Computational Design Approach Research Articles

Computational Design and Virtual Reality Tools as an Effective Approach for Designing Optimization, Enhancement, and Validation of Islamic Parametric Elevation

Virtual reality was investigated with various computational design approaches to improve users’ ability to communicate, share, and grasp the design’s requirements to better conceptualize ideas during various design and review stages. The study aims to show how computational design and virtual reality are utilized to forecast challenges, address design problems/limitations in a specific study space, and validate results. A case study of the main Architectural Engineering department building at the University of Sharjah (UoS) campus in Sharjah, United Arab Emirates, was considered. The study focused on indoor daylight intake, ventilation, functionality, user comfortability, structural integrity, coherency and consistency, and performance optimization as factors to further evaluate and aid in the selection process of the optimal design. Consequently, innovative computational design tools were used in the study’s methodology to assess offered alternatives, such as altering and fabricating the building’s skin to deal with the challenges described above and improving the selected room’s visual and environmental conditions, such as optimal daylighting and ensuring users’ comfortability. The users’ immersive experience resulted in more accurate visualization and navigation around the to-be-built environment, allowing for more significant analysis and comprehension that further validated the results obtained. The chosen case study thus demonstrated the potential for computational design, mixed reality techniques, and strategies to enable an efficient process that ultimately verifies approaches taken toward a much more optimal solution through better visualization and contextualizing.

Computer-Assisted Design of Peptide-Based Radiotracers.

In medical imaging, techniques such as magnetic resonance imaging, contrast-enhanced computerized tomography, positron emission tomography (PET), and single-photon emission computed tomography (SPECT) are extensively available and routinely used for disease diagnosis. PET probes with peptide-based targeting are typically composed of small peptides especially developed to have high affinity and specificity for a range of cellular and tissue targets. These probes' key benefits include being less expensive than traditional antibody-based PET tracers and having an effective chemical modification process that allows them to be radiolabeled with almost any radionuclide, making them highly appealing for clinical usage. Currently, as with every pharmaceutical design, the use of in silico strategies is steadily growing in this field, even though it is not part of the standard toolkit used during radiopharmaceutical design. This review describes the recent applications of computational design approaches in the design of novel peptide-based radiopharmaceuticals.

COMPUTATIONAL DESIGN FOR FUTURISTIC ENVIRONMENTALLY ADAPTIVE BUILDING FORMS AND STRUCTURES

Introduction: With the rapid development in computational design, both architectural design and representation processes have witnessed a revolutionary change from the analog to the digital medium, opening new doors for adaptability in the architectural design process by leveraging nature concepts in design. The computational design approach starts with the mathematical model definition based on numerical relations and equations, thus, replacing the standard visual representation. Purpose of the study: We aimed to integrate computational design technologies to create self-learning buildings that could adapt to environmental challenges and adjust accordingly by collecting data from the surrounding environment via the implementation of sensors. Methods: We started with extensive research on state-of-the-art computational design in architecture, followed by the design implementation and the implementation of the architectural design of a building. The design followed a parametric approach to design and strategies. An algorithm was developed with Grasshopper Scripting to generate documents that mimic the growth process of cellular bone structures and adapt that form to a selected project site. To ensure that the generated form is adaptable, we performed multiple analyses, such as sunlight, radiation, and shadow analysis, before selecting the form and finishing its development. The results show that an environmentally responsive form that extends from the surrounding environment is characterized by high levels of adaptability. Results: In the course of the study, the effectiveness of computational design technologies in architecture was established.

AFFORDABLE COMPUTATION FOR ARCHITECTURE

Current architectural requirements prioritize the need to minimize the ecological footprint. By taking advantage of computational design approaches like Algorithmic Design (AD), architects can enhance their design processes with analysis, optimization, and visualization mechanisms, which are critical to explore design solutions that meet this need. However, these mechanisms are also highly time- and resource-consuming, often implying a quality tradeoff or the acquisition of High-Performance Computing (HPC) machines. The latter are not yet affordable for most design studios but, fortunately, they can be contracted as a service. This paper evaluates the impact of computation as a service in architecture and, more specifically, the remote use of HPC for AD, with the aim of reducing the time and costs associated with computationally expensive processes. A set of experiments were made involving analysis, optimization, and rendering of a selected case study. Results indicate that HPC services are advantageous, particularly when performing embarrassingly parallelizable tasks such as rendering. However, some challenges remain, namely the required expertise.

Anti-viral drug discovery against monkeypox and smallpox infection by natural curcumin derivatives: A Computational drug design approach.

In the last couple of years, viral infections have been leading the globe, considered one of the most widespread and extremely damaging health problems and one of the leading causes of mortality in the modern period. Although several viral infections are discovered, such as SARS CoV-2, Langya Henipavirus, there have only been a limited number of discoveries of possible antiviral drug, and vaccine that have even received authorization for the protection of human health. Recently, another virial infection is infecting worldwide (Monkeypox, and Smallpox), which concerns pharmacists, biochemists, doctors, and healthcare providers about another epidemic. Also, currently no specific treatment is available against Monkeypox. This research gap encouraged us to develop a new molecule to fight against monkeypox and smallpox disease. So, firstly, fifty different curcumin derivatives were collected from natural sources, which are available in the PubChem database, to determine antiviral capabilities against Monkeypox and Smallpox. Preliminarily, the molecular docking experiment of fifty different curcumin derivatives were conducted, and the majority of the substances produced the expected binding affinities. Then, twelve curcumin derivatives were picked up for further analysis based on the maximum docking score. After that, the density functional theory (DFT) was used to determine chemical characterizations such as the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), softness, and hardness, etc. The mentioned derivatives demonstrated docking scores greater than 6.80 kcal/mol, and the most significant binding affinity was at -8.90 kcal/mol, even though 12 molecules had higher binding scores (-8.00 kcal/mol to -8.9 kcal/mol), and better than the standard medications. The molecular dynamic simulation is described by root mean square deviation (RMSD) and root-mean-square fluctuation (RMSF), demonstrating that all the compounds might be stable in the physiological system. In conclusion, each derivative of curcumin has outstanding absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics. Hence, we recommended the aforementioned curcumin derivatives as potential antiviral agents for the treatment of Monkeypox and Smallpox virus, and more in vivo investigations are warranted to substantiate our findings.

Computational design of clinical trials using a combination of simulation and the genetic algorithm.

Artificial intelligence (AI) has come to be used in various technological fields in recent years. However, there have been no reports of AI-designed clinical trials. In this study, we tried to develop study designs by a genetic algorithm (GA), which is an AI solution for combination optimization problems. Specifically, the computational design approach was applied to optimize the blood sampling schedule for a bioequivalence (BE) study in pediatrics and optimize the allocation of dose groups for a dose-finding study. The GA could reduce the number of blood collection points from 15 (typical standard) to seven points without meaningful impact on the accuracy and precision of the pharmacokinetic estimation for the pediatric BE study. For the dose-finding study, up to 10% reduction of the total number of required subjects from the standard design could be achieved. The GA also created a design that would lead to a drastic reduction of the required number of subjects in the placebo arm while keeping the total number of subjects at a minimum level. These results indicated the potential usefulness of the computational clinical study design approach for innovative drug development.

Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease.

Cardiomyopathy is associated with structural and functional abnormalities of the ventricular myocardium and can be classified in two major groups: hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Computational modeling and drug design approaches can speed up the drug discovery and significantly reduce expenses aiming to improve the treatment of cardiomyopathy. In the SILICOFCM project, a multiscale platform is developed using coupled macro- and microsimulation through finite element (FE) modeling of fluid-structure interactions (FSI) and molecular drug interactions with the cardiac cells. FSI was used for modeling the left ventricle (LV) with a nonlinear material model of the heart wall. Simulations of the drugs' influence on the electro-mechanics LV coupling were separated in two scenarios, defined by the principal action of specific drugs. We examined the effects of Disopyramide and Dygoxin which modulate Ca2+ transients (first scenario), and Mavacamten and 2-deoxy adenosine triphosphate (dATP) which affect changes of kinetic parameters (second scenario). Changes of pressures, displacements, and velocity distributions, as well as pressure-volume (P-V) loops in the LV models of HCM and DCM patients were presented. Additionally, the results obtained from the SILICOFCM Risk Stratification Tool and PAK software for high-risk HCM patients closely followed the clinical observations. This approach can give much more information on risk prediction of cardiac disease to specific patients and better insight into estimated effects of drug therapy, leading to improved patient monitoring and treatment.

Decoupling peptide binding from T cell receptor recognition with engineered chimeric MHC-I molecules.

Major Histocompatibility Complex class I (MHC-I) molecules display self, viral or aberrant epitopic peptides to T cell receptors (TCRs), which employ interactions between complementarity-determining regions with both peptide and MHC-I heavy chain 'framework' residues to recognize specific Human Leucocyte Antigens (HLAs). The highly polymorphic nature of the HLA peptide-binding groove suggests a malleability of interactions within a common structural scaffold. Here, using structural data from peptide:MHC-I and pMHC:TCR structures, we first identify residues important for peptide and/or TCR binding. We then outline a fixed-backbone computational design approach for engineering synthetic molecules that combine peptide binding and TCR recognition surfaces from existing HLA allotypes. X-ray crystallography demonstrates that chimeric molecules bridging divergent HLA alleles can bind selected peptide antigens in a specified backbone conformation. Finally, in vitro tetramer staining and biophysical binding experiments using chimeric pMHC-I molecules presenting established antigens further demonstrate the requirement of TCR recognition on interactions with HLA framework residues, as opposed to interactions with peptide-centric Chimeric Antigen Receptors (CARs). Our results underscore a novel, structure-guided platform for developing synthetic HLA molecules with desired properties as screening probes for peptide-centric interactions with TCRs and other therapeutic modalities.

Computational design of efficient near-infrared TADF emitters with hot-exciton characteristics

Our study presents a computational design approach for developing efficient near-infrared (NIR) organic emitters with hot-exciton characteristics.

Design for multi-material 4D printing: Development of an algorithm for interlocking blocks assembly generation

4D printing involves smart materials (SMs) that can respond to stimulation energy by changing their shape/property. Current advances in digital materials require addressing heterogeneous (multi-material deposition) 4D printing to attain targeted shape-changes. However, innovative structures embedding inert and SMs simultaneously are yet difficult to build. To overcome this challenge, we propose a genuine computational design approach addressed to heterogeneous additive manufacturing (AM). The main idea is to resort to an efficient algorithm capable of converting a computed digital multi-material distribution in a voxel-based 3D/4D structure to interlocking blocks intended to be printed separately with different AM techniques and then assembled.

Identification of potential death-associated protein kinase-1 (DAPK1) inhibitors by an integrated ligand-based and structure-based computational drug design approach

Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine kinase that is abundantly expressed in the memory- and cognition-related brain areas. DAPK1 is associated with several pathological hallmarks of Alzheimer’s disease (AD); it is an attractive target for designing a novel DAPK1 inhibitor as an effective therapeutic treatment for AD. In the present study, we have used an integrated ligand-based and structure-based drug design method to identify DAPK1 inhibitors. The pharmacophoric features of compound 38 G (PDB ID 4TXC) were mapped, and the models were evaluated using enrichment factor (EF) and goodness of hit (GH) score. The selected models were used to screen Zinc 15 compounds library. The identified hits were passed through drug-likeliness and PAINS filtering. The docking study was performed in three steps to yield molecules with good binding energy and ligand-target interactions. Finally, three hits were obtained, that is, ZINC000020648330, ZINC000006755051 and ZINC000020650468, which were subjected to rigorous molecular dynamics simulation. All three hits exhibited optimal stability under simulated conditions and low predicted toxicity. Communicated by Ramaswamy H. Sarma

Computationally-guided design and selection of high performing ribosomal active site mutants.

Understanding how modifications to the ribosome affect function has implications for studying ribosome biogenesis, building minimal cells, and repurposing ribosomes for synthetic biology. However, efforts to design sequence-modified ribosomes have been limited because point mutations in the ribosomal RNA (rRNA), especially in the catalytic active site (peptidyl transferase center; PTC), are often functionally detrimental. Moreover, methods for directed evolution of rRNA are constrained by practical considerations (e.g. library size). Here, to address these limitations, we developed a computational rRNA design approach for screening guided libraries of mutant ribosomes. Our method includes in silico library design and selection using a Rosetta stepwise Monte Carlo method (SWM), library construction and in vitro testing of combined ribosomal assembly and translation activity, and functional characterization in vivo. As a model, we apply our method to making modified ribosomes with mutant PTCs. We engineer ribosomes with as many as 30 mutations in their PTCs, highlighting previously unidentified epistatic interactions, and show that SWM helps identify sequences with beneficial phenotypes as compared to random library sequences. We further demonstrate that some variants improve cell growth in vivo, relative to wild type ribosomes. We anticipate that SWM design and selectionmay serve as a powerful tool for rRNA engineering.

Protein re-surfacing of E. coli L-Asparaginase to evade pre-existing anti-drug antibodies and hypersensitivity responses.

The optimal use of many biotherapeutics is restricted by Anti-drug antibodies (ADAs) and hypersensitivity responses which can affect potency and ability to administer a treatment. Here we demonstrate that Re-surfacing can be utilized as a generalizable approach to engineer proteins with extensive surface residue modifications in order to avoid binding by pre-existing ADAs. This technique was applied to E. coli Asparaginase (ASN) to produce functional mutants with up to 58 substitutions resulting in direct modification of 35% of surface residues. Re-surfaced ASNs exhibited significantly reduced binding to murine, rabbit and human polyclonal ADAs, with a negative correlation observed between binding and mutational distance from the native protein. Reductions in ADA binding correlated with diminished hypersensitivity responses in an in vivo mouse model. By using computational design approaches to traverse extended distances in mutational space while maintaining function, protein Re-surfacing may provide a means to generate novel or second line therapies for life-saving drugs with limited therapeutic alternatives.

Inverse Design of Pore Wall Chemistry To Control Solute Transport and Selectivity.

Next-generation membranes for purification and reuse of highly contaminated water require materials with precisely tuned functionality to address key challenges, including the removal of small, charge-neutral solutes. Bioinspired multifunctional membrane surfaces enhance transport properties, but the combinatorically large chemical space is difficult to navigate through trial and error. Here, we demonstrate a computational inverse design approach to efficiently identify promising materials and elucidate design rules. We develop a combined evolutionary optimization, machine learning, and molecular simulation workflow to spatially design chemical functional group patterning in a model nanopore that enhances transport of water relative to solutes. The genetic optimization discovers nonintuitive functionalization strategies that hinder the transport of solutes through the pore, simply by patterning hydrophobic methyl and hydrophilic hydroxyl functional groups. Examining these patterns, we demonstrate that they exploit an unexpected diffusive solute hopping mechanism. This inverse design procedure and the identification of novel molecular mechanisms for pore chemical heterogeneity to impact solute selectivity demonstrate new routes to the design of membrane materials with novel functionalities. More broadly, this work illustrates how chemical design is a powerful strategy to modulate water-mediated surface-solute interactions in complex, soft material systems that are relevant to diverse technologies.

Framework of an algorithm-aided BIM approach for modular residential building information models

The digital transformation of the construction industry and the lack of integration of digital technologies in design and construction processes are the motivation for this research. BIM solutions enable new levels of design processes and provide platforms for computational design and novel approaches in the AEC industry. In computational design parametric, generative or algorithmic procedures are utilized to support, optimize, or replace manual processes. The combination of BIM and generative, parametric or algorithmic design forms a hybrid that aims to combine the advantages of both concepts and allows for generative design processes with the creation of BIM objects containing metadata. Along with the digital transformation and novel approaches in the AEC industry, modular construction aims to shift from mass production to mass customization and maximize opportunities for cost-effective, economical, and sustainable buildings. This paper addresses the approach of generating building information models using algorithm-aided design combined with BIM at an early design stage for modular multi-story residential buildings that are affordable and sustainable. In this study, we present the framework of an algorithm-aided BIM approach, from the concept of the generative algorithm to the evaluation approach and the proof of concept as the test of the framework.

Computational approach for form-finding optimal design

In this paper, an optimization strategy for a canopy, based on computational modelling approaches is presented. The design approach is applied to a realistic roof structure of an ecological island (waste collection centre) and has been completely redesigned with the aid of a Genetic Algorithm and a Dynamic Relaxation Algorithm. The preliminary design of the roof structure can be formulated as a shape optimization problem, involving functional needs and constraints at different scales of the structure. The proposed hypothesis solution was studied by using an optimization procedure through algorithms in the software Rhinoceros3D®/Grasshopper®. The main aim of this work is to explore different modelling approaches for form-finding that can be built from the use of numerical simulations based on algorithms. To this aim, the need to meet various requirements (structural, functional, formal) involving a team of architects and engineers can be interpreted as a matter of structural optimization.

Orbital Occupancy and Spin Polarization: From Mechanistic Study to Rational Design of Transition Metal-Based Electrocatalysts toward Energy Applications.

Over the past few decades, development of electrocatalysts for energy applications has extensively transitioned from trial-and-error methodologies to more rational and directed designs at the atomic levels via either nanogeometric optimization or modulating electronic properties of active sites. Regarding the modulation of electronic properties, nonprecious transition metal-based materials have been attracting large interest due to the capability of versatile tuning d-electron configurations expressed through the flexible orbital occupancy and various possible degrees of spin polarization. Herein, recent advances in tailoring electronic properties of the transition-metal atoms for intrinsically enhanced electrocatalytic performances are reviewed. We start with discussions on how orbital occupancy and spin polarization can govern the essential atomic level processes, including the transport of electron charge and spin in bulk, reactive species adsorption on the catalytic surface, and the electron transfer between catalytic centers and adsorbed species as well as reaction mechanisms. Subsequently, different techniques currently adopted in tuning electronic structures are discussed with particular emphasis on theoretical rationale and recent practical achievements. We also highlight the promises of the recently established computational design approaches in developing electrocatalysts for energy applications. Lastly, the discussion is concluded with perspectives on current challenges and future opportunities. We hope this review will present the beauty of the structure-activity relationships in catalysis sciences and contribute to advance the rational development of electrocatalysts for energy conversion applications.

A Computational QSAR, Molecular Docking and In Vitro Cytotoxicity Study of Novel Thiouracil-Based Drugs with Anticancer Activity against Human-DNA Topoisomerase II.

Computational chemistry, molecular docking, and drug design approaches, combined with the biochemical evaluation of the antitumor activity of selected derivatives of the thiouracil-based dihydroindeno pyrido pyrimidines against topoisomerase I and II. The IC50 of other cell lines including the normal human lung cell line W138, lung cancer cell line, A549, breast cancer cell line, MCF-7, cervical cancer, HeLa, and liver cancer cell line HepG2 was evaluated using biochemical methods. The global reactivity descriptors and physicochemical parameters were computed, showing good agreement with the Lipinski and Veber’s rules of the drug criteria. The molecular docking study of the ligands with the topoisomerase protein provides the binding sites, binding energies, and deactivation constant for the inhibition pocket. Various biochemical methods were used to evaluate the IC50 of the cell lines. The QSAR model was developed for colorectal cell line HCT as a case study. Four QSAR statistical models were predicted between the IC50 of the colorectal cell line HCT to correlate the anticancer activity and the computed physicochemical and quantum chemical global reactivity descriptors. The predictive power of the models indicates a good correlation between the observed and the predicted activity.

A computational design approach for multi-material 4D printing based on interlocking blocks assembly

4D printing is considered as a disruptive manufacturing technology for creating innovative devices able to evolve in their usage environment. By coupling additive manufacturing (AM) processes and active/passive materials under the effect of an energy stimulation, objects can change properties, shapes, or even functionalities. To achieve a desired shape-change, recent advances in computational design around digital materials require tackling multi-material 4D printing. However, the deposition of active and passive materials in a single run remains difficult due to the different AM techniques required. To overcome this limitation in the context of complex materials distributions, an original approach is to address multi-material 4D printing from the perspective of interlocking blocks assembly. Therefore, the objective of the paper is to propose a computational design approach that converts a multi-material 4D object with a computed digital materials distribution into suitable interlocking blocks. The latter can be printed separately using single material AM and then assembled to achieve the targeted shape change. An implementation of the approach via a dedicated plugin within a computer-aided design environment is presented. A case study is introduced to illustrate the relevance and the applicability of the proposed approach.

Design and characterization of a cobalt-free stainless maraging steel for laser-based powder bed fusion

This study presents a new Co-free stainless maraging variant for laser-based powder bed fusion developed using a computational alloy design approach. The goal was to develop an easily printable material with similar performance to 18Ni-300. After screening numerous compositions, Fe-13.2Cr-9.1Ni-1.1Al-0.6Mo-0.5Nb-0.23Ti-0.5Mn-0.5Si (wt.%) was selected. This composition showed excellent printability with low porosity levels. The precipitation strengthening response was evaluated by aging at 500 °C for 15 min, 3 h and 18 h, measuring hardness, tensile strength, and by characterization using atom probe tomography. After 15 min of aging, 90% of the maximum hardness was reached, thanks to formation of (Ni, Al, Nb, Ti, Mn, Si) clusters with a density of 1.5 × 1024 m-3. Between 15 min and 3 h, distinct precipitates formed with a radius of ∼1.4 nm. The precipitates underwent a splitting phenomenon after 18 h, forming several unique Ni-rich precipitates including Ni16Si7(Ti, Nb)6 and Ni3(Al, Ti, Nb, Si). The splitting can be a reason for the slow coarsening rate, as the average precipitate radius after 18 h was only 2 nm. Simulations of the precipitation sequence using PRISMA indicated very rapid and dense precipitation of L12-Ni3X precipitates with a slow coarsening rate, in agreement with experimental observations.