Computational Design Research Articles

Retraction Note: Collaborative communication and computational design for energy-efficient edge based learning network

Retraction Note: Collaborative communication and computational design for energy-efficient edge based learning network

Phononic origin of the infrared dielectric properties of RE2O3 (RE = Y, Gd, Ho, Lu) compounds

Understanding the phononic origin of the infrared (IR) dielectric properties of yttria (Y2O3) and other rare-earth sesquioxides (RE2O3) is a fundamental task in the search of appropriate RE2O3 materials that serve particular IR optical applications. We herein investigate the IR dielectric properties of RE2O3 (RE = Y, Gd, Ho, Lu) using density functional theory-based phonon calculations and Lorentz oscillator model. The abundant IR-active optical phonon modes that are available for effective absorption of photons result in high reflectance of RE2O3, among which four IR-active modes originated from large distortions of REO6 octahedra are found to contribute dominantly to the phonon dielectric constants. Particularly, the present calculation method by considering one-phonon absorption process is demonstrated with good reliability in predicting the IR dielectric parameters of RE2O3 at the far-IR as well as the vicinity of mid-IR region, and the potential cutoff frequency/wavelength of its applicability is disclosed as characterized by the maximum frequency of IR-active longitudinal phonon modes. The results deepen the understanding on IR dielectric properties of RE2O3, and aid the computational design of materials with appropriate IR properties.

Digital twin of surface acoustic wave transceivers for a computational design of an optimal wave guiding layer thickness

AbstractDetection of biomarkers is exploited in lab-on-a-chip devices by means of Love type Surface Acoustic Waves (SAW). Finger type arrangement of electrodes, used for InterDigital-Transducers (IDT), perform well to create and detect SAW by using electro-mechanical coupling. Efficiency of such a transceiver depends on design parameters such as chosen material orientation, thickness, placement of electrodes. An optimized design reduces production costs, hence, we need a digital twin of the device with multiphysics simulations that compute deformation and electric field. In this study, we develop a framework with the open-source package called FEniCS for modal and transient analyses of IDTs by using the Finite Element Method (FEM). Specifically, we discuss all possible sensor design parameters and propose a computational design guideline that determines the “best” thickness parameter by maximizing mass sensitivity, thus, efficiency for a Love surface acoustic wave sensor.

Computational design of non-porous pH-responsive antibody nanoparticles

AbstractProgramming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.

Computer-Aided Design of Novel Active Components in Plant Protection

The production demands highly specific environmentally and toxicologically acceptable plant protection products are increasing. Computer-aided molecular design of new active components has a great deal in developing plant protection products to avoid that long-lasting and expensive process. Computational design of future compounds and their synthesis, evaluation of their effectiveness on harmful and beneficial organisms in the soil, as well as detailed research mechanism of action at the molecular level, represents an initial stage in the long-lasting and expensive process of plant protection products. In this paper, the recent advances in quantitative structure-activity relationship (QSAR) studies, molecular docking, and calculation of “Pesticidelikeness properties “, as well, have been reviewed. QSAR models for antifungal activities against phytopathological fungi were obtained for the thiazoline and coumarine derivatives, coumarinyl Schiff bases, and coumarin-1,2,4-triazoles. A molecular docking study revealed that antifungal activities of fluorinated pyrazole aldehydes are related to the inhibition of proteinase K, coumarinyl Schiff bases with endoglucanase and pectinase, hybrids of coumarins and 1,2,4-triazoles with sterol 14α-demethylase inhibition, 3-gydroxycoumarin chitin synthase, while γ-thionins strongly binds to fungal membrane moieties.

Engineering and evaluation of FXa bypassing agents that restore hemostasis following Apixaban associated bleeding

Direct oral anticoagulants (DOACs) targeting activated factor Xa (FXa) are used to prevent or treat thromboembolic disorders. DOACs reversibly bind to FXa and inhibit its enzymatic activity. However, DOAC treatment carries the risk of anticoagulant-associated bleeding. Currently, only one specific agent, andexanet alfa, is approved to reverse the anticoagulant effects of FXa-targeting DOACs (FXaDOACs) and control life-threatening bleeding. However, because of its mechanism of action, andexanet alfa requires a cumbersome dosing schedule, and its use is associated with the risk of thrombosis. Here, we present the computational design, engineering, and evaluation of FXa-variants that exhibit anticoagulation reversal activity in the presence of FXaDOACs. Our designs demonstrate low DOAC binding affinity, retain FXa-enzymatic activity and reduce the DOAC-associated bleeding by restoring hemostasis in mice treated with apixaban. Importantly, the FXaDOACs reversal agents we designed, unlike andexanet alfa, do not inhibit TFPI, and consequently, may have a safer thrombogenic profile.

Computational Design of Cyclic Peptide Inhibitors of a Bacterial Membrane Lipoprotein Peptidase.

There remains a critical need for new antibiotics against multi-drug-resistant Gram-negative bacteria, a major global threat that continues to impact mortality rates. Lipoprotein signal peptidase II is an essential enzyme in the lipoprotein biosynthetic pathway of Gram-negative bacteria, making it an attractive target for antibacterial drug discovery. Although natural inhibitors of LspA have been identified, such as the cyclic depsipeptide globomycin, poor stability and production difficulties limit their use in a clinical setting. We harness computational design to generate stable de novo cyclic peptide analogues of globomycin. Only 12 peptides needed to be synthesized and tested to yield potent inhibitors, avoiding costly preparation of large libraries and screening campaigns. The most potent analogues showed comparable or better antimicrobial activity than globomycin in microdilution assays against ESKAPE-E pathogens. This work highlights computational design as a general strategy to combat antibiotic resistance.

Computational design of cooperatively acting molecular catalyst systems: carbene based tungsten- or molybdenum-catalysts with rhodium- or iridium-complexes for the ionic hydrogenation of N2 to NH3.

This density functional theory (DFT) study explores the efficacy of cooperative catalytic systems in enabling the ionic hydrogenation of N2 with H2, leading to NH3 formation. A set of N-heterocyclic carbene-based pincer tungsten/molybdenum metal complexes of the form [(PCP)M1(H)2] (M1 = W/Mo) were chosen to bind N2 at the respective metal centres. Simultaneously, cationic rhodium/iridium complexes of type [Cp*M2{2-(2-pyridyl)phenyl}(CH3CN)]+ (Cp* = C5(CH3)5 and M2 = Rh/Ir), are employed as cooperative coordination partners for heterolytic H2 splitting. The stepwise transfer of protons and hydrides to the bound N2 and intermediate NxHy units results in the formation of NH3. Interestingly, the calculated results reveal an encouraging low range of energy spans ranging from ∼30 to 42 kcal mol-1 depending on different combinations of ligands and metal complexes. The optimal combination of pincer ligand and metal center allowed for an energy span of unprecedented 29.7 kcal mol-1 demonstrating significant potential for molecular catalysts for the N2/H2 reaction system. While exploring obvious potential off-cycle reactions leading to catalyst deactivation, the computed results indicate that no increase in energy span would need to be expected.

A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings

Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodology in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention.

PARAMETRIC MODELING AS AN INNOVATIVE APPROACH IN GRAPHIC DESIGN

Purpose. The article focuses on exploring the possibilities and strategies of innovative integration of parametricism into the field of graphic design and analyzing the main problems and potential solutions in using parametric design in creating graphic objects. Methodology. According to the research topic, the methods employed include comparative study, theoretical analysis, and synthesis. Results. The article discusses the definitions and characteristics of parametric design, examines its applications for optimizing design thinking and creating personalized objects. Various levels of innovative implementations are presented, offering several strategies for integrating parametric modeling into graphic design, including visual element generation, layout optimization, design interactivity enhancement, and data visualization in personalized design. Additionally, integration challenges related to the use of artificial intelligence technologies are discussed. Scientific novelty. The article analyzes various levels and effective strategies for applying parametric modeling in the field of graphic design, exploring current aspects of interaction that are innovative and emphasizing the importance of utilizing parametric design methodology, artificial intelligence, and computational design in the paradigm of contemporary graphic design. Practical significance. The materials presented in the article can be utilized for investigating the innovative application of digital design, parametric design, and artificial intelligence in the field of graphic design.

Computational design, docking, and molecular dynamics simulation study of RNA helicase inhibitors of dengue virus.

RNA viruses are complex pathogens in terms of their genetic makeup, mutation frequency, and transmission modes. They contain the RNA helicase enzyme, which plays a crucial role in the viral genome replication process. This work aims to develop and screen a potential molecule that could function as a dengue virus (DENV) RNA helicase inhibitor. The present study was performed by taking 26 potential derivatives of gedunin phytochemicals from the PubChem database as ligands. The binding study of the compounds were analyzed by in silico docking method considering DENV RNA helicase enzyme as the receptor. After a thorough analysis of the docking scores, toxicity, and physicochemical properties, compound tetrahydrogedunin was obtained as the best. Based on tetrahydrogedunin molecular structure, 100 drug-like molecules were designed using the Data Warrior tool. After the screening process for drug-likeness and ADMET properties, the derivative number 42 was considered as the promising. Further comparative docking of derivative 42 and a standard inhibitor molecule ST-610 with DENV RNA helicase enzyme showed binding affinity as -10.0 kcal/mol and -9.6 kcal/mol, respectively. The favorable interaction between DENV RNA helicase and derivative 42 was further validated by 50 nanoseconds molecular dynamics simulation and MM-GBSA analysis. Since the antiviral activity of derivative 42 has not been reported till date, the compound was predicted as a novel therapeutic molecule that can act against the dengue virus (DENV) RNA helicase enzyme.

Abstract PO5-27-05: Computational design and validation of a novel peptide-drug conjugate for treatment of triple negative breast cancer

Abstract Sortilin (SORT1) is a member of the vacuolar protein sorting 10 protein (Vps10p) family that functions as a receptor regulating peptide and protein trafficking between the plasma membrane, lysosomes, and trans-golgi network. As a cell surface receptor, SORT1 is able to mediate efficient endocytosis of extracellular ligands to the lysosomal compartment. Numerous reports have identified enriched SORT1 expression in a variety of tumor types, including triple-negative breast cancer (TNBC), a subtype of breast cancer associated with aggressive clinical behavior and poor disease outcomes. We sought to exploit SORT1-dependent internalization of peptides as a platform for rapid and specific chemotherapy delivery into TNBC cells. Using ProteinStudio (our proprietary computation-enabled design capabilities), we generated high-affinity SORT1 targeting peptides that exhibit efficient receptor dependent internalization and lysosomal localization. Alternative computational approaches such as AlphaFold2 failed to recapitulate the peptide design. Peptide drug conjugates (PDCs) were generated via a linkage strategy that combines our designed peptides to the antimitotic agent monomethyl auristatin E (MMAE). Our PDC molecules exhibit potent tumor regression in a MDA-MB-231 TNBC cell derived xenograft model, thereby highlighting the potential of SORT1-engaging PDCs as an efficacious targeted chemotherapeutic delivery strategy. Citation Format: Francine Liu, Andrew Zhai, Ozge Yoluk, Aron Broom, Tracy Stone, Glenn Butterfoss, Serban Popa, Tianyu Li, Lucas Siow, Christopher Ing, David White. Computational design and validation of a novel peptide-drug conjugate for treatment of triple negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-27-05.

Development of Inhibitory Compounds for Metallo-beta-lactamase through Computational Design and Crystallographic Analysis.

Metallo-β-lactamases (MBL) deactivate β-lactam antibiotics through a catalytic reaction caused by two zinc ions at the active center. Since MBLs deteriorate a wide range of antibiotics, they are dangerous factors for bacterial multidrug resistance. In this work, organic synthesis, computational design, and crystal structure analysis were performed to obtain potent MBL inhibitors based on a previously identified hit compound. The hit compound comprised 3,4-dihydro-2(1H)-quinolinone linked with a phenyl-ether-methyl group via a thiazole ring. In the first step, the thiazole ring was replaced with a tertiary amine to avoid the planar structure. In the second step, we virtually modified the compound by keeping the quinolinone backbone. Every modified compound was bound to a kind of MBL, imipenemase-1 (IMP-1), and the binding pose was optimized by a molecular mechanics calculation. The binding scores were evaluated for the respective optimized binding poses. Given the predicted binding poses and calculated binding scores, candidate compounds were determined for organic syntheses. The inhibitory activities of the synthesized compounds were measured by an in vitro assay for two kinds of MBLs, IMP-1 and New Delhi metallo-β-lactamase (NDM-1). A quinolinone connected with an amine bound with methyl-phenyl-ether-propyl and cyclohexyl-ethyl showed a 50% inhibitory concentration of 4.8 μM. An X-ray crystal analysis clarified the binding structure of a synthesized compound to IMP-1. The δ-lactam ring of quinolinone was hydrolyzed, and the generated carboxyl group was coordinated with zinc ions. The findings on the chemical structure and binding pose are expected to be a base for developing MBL inhibitors.

Joint In-Orbit Computation and Communication for Minimizing Download Time From LEO Satellites

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

Automated optimization of the solubility of a hyper-stable α-amylase.

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

Machine Hands on Flaws to Machine: The Surprising Sources of Biases in Machine Learning Models

Machine Hands on Flaws to Machine: The Surprising Sources of Biases in Machine Learning Models

Data-guided for discovering high-strength, cost-effective, and low-carbon rice husk ash concrete

This study aims to present a data-guided intelligent design framework for discovering high-strength, cost-effective, and low-carbon rice husk ash concrete (RHC). First, gradient boosting (GB) models were developed to predict compressive strength of RHC based on a dataset with 1150 samples. The CatBoost model outperforms XGBoost and LightGBM models on the test set, and its determination of coefficient (R2), root mean squared error (RMSE), and mean absolute error (MAE) are 0.984, 3.270 MPa, and 2.290 MPa respectively. With the best prediction model, SHapley Additive exPlanations (SHAP) was utilized to shed light on the model outputs. The curing age is the most dominant features effecting the compressive strength of RHC, and the next two are the contents of cement and water. Then, a computational design framework was proposed by integrating the machine learning-based model and the evolutionary algorithm. Considering compressive strength, materials cost, embodied carbon, and embodied energy, the multi-objective optimization was conducted leveraging the improved adaptive geometry estimation based many-objective evolutionary algorithm (AGE-MOEA II). The optimal solutions of the different design scenarios were achieved using the multi-criteria decision-making method. The research can provide a theoretical reference for the capability of developing RHC mixture using data-guided approachesto promote the sustainable development of concrete industry.

Paradigm Shift in Architectural Pedagogy incorporating Artificial Intelligence

Abstract: The paradigm shift in architectural pedagogy encompasses various facets, each contributing to a holistic transformation of the educational landscape. From computational design methodologies to generative algorithms, AI empowers students to explore a vast array of design possibilities, pushing the boundaries of creativity and problem-solving. The goal of this study is to understand the paradigm shifts in the constantly changing fields of architectural education with the incorporation of Artificial Intelligence (AI). This change transforms architectural teaching fundamentally in addition to redefining architectural practice approaches. Now that we are at the crossroads of technology innovation and human creativity, it is critical to investigate how artificial intelligence (AI) might transform architecture education and promote creativity, sustainability, and adaptation. The primary exploration reveals a significant gap in the current teaching methods, with little emphasis on AI. The study emphasizes the urgency of incorporating certain approaches into architectural education along with AI which would enhance learning experience of the students.

A Joint Communication and Computation Design for Probabilistic Semantic Communications

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme.

Understanding and computational design of genetic circuits of metabolic networks.

The expression of metabolic proteins is controlled by genetic circuits, matching metabolic demands and changing environmental conditions. Ideally, this regulation brings about a competitive level of metabolic fitness. Understanding how cells can achieve a robust (close-to-optimal) functioning of metabolism by appropriate control of gene expression aids synthetic biology by providing design criteria of synthetic circuits for biotechnological purposes. It also extends our understanding of the designs of genetic circuitry found in nature such as metabolite control of transcription factor activity, promoter architectures and transcription factor dependencies, and operon composition (in bacteria). Here, we review, explain and illustrate an approach that allows for the inference and design of genetic circuitry that steers metabolic networks to achieve a maximal flux per unit invested protein across dynamic conditions. We discuss how this approach and its understanding can be used to rationalize Escherichia coli's strategy to regulate the expression of its ribosomes and infer the design of circuitry controlling gene expression of amino-acid biosynthesis enzymes. The inferred regulation indeed resembles E. coli's circuits, suggesting that these have evolved to maximize amino-acid production fluxes per unit invested protein. We end by an outlook of the use of this approach in metabolic engineering applications.