Computational Methodology Research Articles

Interpreting various molecular interactions of two amino acids prevalent in aqueous antiplatelet drug by experimental and computational methodologies

For efficient drug design and development, employing a holistic method that combines insights gathered from structural, thermodynamic, and biological backgrounds is essential. Understanding the energetic principles underlying molecular interactions requires employing various thermodynamic methods and physicochemical, spectroscopic, and computational analyses, particularly in the early stages of the drug development process to achieve the best possible energy interaction profile and maintain a strong pharmacological assay. The present study focused on the various molecular interactions of L-Isoleucine, an essential amino acid, and L-Proline a nonessential amino acid within varying molalities (0.001, 0.003, 0.005 mol.kg−1) in an aqueous solution of an antiplatelet drug namely ticlopidine hydrochloride [TCP][HCl] at five distinct temperature (293.15 K, 298.15 K, 303.15 K, 308.15 K, and 313.15 K) and pressure at 0.1 MPa. The volumetric properties like apparent molar volume (ϕv), Limiting partial molar volume (ϕv0), Limiting the partial molar volume of transfer (Δϕv0), hydration number (nH), Hepler’s constant (∂ϕE0/∂T) are calculated using density data; The viscosity B-coefficient, temperature derivative of the B-coefficient (dB/dT), Free activation energy of the solvent and solute (Δμ10≠ and Δμ20≠), entropy and enthalpy of activation (ΔS20≠ and ΔH20≠), and solvation number (ns) are all determined using the viscosity data; Walden product (Λm0η0), limiting molar conductance (Λm0), molar conductance (Λm) was calculated using a specific conductance value; surface tension data calculates the Limiting Slope (∂σ/∂m). The UV–visible absorbance was measured, and the association constant was computed. The results of 1H NMR spectroscopy confirmed significant interactions among the protons of amino acids and Ticlopidine hydrochloride. These changes indicate a strong molecular interaction between them, which aligns with our theoretical studies.

Decoding Multidrug Resistance: Genetic Architecture and Codon Usage Patterns in ESKAPE Pathogens

Introduction: The escalating challenge of multidrug resistance among ESKAPE pathogens has become a prominent concern for global healthcare providers, leading to amplified morbidity and mortality rates. Method: Therefore, we conducted this study to elucidate the genetic architecture of ESKAPE constituents with the intent of ameliorating pathogenicity and facilitating drug development efforts. A comprehensive array of computational tools and statistical methodologies were employed to scrutinize the genomes of ESKAPE pathogens, revealing that translational selection profoundly influences the codon usage bias within this pathogenic cohort. Notably, leucine emerged as the predominantly amino acid, except in the case of Acinetobacter baumannii, where arginine exhibited preeminence. There was a universal preference for at least one histidine codon across all ESKAPE pathogens. GpC emerged as the most prominently overrepresented dinucleotide at the codon pair junction in all ESKAPE pathogens. Result: Furthermore, a comparison of gyrB gene sequences and phylogenic tree construction showed a distinct evolutionary relationship between AT-rich and GC-rich ESKAPE pathogens. Additionally, identification, characterization, and phylogenetic analysis of multiple antibiotic resistance genes revealed distinct evolutionary relationships. Conclusion: It was discerned that despite substantial variability amongst antibioticresistance genes, leucine emerged as the predominant amino acid. conclusion: Ultimately, we can say that codon and amino acid usage bias in ESKAPE pathogens and their ARGs genes has contributed the evolution of antibiotic resistance nature if the pathogens. Therefore, our study underscores the importance of continued surveillance and research efforts to address the growing threat of antibiotic resistance in ESKAPE pathogens .

Ultralow thermal conductivity in Si–Ge nanograin mixtures: A cost-effective granular material for thermoelectric applications

This paper presents a theoretical study of the thermal conductivity of Si–Ge nanograin mixtures using a multiscale computational methodology based on solving the Boltzmann transport equation for phonons with first-principles techniques. A size-dependent correction factor is developed to account for the spatial dependence of the phonon distribution function on nanograin size, with parameters derived from the phonon properties of infinite Si and Ge crystals. This approach makes it possible to accurately calculate the thermal conductivity within a single nanograin, using force constants obtained from first-principles calculations. Thermal energy transport by phonons across grain boundaries is modeled by accounting for phonon transmission by two-phonon processes, weighting specular, and diffuse transmission for each phonon mode as a function of the root-mean-square roughness of the boundary relative to the phonon wavelength. The boundary thermal conductance model, previously validated against experimental data, is implemented using first-principles techniques. This approach excludes specular transmission for phonon modes with specific symmetries while ensuring conservation of the total number of modes in each symmetry class. The study examines the influence of grain size, nanograin mixture composition, temperature, and boundary asperities on the thermal conductivity of nanograin mixtures.

The digital evolution in toxicology: pioneering computational education for emerging challenges

The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students’ problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions.

Robocasting of Hierarchical Porous Al2O3 Structures: A Computational and Experimental Methodology for Porosity Estimation and its Effect

Robocasting of Hierarchical Porous Al2O3 Structures: A Computational and Experimental Methodology for Porosity Estimation and its Effect

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

Unveiling Weak Signals of Emergence in Underwater Sensing Research Trends

Detecting emerging research trends is crucial as it allows for the proactive identification and monitoring of novel and influential topics in the scientific community. Monitoring research trends aids researchers, institutions, and policymakers in allocating resources, fostering innovation, and staying competitive in rapidly changing scientific landscapes. The growing significance of underwater sensing technologies in various domains has propelled research endeavours aimed at understanding the characteristics of academic discourse in this field. In this work, we comprehensively analyzed the academic research topics related to underwater sensing technologies using advanced computational methodologies. Leveraging natural language processing, topic modelling, and weak signal detection techniques, and focusing on underwater sensing as the case technology, we dissect a large corpus of scholarly articles published between 2007 and 2021 to unveil underlying thematic patterns and emergent trends within this domain while shedding light on signals of emerging technologies. Among the eighty extracted topics, six research topics were identified and recognized as emerging weak signals, and validated by experts. Notably, deep learning for underwater imaging was the only topic that transitioned from being weak to a strong signal in the final period.

Methodical evaluation of Boyle temperatures using Mayer sampling Monte Carlo with application to polymers in implicit solvent.

The Boyle temperature, TB, for an n-segment polymer in solution is the temperature where the second osmotic virial coefficient, A2, is zero. This characteristic is of interest for its connection to the polymer condensation critical temperature, particularly for n → ∞. TB can be measured experimentally or computed for a given model macromolecule. For the latter, we present and examine two approaches, both based on the Mayer-sampling Monte Carlo (MSMC) method, to calculate Boyle temperatures as a function of model parameters. In one approach, we use MSMC calculations to search for TB, as guided by the evaluation of temperature derivatives of A2. The second approach involves numerical integration of an ordinary differential equation describing how TB varies with a model parameter, starting from a known TB. Unlike general MSMC calculations, these adaptations are appealing because they neither invoke a reference for the calculation nor use special averages needed to avoid bias when computing A2 directly. We demonstrate these methods by computing TB lines for off-lattice linear Lennard-Jones polymers as a function of chain stiffness, considering chains of length n ranging from 2 to 512 monomers. We additionally perform calculations of single-molecule radius of gyration Rg and determine the temperatures Tθ, where linear scaling of Rg2 with n is observed, as if the polymers were long random-walk chains. We find that Tθ and TB seem to differ by 6% in the n → ∞ limit, which is beyond the statistical uncertainties of our computational methodology. However, we cannot rule out systematic error relating to our extrapolation procedure as being the source of this discrepancy.

WSC: A Crowd‐Powered Framework for Mapping Decomposable Complex‐Task With Worker‐Set

ABSTRACTThe crowdsourcing platform serves as an intermediary managing the interaction between a requester who posts a decomposable task and a pool of workers who bid to solve it. Each worker intending to take up the task (partially or fully) decomposes it into multiple independent subtasks and submits it to the platform. Selection of a diverse set of workers (based on the bids received) to solve the decomposable task is challenging as it requires balancing factors like cost and quality while encouraging collaboration. We propose a Worker Set Computation (WSC) methodology to address these challenges by selecting a custom set of potential workers who can collaboratively complete the task with the optimal cost, in an efficient way. The aging technique is employed to dynamically update the weight of each worker, giving more weightage to the feedback received in the recent past. This, in turn, not only favors those workers who were rated well in the immediate past but also ensures that one odd feedback does not influence the overall rating heavily. We compare the performance of the proposed method against the state‐of‐the‐art methods, considering the computational (and budget) requirements, as well as the aging‐based worker rating.

Development of novel CDK9 and CYP3A4 inhibitors for cancer therapy through field and computational approaches

Cyclin-dependent kinase 9 (CDK9) and cytochrome P450 3A4 (CYP3A4) have emerged as promising targets in the development of anticancer drugs, presenting a consistent challenge in the quest for potent inhibitors. CDK9 inhibitors can selectively target fast-growing cancer cells by disrupting transcription elongation, which in turn hinders the production of proteins essential for cell cycle progression and survivaŚ. Understanding how CYP3A4 metabolizes specific chemotherapy drugs allows for personalized treatment plans, optimizing drug dosages according to a patient’s metabolic profile. Since many cancer patients undergo combination therapies, and CYP3A4 is vital in drug metabolism, its inhibition or induction by one drug can alter the plasma levels of others, potentially leading to treatment failure or increased toxicity. Therefore, managing CYP3A4 activity is critical for effective cancer treatment. Employing a range of computational methodologies, this study systematically investigated the binding mechanisms of pyrimidine derivatives against CDK9 and CYP3A4. The field-based model demonstrated high R2 values (0.99), with Q2 (0.66), demonstrating its ability to predict in silico inhibitory activity against the target of this study. The screening process followed in this work led to the discovery of powerful new inhibitor compounds. Of the 15 new compounds designed, three have a high affinity with the target (ranging from −8 to −9 kcal/mol kcal/mol) and were singled out through docking filtration for more detailed investigation. As well as, a reference compound with a substantial pIC50 value of 8.4, serving as the foundation for the development of the new compounds, was included for comparative analysis. To elucidate the essential features of CDK9 and CYP3A4 inhibitor design, a comparative analysis was conducted between 3D-QSAR-generated contours and molecular docking conformations of ligands. Molecular dynamics simulations were carried out for a duration of 100 ns on selected docked complexes, specifically those involving novel compounds with CDK9 and CYP3A4 enzymes. Additionally, the binding free energy for these complexes was assessed using the MM/PBSA method, which evaluates the free energy landscape of protein-ligand interactions. The results of MM/PBSA highlighted the strength of the new compounds in enhancing interactions with the target protein, which favors the results of molecular docking and MD simulation. These insights contribute to a deeper understanding of the mechanisms underlying CDK9 and CYP3A4 inhibition, offering potential avenues for the development of innovative and effective CDK9 inhibitors.

Recent strategies in target identification of natural products: Exploring applications in chronic inflammation and beyond.

Natural products are a treasure trove for drug discovery, especially in the areas of infection, inflammation and cancer, due to their diverse bioactivities and complex, and varied structures. Chronic inflammation is closely related to many diseases, including complex diseases such as cancer and neurodegeneration. Improving target identification for natural products contributes to elucidating their mechanism of action and clinical progress. It also facilitates the discovery of novel druggable targets and the elimination of undesirable ones, thereby significantly enhancing the productivity of drug discovery and development. Moreover, the rise of polypharmacological strategies, considered promising for the treatment of complex diseases, will further increase the demand for target deconvolution. This review underscores strategies for identifying natural product targets (NPs) in the context of chronic inflammation over the past 5 years. These strategies encompass computational methodologies for early target discovery and the anticipation of compound binding sites, proteomics-driven approaches for target delineation and experimental biology techniques for target validation and comprehensive mechanistic exploration.

Engineering of the High‐Power Laser‐Induced Synthesis of Ni‐Based Metal‐Organic Framework: Investigation of its Optical Properties, Computational Methodology, Electrocatalytic Performances, and Glucose‐Sensing Ability

AbstractMetal‐organic frameworks (MOFs) are porous materials with numerous chemical and structural possibilities. Due to their ease of modification, well‐organized structure, and diverse guest molecule chemistry, MOFs are ideal platforms for uncovering improved functional material design characteristics. Quantitative analysis of glucose is crucial, especially in some food products, for quality control as well as evaluation of the glucose levels helps diagnose and treat diabetes. Recent glucose sensing devices have relied heavily on MOFs and other nanomaterials to enable user‐friendly and safe non‐invasive sensing methods. Nevertheless, the conventional synthesis methods involve multi‐day reactions, cooling, and depressurization processes. This study demonstrates the unprecedented high‐power laser‐induced rapid synthesis (LIRS) of Ni‐based MOF nanospheres with interconnected nano‐rods and noncentrosymmetric primitive triclinic crystalline structure, highlighting their multifunctional usage in sensing and gas sorption applications. Ab initio simulations show excellent agreement with the experimental physical and gas sorption properties. Furthermore, the Ni‐MOF‐based biosensor accurately measures glucose real‐life beverage samples, yielding promising glucose detection biosensor results with a low limit of the detection (LOD) of 13.96 µM and high sensitivity of 120.606 µA mM−1 cm−2.

Comparative Analysis on the Application of Markowitz Model and Index Model in Portfolios

This article presents a comparison between the Markowitz model and the index model, focusing on their applications in portfolio management. The Markowitz model, also known as the mean-variance model, revolutionized portfolio theory by introducing a quantitative method for balancing risk and return. This model constructs optimal portfolios that maximize expected return for a given level of risk or minimize risk for a given expected return, using a covariance matrix to assess relationships between asset returns. In contrast, index models popularized by William Sharpe offer a simplified approach focused on the relationship between individual asset returns and a common benchmark (usually a market portfolio). The article explores the theoretical foundations of the two models, highlighting their mathematical formulations and assumptions. It also discusses the computational methodologies involved in implementing these models and examines the challenges and advances in optimization techniques. It evaluates how these technological innovations have enhanced portfolio optimization, enabling the handling of larger datasets and more complex market dynamics. In addition to comparing these two models, the article delves deeper into the concepts of efficient frontier, inefficient frontier and minimum variance frontier. The efficient frontier is the portfolios that offer the highest expected return for target risk, while the inefficient frontier includes portfolios that do not offer the best return for the level of risk. Minimum variance frontier focuses on portfolios that minimize risk for target expected return, emphasizing the importance of risk management in investment decisions.

Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals.

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Synergizing Experimentation and Computation: Predicting Energetic Potential in New Cyclo-Peroxide Compounds.

This manuscript explores the synthesis of new cyclo-peroxide compounds (CPs) through a systematic approach involving 10 different ketones and two concentrations of H2O2. Following spectroscopic analysis and calorimetric tests on 10 selected compounds, the percentage of Power Index (%PI) was calculated. The study introduces a computational methodology based on the Iterative Stochastic Elimination (ISE) algorithm. The newly constructed ISE model, with demonstrated robust predictive capabilities indicated by its statistical parameters, was employed to screen and score the CPs, assessing their potential as energetic materials. Comparison between %PI obtained experimentally, and the ISE index derived computationally revealed consistent assessments of the new CPs' energetic potential. The research emphasizes that, particularly in the synthesis of cyclic peroxides, the ISE model is a preferable and efficient tool for predicting a compound's potential as an energetic substance. Utilizing the ISE model ensures faster, more cost-effective, and safer decision-making in experimental examinations, focusing attention only on compounds with the highest ISE scores. Furthermore, the manuscript suggests an intriguing avenue for future research by proposing the investigation of ester nitrates. The study advocates a comprehensive approach that combines experimental methods (synthesis, spectroscopy, and DSC) with computational evaluation using the ISE model to identify potential high-energy compounds. This integrated approach promises to enhance the efficiency and reliability of the energetic materials discovery process.

Diffusion and Viscosity in Mixed Protein Solutions.

The viscosity and diffusion properties of crowded protein systems were investigated with molecular dynamics simulations of SH3 mixtures with different crowders, and results were compared with experimental data. The simulations accurately reproduced experimental trends across a wide range of protein concentrations, including highly crowded environments up to 300 g/L. Notably, viscosity increased with crowding but varied little between different crowder types, while diffusion rates were significantly reduced depending on protein-protein interaction strength. Analysis using the Stokes-Einstein relation indicated that the reduction in diffusion exceeded what was expected from viscosity changes alone, with the additional slow-down attributable to transient cluster formation driven by weakly attractive interactions. Contact kinetics analysis further revealed that longer-lived interactions contributed more significantly to reduced diffusion rates than short-lived interactions. This study also highlights the accuracy of current computational methodologies for capturing the dynamics of proteins in highly concentrated solutions and provides insights into the molecular mechanisms affecting protein mobility in crowded environments.

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agonist has been observed to promote the proliferation and viability of hypothalamus cells in the human body. The objective of the present study is to discover new agonist compounds for the GPR40 protein through the utilization of machine learning and pharmacophore-based screening techniques, in conjunction with other computational methodologies such as docking, molecular dynamics simulations, free energy calculations, and investigations of the free energy landscape. In the course of our investigation, we successfully identified five unreported agonist compounds that exhibit robust docking score, displayed stability in ligand RMSD and consistent hydrogen bonding with the receptor in the MD trajectories. Free energy calculations were observed to be higher than control molecule. The measured binding affinities of compounds namely 1, 3, 4, 6 and 10 were -13.9, -13.5, -13.4, -12.9, and -12.1 Kcal/mol, respectively. The identified molecular agonist that has been found can be assessed in terms of its therapeutic efficacy in the treatment of neurological diseases.

Computational exploration of acefylline derivatives as MAO-B inhibitors for Parkinson's disease: insights from molecular docking, DFT, ADMET, and molecular dynamics approaches.

Monoamine oxidase B (MAO-B) plays a pivotal role in the deamination process of monoamines, encompassing crucial neurotransmitters like dopamine and norepinephrine. The heightened interest in MAO-B inhibitors emerged after the revelation that this enzyme could potentially catalyze the formation of neurotoxic compounds from endogenous and exogenous sources. Computational screening methodologies serve as valuable tools in the quest for novel inhibitors, enhancing the efficiency of this pursuit. In this study, 43 acefylline derivatives were docked against the MAO-B enzyme for their chemotherapeutic potential and binding affinities that yielded GOLD fitness scores ranging from 33.21 to 75.22. Among them, five acefylline derivatives, namely, MAO-B14, MAO-B15, MAO-B16, MAO-B20, and MAO-B21, displayed binding affinities comparable to the both standards istradefylline and safinamide. These derivatives exhibited hydrogen-bonding interactions with key amino acids Phe167 and Ile197/198, suggesting their strong potential as MAO-B inhibitors. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the examined acefylline derivatives over time. The simulations demonstrated that among the examined acefylline derivatives and standards, MAO-B21 stands out as the most stable candidate. Density functional theory (DFT) studies were also performed to optimize the geometries of the ligands, and molecular docking was conducted to predict the orientations of the ligands within the binding cavity of the protein and evaluate their molecular interactions. These results were also validated by simulation-based binding free energies via the molecular mechanics energies combined with generalized Born and surface area solvation (MM-GBSA) method. However, it is necessary to conduct in vitro and in vivo experiments to confirm and validate these findings in future studies.

Leveraging Therapeutic Proteins and Peptides from Lumbricus Earthworms: Targeting SOCS2 E3 Ligase for Cardiovascular Therapy through Molecular Dynamics Simulations

Suppressor of cytokine signaling 2 (SOCS2), an E3 ubiquitin ligase, regulates the JAK/STAT signaling pathway, essential for cytokine signaling and immune responses. Its dysregulation contributes to cardiovascular diseases (CVDs) by promoting abnormal cell growth, inflammation, and resistance to cell death. This study aimed to elucidate the molecular mechanisms underlying the interactions between Lumbricus-derived proteins and peptides and SOCS2, with a focus on identifying potential therapeutic candidates for CVDs. Utilizing a multifaceted approach, advanced computational methodologies, including 3D structure modeling, protein–protein docking, 100 ns molecular dynamics (MD) simulations, and MM/PBSA calculations, were employed to assess the binding affinities and functional implications of Lumbricus-derived proteins on SOCS2 activity. The findings revealed that certain proteins, such as Lumbricin, Chemoattractive glycoprotein ES20, and Lumbrokinase-7T1, exhibited similar activities to standard antagonists in modulating SOCS2 activity. Furthermore, MM/PBSA calculations were employed to assess the binding free energies of these proteins with SOCS2. Specifically, Lumbricin exhibited an average ΔGbinding of −59.25 kcal/mol, Chemoattractive glycoprotein ES20 showed −55.02 kcal/mol, and Lumbrokinase-7T1 displayed −69.28 kcal/mol. These values suggest strong binding affinities between these proteins and SOCS2, reinforcing their potential therapeutic efficacy in cardiovascular diseases. Further in vitro and animal studies are recommended to validate these findings and explore broader applications of Lumbricus-derived proteins.

In silico PCR analysis: a comprehensive bioinformatics tool for enhancing nucleic acid amplification assays.

Nucleic acid amplification assays represent a pivotal category of methodologies for targeted sequence detection within contemporary biological research, boasting diverse utility in diagnostics, identification, and DNA sequencing. The foundational principles of these assays have been extrapolated to various simple and intricate nucleic acid amplification technologies. Concurrently, a burgeoning trend toward computational or virtual methodologies is exemplified by in silico PCR analysis. In silico PCR analysis is a valuable and productive adjunctive approach for ensuring primer or probe specificity across a broad spectrum of PCR applications encompassing gene discovery through homology analysis, molecular diagnostics, DNA profiling, and repeat sequence identification. The prediction of primer and probe sensitivity and specificity necessitates thorough database searches, accounting for an optimal balance of mismatch tolerance, sequence similarity, and thermal stability. This software facilitates in silico PCR analyses of both linear and circular DNA templates, including bisulfited treatment DNA, enabling multiple primer or probe searches within databases of varying scales alongside advanced search functionalities. This tool is suitable for processing batch files and is essential for automation when working with large amounts of data.