation.

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.

Neuro-evolutionary computing paradigm for two strain COVID-19 model

The hybrid computational approaches have captured the interest of researchers over the past few decades. This paper examines the evolutionary integrated methodology, i.e. IGASQP of artificial neural networks (ANNs), to solve the nonlinear two-strain COVID-19 pandemic model (TSCPM). This approach exhibits the integrated characteristics of a global algorithm, e.g. the genetic algorithm with local search sequential quadratic programming (IGA-SQP). COVID-19 is a respiratory disease prompted by a mutable ribonucleic acid (RNA) virus. Around the world, variants with many attributes that might influence transmissibility emerged in December 2020. To address this new variant disease, a five-class mathematical model is constructed that comprises susceptible, vaccinated, strain-1 infected, strain-2 infected, and recovered humans based on the disease status of each individual. A mean squared error-based objective (fitness) function is established using TSCPM in terms of ANNs. The IGA-SQP results are provided and compared with the solution of the Adams numerical approach (ANA), and the corresponding absolute error is calculated. The performance indicators are established for many simulations runs that certify the flexibility, robustness, and effectiveness of IGA-SQP to solve TSCPM.

Novel hybrid computational intelligence approaches for predicting daily solar radiation

Novel hybrid computational intelligence approaches for predicting daily solar radiation

Toward a Realistic Surface State of Ru in Aqueous and Gaseous Environments

Identifying the surface species is critical in developing a realistic understanding of supported metal catalysts working in water. To this end, we have characterized the surface species present at a Ru/water interface by employing a hybrid computational approach involving an explicit description of the liquid water and a possible pressure of H2. On the close-packed, most stable Ru(0001) facet, the solvation tends to favor the full dissociation of water into atomic O and H in contrast with the partially dissociated water layer reported for ultra-high-vacuum conditions. The solvation stabilization was found to reach -0.279 J m2, which results in stable O and H species on Ru(0001) in the presence of liquid water even at room temperature. Conversely, introducing even a small H2 pressure (10-2 bar) results in a monolayer of chemisorbed H at the interface, a general trend found on the three most exposed facets of Ru nanoparticles. While hydroxyls were often hypothesized as possible surface species at the Ru/water interface, this computational study clearly demonstrates that they are not stabilized by liquid water and are not found under realistic reductive catalytic conditions.

Exploring the inhibitory potential of novel piperidine-derivatives against main protease (Mpro) of SARS-CoV-2: A hybrid approach consisting of molecular docking, MD simulations and MMPBSA analysis

In the current study, a hybrid computational approach consisting of different computational methods to explore the molecular electronic structures, bioactivity and therapeutic potential of piperidine compounds against SARS-CoV-2. The quantum chemical methods are used to study electronic structures of designed derivatives, molecular docking methods are used to see the most potential docking interactions for main protease (MPro) of SARS-CoV-2 while molecular dynamic and MMPBSA analyses are performed in bulk water solvation process to mimic real protein like aqueous environment and effectiveness of docked complexes. We designed and optimized piperidine derivatives from experimentally known precursor using quantum chemical methods. The UV–Visible, IR, molecular orbitals, molecular electrostatic plots, and global chemical reactivity descriptors are carried out which illustrate that the designed compounds are kinetically stable and reactive. The results of MD simulations and binding free energy revealed that all the complex systems possess adequate dynamic stability, and flexibility based on their RMSD, RMSF, radius of gyration, and hydrogen bond analysis. The computed net binding free energy (ΔGbind) as calculated by MMPBSA method for the complexes showed the values of −4.29 kcal.mol−1 for P1, −5.52 kcal.mol−1 for P2, −6.12 kcal.mol−1 for P3, −6.35 kcal.mol−1 for P4, −5.19 kcal.mol−1 for P5, 3.09 kcal.mol−1 for P6, −6.78 kcal.mol−1 for P7, and −6.29 kcal.mol−1 for P8.The ADMET analysis further confirmed that none of among the designed ligands violates the Lipinski rule of five (RO5). The current comprehensive investigation predicts that all our designed compounds are recommended as prospective therapeutic drugs against Mpro of SARS-CoV-2 and it provokes the scientific community to further perform their in-vitro analysis.

CTDN (Convolutional Temporal Based Deep‐ Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction

The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.

Investigating the biophysical interaction of serum albumins-gold nanorods using hybrid spectroscopic and computational approaches with the intent of enhancing cytotoxicity efficiency of targeted drug delivery

In this contribution, biophysical aspects of binding interaction, conformational changes, and molecular simulation studies of human and bovine serum albumins (HSA and BSA) exposed to gold nanorods (AuNRs) are thoroughly investigated and their bio-nanoconjugates were then used in in-vitro cytotoxic studies. The structural variations of AuNRs in the presence of serum proteins were confirmed by absorbance and high-resolution transmission electron microscopic (HR-TEM) pictures. According to the results of the steady-state emission quenching, AuNRs affect the intrinsic emission of HSA and BSA by a static mechanism. Synchronous emission spectrum results showed that the microenvironment near the tryptophan and tyrosine amino acid residues of serum proteins is altered, and the impact is stronger toward tryptophan than tyrosine. Furthermore, it was demonstrated by infrared, absorbance, and three-dimensional (3D) luminescence spectroscopic methods that the binding of AuNRs can arouse concerns and cause changes in the structural and/or microenvironmental characteristics of HSA and BSA. An outcome of the computational molecular simulation study supports the stability of the bio-nanoconjugate, and molecular docking interaction investigations suggest that the two-dimensional AuNRs surface associated with the cysteine residue of HSA and BSA contains sulphur atoms. Furthermore, using an in vitro model using the A-549 and MCF-7 cell lines, we calculated the toxicity and the effectiveness of cellular absorption of the AuNRs and their bio-nanoconjugates as a function of supplied dosage. These cytotoxicity findings demonstrate that protein adsorption affects AuNRs' physicochemical characteristics as well as enhances their subsequent biological function.