Design Techniques Research Articles

Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio

Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (C<sub>L</sub>/C<sub>D</sub>), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted C<sub>L</sub>/C<sub>D</sub> of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.

Design and Analysis of High Performance FinFET-Based Linear Feedback Shift Register for Cryptography Applications

Nowadays, all the elements of our surrounding is occupied by electronics. Electronics occupies major role on our day today life. Electronics device integration on a single chip enhances performance in terms of speed, low-power circuits, and area. Integration of VLSI provides better scalability and reliability. This research paper focused on the implementation of Linear feedback shift registers (LFSR) using FinFET techniques at the layout level for various applications, including cryptography. FinFETs are highlighted as a replacement for CMOS technology, offering advantages such as improved performance in terms of speed, low-power circuits, and area. Linear feedback shift registers are commonly used in digital systems and cryptography for generating pseudo-random sequences. The use of FinFET technology in the layout level suggests an interest in exploring advanced semiconductor technologies to enhance the performance of these circuits. The paper appears to cover different design methods of LFSRs, which could include aspects like circuit optimization, power efficiency, and reliability. The integration of electronics on a single chip, particularly with FinFET technology, is noted for its potential to provide better scalability and reliability. The first architecture is created using bulk CMOS techniques; whereas the second is constructed using two fin FinFET LFSRs. Using the Microwind designing tool, the third technique is developed with a 3-fin FinFET LFSR. It provides a solution for a novel FinFET architecture that implements LFSR. When comparing CMOS and FinFET circuit designs for LFSR, the latter achieves superior performance in terms of area and power efficiency. An analysis is conducted on the design techniques and performance of CMOS LFSR, 2 fin FinFET based LFSR, and 3 fin FinFET. According to the experimental findings, the CMOS-based LFSR uses 1.243 mW of power; the FinFET-based LFSR uses 90.47 μW, and the two fin FinFET LFRS uses 0.254 mW.

Antenna Systems for Satellite Communication Terminals

This Roadmap presents the research trends on antenna systems for Satellite Communication terminals. To address the challenges posed by this application in terms of wide fractional bandwidth and large field-of-view, several antenna concepts and architectures are being investigated, which can be grouped in three main technology areas: fully electronic scan, fully mechanical scan with unconventional steering mechanisms and beam switching. Design techniques, current results and future developments are outlined, considering representative examples for each technology area.

Presentation Techniques of Coin Design

Presentation Techniques of Coin Design

Application of DMSFNN-COA technique for brand image design

Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with Cheetah Optimization Algorithm (BID-DMSFNN-COA), for classifying product color brand images as “Stylish” and “Natural”. By leveraging deep learning techniques and optimization algorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as “Stylish” and “Natural”. Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both “Natural” and “Stylish” classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for “Stylish” and 65 %–70 % for “Natural” product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.

Economic and technical analysis of an HRES (Hybrid Renewable Energy System) comprising wind, PV, and fuel cells using an improved subtraction-average-based optimizer

HRES (Hybrid Renewable Energy Systems) has been designed because of the increasing demand for environmentally friendly and sustainable energy. In this study, an Improved Subtraction-Average-Based Optimizer (ISABO) is presented for optimizing the HRES system by wind power, fuel cells, and solar energy. The suggested approach, by introducing adaptive mechanisms and enhancing processes, improves the performance of the traditional subtraction-average-based optimization. Optimization aims to provide reliable and efficient energy while lowering system expenses. The efficacy of ISABO is evaluated for this goal and compared with other optimization techniques. According to the findings, The ISABO algorithm, when equipped with adaptive mechanisms, surpasses conventional optimization techniques by achieving a 12 % decrease in Net Present Cost (NPC) and Levelized Cost of Electricity (LCOE) along with a 45 % cost reduction in electrolyzers. Through simulations, it has been shown that the ISABO algorithm ensures the lowest average NPC at $1,357,018.15 while also upholding system reliability with just a 0.8 % decline in Load Point Supply Probability (LPSP) in the event of a PV unit failure. This research validates that hybrid PV/wind/fuel cell systems present superior cost-effectiveness and reliability, thereby opening doors for more economical renewable energy solutions. The study reveals hybrid PV/wind/fuel cell systems are more cost-effective than purely wind, PV, or fuel cell systems. This advancement in HRES design and optimization techniques will enable more cost-effective renewable energy options.

Discovery of HDM2010, a highly potent PTPN2 inhibitor for immunotherapy.

e15021 Background: Protein tyrosine phosphatase non-receptor type 2 (PTPN2), a negative regulator of JAK–STAT pathways that plays a crucial role in the immune system, has been identified as a promising target for immunotherapy. Genetic deletion of PTPN2 in either tumor cells or host immune cells promotes the anti-tumor immunity. In addition, PTPN2 inhibitors, alone or in combination with anti-PD1 therapy, significantly reduced tumor growth and enhanced survival in several subcutaneous tumour models. These findings demonstrate that PTPN2 inhibitors may have clinical benefits for immunotherapy-resistant tumors. Herein, the discovery of a series of novel PTPN2 inhibitors and their implications for immunotherapy are reported. Methods: We have successfully developed a series of small molecule inhibitors that specifically target PTPN2/N1. Molecular design was based on a novel backbone and incorporating both structure-based drug design (SBDD) and computational chemistry techniques. To evaluate the efficacy of our inhibitors, PTPN2 phosphatase activity was monitored using the small-molecule substrate DiFMUP in a fluorescence assay format in vitro. Tumor cells were treated with IFNγ and the cell viability was measured. Pharmacokinetic studies were conducted in mice, with serial blood samples analyzed by liquid chromatography−mass spectrometry (LC−MS)/MS. Pharmacokinetic parameters were calculated by noncompartmental analysis. Results: Based on the co-crystal structure of this target reported and multiple previous research projects, we have designed a multitude of novel compounds with the employment of computer-aided drug design (CADD) and scaffold hopping. Through a comprehensive evaluation of the in vitro and in vivo parameters, we have identified a series of compounds displaying potent PTPN2 inhibition (IC50 < 10 nM). Moreover, the compounds displayed remarkable efficacy in killing tumor cells to IFNγ treatment (IC50 < 100 nM). Artificial intelligence was utilized to conduct assessment and prediction of ADME properties, improving the drug-ability including permeability and physicochemical properties. Currently, a series of compounds demonstrating outstanding potency and good pharmacokinetic profiles, with lower hERG blockage (hERG IC50 > 30 µM), were synthesized and characterized. Among these hit compounds, a leading compound with PTPN2 IC50 = 1.1 nM and tumor cells IC50 = 79.5 nM was identified for further optimization and evaluation. Conclusions: We reported our work on discovering HDM2010, in which a series of potent PTPN2 inhibitors with prominent PK and safety profile were synthesized and characterized. Further optimization including in vivo experiments will lead to a preclinical candidate (PCC) selection.

Pengaruh Senam Tai Chi terhadap Psychological Wellbeing Lansia di Posyandu Sugih Waras, Desa Ngargorejo, Kecamatan Ngemplak, Boyolali

Abstract Background: Elderly people experience aging which causes changes, especially psychological, experiencing empty nest syndrome, anxiety disorders, depression, loneliness, and other psychological disorders which can affect psychological well-being. Tai Chi is an exercise that can have a positive effect on psychological well-being in sufferers of various chronic diseases, can reduce symptoms of anxiety and depression, and improve quality of life. Objectives: This study aims to determine the effect of Tai Chi exercise on the psychological well-being of elderly people at Sugih Waras Integrated Health Service Post, Ngargorejo Village, Ngemplak District, Boyolali. Methods: This research is a Pre-Experimental type of quantitative research with a one group pretest-posttest design and purposive sampling technique. Psychological well-being was measured using the Indonesian version of the 18-item Ryff Psychological Well-being Scale (RPWB). Data analysis used SPSS version 25. Results: Respondents were dominated by young elderly (73.9%), female (95.7%), housewives (91.3%), individuals with no schooling/not yet finished elementary school (47.8%), widows (69.6%), and individuals living with family (69.6%). The average pre-test and post-test RPWB scores increased by 2.53. The results of the paired sample t-test hypothesis test showed a p-value of 0.000 which is less than 0.05, indicating an influence of Tai Chi exercise on the psychological well-being of the elderly at Sugih Waras Integrated Health Service Post, Ngargorejo Village, Ngemplak District, Boyolali. Conclusion: Tai Chi exercise has an effect on the psychological well-being of the elderly at Sugih Waras Integrated Health Service Post, Ngargorejo Village, Ngemplak District, Boyolali. Keywords : Tai Chi, Psychological Well-being, Elderly

Where Are Czech Adolescents Active? The Patterns of Movement and Transport Behavior in Different Active Living Domains.

To understand the environmental determinants of physical activity (PA), precise spatial localization is crucial. This cross-sectional study focuses on the spatiotemporal distribution of PA among Czech adolescents (n = 171) using Global Positioning System loggers and accelerometers. The results showed that adolescents spent most of their time in sedentary behavior, with 57.2% and 58.5% of monitored time at home and school, respectively. The park and playground had the lowest proportion of sedentary behavior but also the lowest amount of moderate to vigorous PA (MVPA). However, when considering the time spent in each domain, the highest proportion of MVPA was seen in publicly accessible playgrounds (13.3% of the time). Chi-square analysis showed that the relative distribution of different PA intensities did not differ across spatial domains. Based on these results, the authors propose 2 key strategies for increasing MVPA in adolescents: Increase the time spent in activity-supportive environments, such as parks and playgrounds, and design techniques to increase MVPA at home and school settings.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis.

Computer-aided drug design (CADD) is a drug design technique for computing ligand‒receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics. A systematic review of studies published between 2000 and July 20, 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analysed using software such as Excel, VOSviewer, RStudio, and CiteSpace. A total of 2,031 publications were included. These publications primarily originated from 99 countries or regions, led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications. Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modelling, pharmacophore modelling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics direction for future development. Furthermore, this paper will be helpful for better understanding the frontiers and hotspots of CADD.

Merging automatic differentiation and the adjoint method for photonic inverse design

Optimizing the shapes and topology of physical devices is crucial for both scientific and technological advancements, given their wide-ranging implications across numerous industries and research areas. Innovations in shape and topology optimization have been observed across a wide range of fields, notably structural mechanics, fluid mechanics, and more recently, photonics. Gradient-based inverse design techniques have been particularly successful for photonic and optical problems, resulting in integrated, miniaturized hardware that has set new standards in device performance. To calculate the gradients, there are typically two approaches: namely, either by implementing specialized solvers using automatic differentiation (AD) or by deriving analytical solutions for gradient calculation and adjoint sources by hand. In this work, we propose a middle ground and present a hybrid approach that leverages and enables the benefits of AD for handling gradient derivation while using existing, proven but black-box photonic solvers for numerical solutions. Utilizing the adjoint method, we make existing numerical solvers differentiable and seamlessly integrate them into an AD framework. Further, this enables users to integrate the optimization environment seamlessly with other autodifferentiable components such as machine learning, geometry generation, or intricate post-processing which could lead to better photonic design workflows. We illustrate the approach through two distinct photonic optimization problems: optimizing the Purcell factor of a magnetic dipole in the vicinity of an optical nanocavity and enhancing the light extraction efficiency of a µLED.

Towards an AI-Driven User Interface Design for Web Applications

The increasing exploitation of Artificial Intelligence (AI) technologies has enabled the design of user interfaces in a way that integrating artificial intelligence capabilities has become crucial in the modern digital landscape. Exploring the main features and best practices for designing user interfaces for Web applications, which effectively support and leverage AI functionalities, is currently one of the relevant topics in this context. This research work discusses the fundamental principles of user interface (UI) design, and the challenges posed by the integration of AI into web applications. It emphasizes the need to strike a balance between the AI advanced capabilities and the users' ability to understand and control the system. Furthermore, the paper highlights the importance of creating intuitive and engaging UI designs that empower users to interact with AI-driven features effortlessly. The study presents a comprehensive analysis of various UI design techniques specifically tailored for AI-enabled web applications user interfaces. Additionally, the paper explores the incorporation of AI-driven recommendation systems, personalized interfaces, and adaptive designs, which dynamically adapt to users' preferences and behavior. To validate the proposed user interface design principles, the study presents a proposal for a guidelines structure that promotes empirical evaluations through user studies and usability testing. Results collected via a survey based on measuring the effectiveness and user satisfaction of AI-enabled Web interfaces. User interfaces in real-life scenarios are presented and provides information on the impact of UI design decisions on user interaction and overall experience. The outcomes of this research work contribute to a deeper understanding of UI design for AI-supported Web applications user interfaces and offer practical guidelines for designers and developers. By embracing the suggested principles, organizations and designers can create Web interfaces that effectively harness the power of AI while prioritizing user-centricity, accessibility, and ethical considerations.

Predicting the shear strength of rectangular RC beams strengthened with externally-bonded FRP composites using constrained monotonic neural networks

Fiber-reinforced polymer (FRP) composites bonded externally to reinforced concrete beams have shown promise for increasing shear load-carrying capacity. However, accurately predicting the shear strength contribution of FRP remains challenging due to the complexity of the shear failure mechanisms and interactions between the contributing components. Existing design provisions have limitations in modeling this behavior. Recently, machine learning has shown promise for addressing such complex problems but lacks transparency. Therefore, this study developed a data-driven Constrained Monotonic Neural Network (CMNN) approach implemented using Python libraries Keras and Tensorflow, which integrates domain knowledge to provide reliable FRP shear-strength predictions. This framework directly encodes expected monotonic input-output relationships through constraints, harnessing both predictive power and monotonicity guarantees. A comprehensive database of 273 beam tests was used to train and evaluate the model. Bayesian optimization through the Optuna framework and 4-fold cross-validation were used to tune the hyperparameters of the CMNN model. Extensive evaluation demonstrated the CMNN model's superior accuracy, yielding an R² value over 0.9, a CI above 0.93 and a low MAE under 16 kN on both training and unseen test data. The model also outperformed existing design code provisions, achieving significantly optimal performance metrics across various strengthening configurations. The analysis of the predictions revealed logical sensitivity trends aligned with the principles of shear mechanics. Such physics-informed machine learning presents a next-generation technique for performance-based structural design, contributing an effective and interpretable tool to advance knowledge on quantifying the shear capacity contribution of externally bonded FRP reinforcement in concrete beams.

Designing and Analysis of Multiplate Clutch by ANSYS Software

In this paper, the multi-plate clutch design process will be presented. As we know, the clutch is very useful in engaging and disengaging the vehicle's transmission with the engine. There are many types of clutches on the market. This study presents an in-depth analysis of the design of multi-plate wet clutches for automotive applications. It explores different design techniques, challenges and advances, focusing on aspects such as contact characteristics, thermal management, structural integrity and material selection. Additionally, the integration of multi-plate clutch systems with hybrid powertrains and the implications of the electrification trend are also explored. By addressing key aspects of multi-plate clutch design and identifying future research directions, this article aims to contribute to the advancement of automotive engineering, promoting innovations new to improve the performance, efficiency and durability of modern vehicles. The design proposed in this paper is rated to be 93% accurate. In our proposed system, the proposed design has a deformation of 0.0000187 mm and can withstand a maximum nominal stress of 0.20076 N/mm2,

Analysis of Gingival Fibroblasts Behaviour in the Presence of 3D-Printed versus Milled Methacrylate-Based Dental Resins-Do We Have a Winner?

Computer-aided design and computer-aided manufacturing (CAD/CAM) techniques are based on either subtractive (milling prefabricated blocks) or additive (3D printing) methods, and both are used for obtaining dentistry materials. Our in vitro study aimed to investigate the behavior of human gingival fibroblasts exposed to methacrylate (MA)-based CAD/CAM milled samples in comparison with that of MA-based 3D-printed samples to better elucidate the mechanisms of cell adaptability and survival. The proliferation of human gingival fibroblasts was measured after 2 and 24 h of incubation in the presence of these samples using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, and the membrane integrity was assessed through the lactate dehydrogenase release. The level of reactive oxygen species, expression of autophagy-related protein LC3B-I, and detection of GSH and caspase 3/7 were evaluated by fluorescence staining. The MMP-2 levels were measured using a Milliplex MAP kit. The incubation with MA-based 3D-printed samples significantly reduced the viability, by 16% and 28% from control after 2 and 24 h, respectively. There was a 25% and 55% decrease in the GSH level from control after 24 h of incubation with the CAD/CAM milled and 3D-printed samples, respectively. In addition, higher levels of LC3B-I and MMP-2 were obtained after 24 h of incubation with the MA-based 3D samples compared to the CAD/CAM milled ones. Therefore, our results outline that the MA-CAD/CAM milled samples displayed good biocompatibility during 24-h exposure, while MA-3D resins are proper for short-term utilization (less than 24 h).

A multi-objective optimization design method for underwater composite sandwich structures with high acoustic transmission and low vibration performance

ABSTRACT To address the issue of reduced structural integrity and optimality resulting from the conventional serial design of sonar dome structures, this paper establishes a multi-objective optimal design model for composite sandwich structures, utilising the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The model's stability and reliability are subsequently validated. Upon this model, a structural optimization design technique is postulated for composite sandwich structures within the realm of Fluid-Structure Interaction (FSI) multi-physics coupling. This approach aims to optimize the lightweight, high-rigidity composite sandwich structure, enhancing its acoustic transmission and minimizing vibration performance under turbulent pressure pulsations. The suggested method notably enhances the efficiency of structural design while ensuring precision. The proposed method effectively addresses the deficiency of serially designed sonar domes by neglecting the synergistic effects among multiple disciplines. In summary, this approach offers technical support and new research perspectives for the design of sonar domes under the coupling of multiple physical fields.

Optimization of the Educational Experience in Higher Education Using Predictive Artificial Intelligence Models

Aim: This study investigates the application of machine learning-based predictive models in university education, in order to improve student experience and satisfaction, evaluating the effectiveness of these tools in a modern educational context. Theoretical Framework: The research analyzes the strategic transformation in higher education, driven by digitalization and the evolving expectations of students and the labor market. The crucial role of AI-based predictive models in this change is explored. Methodology: A methodology combining opportunity identification and business case development in educational settings is employed. The approach focuses on business design and experimental machine learning techniques, emphasizing model accuracy, evaluation of the costs of inaccurate predictions, and ethics in data manipulation. Results: The predictive models achieved 95.7% accuracy in predicting student satisfaction, showing a significant positive correlation between teaching personalization and student satisfaction. These results highlight the ability of the models to influence educational decisions that improve the student experience and underscore their adaptability to specific learning needs, thus contributing to a more personalized and effective education. Discussion: The importance of balancing the adoption of advanced technologies with the maintenance of a student-centered pedagogical approach is emphasized. The methodology used to identify modeling heuristics highlights how strategic decisions can guide the technical development of AI models, ensuring that solutions are not only innovative, but also aligned with educational needs. Research Implications: The approach taken suggests that the application of predictive models has the potential to radically transform teaching and learning, aligning them with the demands of the digital future. Originality/Value: The proposed machine learning model is revealed as an effective tool to identify areas for improvement in university education. Its high accuracy in classifying students provides a unique perspective on how to improve educational quality and student satisfaction, emphasizing the importance of addressing individual needs for educational improvement.

Application of multi-objective optimization genetic algorithm to design terahertz metamaterials with fano resonances

In the prosperous development of terahertz (THz) metamaterials, Fano resonances have gained attention due to their potential applications in ultrasensitive systems. The performance of Fano resonance is directly influenced by the geometrical parameters of the element structure. However, the traditional design rules for Fano resonances in metamaterials rely on an empirical trial-and-error strategy, necessitating significant effort to achieve optimal results. To address this issue, we propose a design method in this study that utilizes the finite integration technique in time domain (FITD) along with a multi-objective optimization genetic algorithm for the intelligent design of metamaterial structures exhibiting the Fano resonance phenomenon. The FITD method is primarily used to calculate the Fano resonance with different metamaterial geometric structure parameters, while the genetic algorithm efficiently selects the optimal solution. Our method, characterized by high efficiency and complete independence from prior knowledge, could offer a new design technique for metamaterials with specific functions, thereby contributing to the development of THz applications.

Advancements and future prospects of Gallium Nitride (GaN) in semiconductor technology

Gallium Nitride (GaN) is a semiconductor material distinguished by its exceptional attributes and potential for diverse applications. Characterized by high electron mobility, substantial saturation drift velocity, an extensive forbidden energy band, and remarkable thermal stability, GaN demonstrates versatility in various electronic applications. These include high-frequency power amplifiers, luminous high-brightness Light Emitting Diodes (LEDs), and lasers. Advancements in synthesis techniques, such as gas-phase epitaxy, hydrothermal, and solution methods, have facilitated the production of high-quality GaN thin films. The research on GaNs physical properties encompasses an in-depth analysis of its structure, crystallographic defects, and optical characteristics, alongside investigations into the performance of optoelectronic devices. Future research trajectories are oriented towards developing scalable synthesis methodologies, designing and optimizing energy-efficient devices, and enhancing sustainable and eco-friendly attributes. Nonetheless, challenges persist in the realms of cost efficiency and reliability of GaN materials, necessitating further refinement in design and processing techniques and broadening the spectrum of potential applications. With no doubt, GaN materials exhibit significant potential for future development and are poised to assume a crucial role in the advancement of science and technology.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review.

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment. The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023. Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion. By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.