Computer-aided Design Methods Research Articles

Classifying Digital Medical Images for Breast Cancer Prediction Using Machine Learning

Statistics show that among the 1.67 million cancer reported cases worldwide, breast cancer is the most common cancer among women and constitutes the largest burden of the disease in developing countries. However, if detected early enough, it can be managed. Mammography is one of the best ways to identify and diagnose breast abnormalities among various medical imaging modalities. It typically detects signs and symptoms of breast cancer, including microcalcifications, lumps, nodules, architectural abnormalities, asymmetry, bilateral asymmetry, etc. These features can be benign or cancerous when they appear in the breast. Researchers have focused on creating fully automated computer-aided design methods to help radiologists combat this type of cancer. Artificial Intelligence( AI) -based algorithms have been essential in creating systems that allow for automated diagnosis, rapid response, and low mortality. In this work, several machine learning methods were compared—such as logistic regression, naive Bayesian Gaussian algorithms, support vector machines (SVM), linear support vector machines (SVM), and artificial neural networks (ANN). Processing time and accuracy were the main evaluation metrics where naive Bayes outperformed SVM, followed by linear SVM and logistic regression, with ANNs failing in accuracy. These results highlight how naive Bayes algorithms can help in early detection of breast cancer, leading to faster and more efficient treatments and ultimately better patient care.

Computer-Aided Design of VEGFR-2 Inhibitors as Anticancer Agents: A Review.

Due to its intricate molecular and structural characteristics, vascular endothelial growth factor receptor 2 (VEGFR-2) is essential for the development of new blood vessels in various pathological processes and conditions, especially in cancers. VEGFR-2 inhibitors have demonstrated significant anticancer effects by blocking many signaling pathways linked to tumor growth, metastasis, and angiogenesis. Several small compounds, including the well-tolerated sunitinib and sorafenib, have been approved as VEGFR-2 inhibitors. However, the widespread side effects linked to these VEGFR-2 inhibitors-hypertension, epistaxis, proteinuria, and upper respiratory infection-motivate researchers to search for new VEGFR-2 inhibitors with better pharmacokinetic profiles. The key molecular interactions required for the interaction of the small molecules with the protein target to produce the desired pharmacological effects are identified using computer-aided drug design (CADD) methods such as pharmacophore and QSAR modeling, structure-based virtual screening, molecular docking, molecular dynamics (MD) simulation coupled with MM/PB(GB)SA, and other computational strategies. This review discusses the applications of these methods for VEGFR-2 inhibitor design. Future VEGFR-2 inhibitor designs may be influenced by this review, which focuses on the current trends of using multiple screening layers to design better inhibitors.

Computer Models Assist Innovative Design of Graphic Arts

In order to study the theory and application of computer aided design (CAD) related to the composition of art, the theory of symbolic matrix was introduced to realize the purpose of the study of the interaction between perceptual and rational problems. In this paper, the original image was extracted from the design object, and the transformation between the original image and the symbol matrix was realized by the correspondence with the elements in the matrix. Based on the research route, four kinds of mathematical models were established, namely random distribution, symmetric distribution, central diffusion, and reorganization. By applying the mathematical model constructed in this paper, the picture was transformed, the combination of various styles of graphics was added, and how to put the simple image picture into a meaningful integrated image picture was discussed. The computer-aided design method of the existing verification examples was summarized in this paper, and the design method was accurate and effective. Compared with traditional graphic art design methods, the computer-aided design method mentioned in the paper has the following advantages. Computer-aided design can quickly process and analyze images, convert and combine them through mathematical models and algorithms, greatly saving designers time and energy. By introducing symbol matrix theory and establishing various mathematical models, computer-aided design can provide diverse graphic combinations and style transformations, helping designers explore more innovative design solutions.

The In vitro evaluation of in silico-designed synthetic peptides AKVUAM-1 and AKVUAM-2 on human lung fibroblast cell line MRC5 and Mycobacterium tuberculosis isolates

Tuberculosis is a major global health problem caused by Mycobacterium tuberculosis and the increase in drug resistance is driving the need for new treatments. Today, various approaches are being applied in the development of drugs for the treatment of tuberculosis. Computer-aided drug design (CADD) enables the prediction of pharmacological efficacy for potential drug molecules during the design process. Thus, new therapeutic compounds can be developed that are more potent, less toxic and have fewer side effects than existing drugs. In this study, we investigated the in vitro activities of AKVUAM-1 and AKVUAM-2 synthetic peptides designed in silico by computer-aided drug design method to inhibit the interaction between M. tuberculosis outer membrane protein Cpn T and macrophage surface receptor CR-1 and Surfactant D protein. Notably, these synthetic peptides do not show cytotoxic effect on normal lung tissue and do not kill M. tuberculosis directly. The MIC values for AKVUAM-1 were higher than 512 μg/ml for all bacterial strains except IST-16 strain (128 μg/ml). According to our results, AKVUAM-1 and AKVUAM-2 synthetic peptides have the potential to be successful candidates for investigating their potential to block macrophage entry of bacilli as targeted.

Fungicidal Activity of Novel 6-Isothiazol-5-ylpyrimidin-4-amine-Containing Compounds Targeting Complex I Reduced Nicotinamide Adenine Dinucleotide Oxidoreductase.

To discover novel inhibitors of the complex I reduced nicotinamide adenine dinucleotide (NADH) oxidoreductase as fungicides, a series of 6-isothiazol-5-ylpyrimidin-4-amine-containing compounds were designed using a computer-aided pesticide design method and splicing of substructures from diflumetorim and isotianil. In vitro fungicidal bioassays indicated that compounds T17-T24 showed high inhibitory activity against Rhizoctonia solani with an effective concentration (EC50) value falling between 2.20 and 23.85 μg/mL, which were more active than or equivalent to the lead diflumetorim with its EC50 of 19.80 μg/mL. In vivo antifungal bioassays demonstrated that, at a concentration of 200 μg/mL, T7 and T21 showed higher inhibition against Pseudoperonospora cubensis than all other compounds, while T23 exhibited the highest inhibition against Sphaerotheca fuliginea. T23 showed an approximately twofold lower inhibition potency against R. solani complex I NADH oxidoreductase than diflumetorim. Molecular docking and transcriptomic analyses indicated that T23 and diflumetorim both might share the same mode of action, targeting NADH oxidoreductase. T23 as a good fungicidal candidate against R. solani is worthy of further investigation.

Foundational Engineering of Artificial Blood Vessels' Biomechanics: The Impact of Wavy Geometric Designs.

The design of wavy structures and their mechanical implications on artificial blood vessels (ABVs) have been insufficiently studied in the existing literature. This research aims to explore the influence of various wavy geometric designs on the mechanical properties of ABVs and to establish a foundational framework for advancing and applying these designs. Computer-aided design (CAD) and finite element method (FEM) simulations, in conjunction with physical sample testing, were utilized. A geometric model incorporating concave and convex curves was developed and analyzed with a symbolic mathematical tool. Subsequently, a total of ten CAD models were subjected to increasing internal pressures using a FEM simulation to evaluate the expansion of internal areas. Additionally, physical experiments were conducted further to investigate the expansion of ABV samples under pressure. The results demonstrated that increased wave numbers significantly enhance the flexibility of ABVs. Samples with 22 waves exhibited a 45% larger area under 24 kPa pressure than those with simple circles. However, the increased number of waves also led to undesirable high-pressure gradients at elevated pressures. Furthermore, a strong correlation was observed between the experimental outcomes and the simulation results, with a notably low error margin, ranging from 19.88% to 3.84%. Incorporating wavy designs into ABVs can effectively increase both vessel flexibility and the internal area under pressure. Finally, it was found that expansion depending on the wave number can be efficiently modeled with a simple linear equation, which could be utilized in future designs.

High-temperature heat pump working fluid development based on molecular design and the group contribution method

High-temperature heat pumps are widely recognized as a critical approach for decarbonizing energy-intensive industries, and their performance is significantly influenced by the working fluids. However, conventional working fluid selection methods primarily focus on existing fluids, limiting the selection scale. Therefore, a group-contribution-based computer-aided molecular design method has been proposed to design and select working fluids for high-temperature heat pumps on a large scale, comprehensively considering environmental impact, safety, operating reliability and performance. A total of 9771 working fluids were generated by the computer-aided process based on the backtracking searching algorithm. Under different working conditions, 208, 232, 243, 228, and 208 working fluids have passed the multi-level screening process based on the group contribution method, respectively, with an overall passing rate below 2.49 %. The H(C)FOs among the top 50 working fluids, ranked by the coefficient of performance, were further analyzed. Statistically, 92.4 % and 98.73 % of the total top 50 working fluids had their Tbr and Tc values within the ranges of 0.640–0.675 and 432–480 K, respectively. Among the 18 HCFOs within the top 50 list, 83.3 % were non-existent. R1251zd and R1344zf demonstrated the best ranking results, while R1242zf, R1233yf, and R1233xf exhibited superior working condition flexibility. As an existing HCFO, R1251yd outperformed other working fluids. Of the 35 HFOs in the top 50 list, 88.57 % were non-existent, and R1381myz, R1371my and R1381mzz showed both exceptional performance and superior working condition adaptability. In conclusion, there remain promising prospects and a notable necessity for the new H(C)FO development, and guidance can be expected from this study in this regard.

Stable diesel and bio-oil emulsion through solvent extraction and ultrasonic aided emulsification

Fast pyrolysis bio-oil is considered a highly preferable supplement to conventional fossil fuels. However, the direct application of pyrolysis bio-oil is limited due to poor fuel properties. Emulsification of bio-oil with diesel using a solvent is considered a relatively straightforward technique for utilizing bio-oil directly with diesel. In this study, new solvents were studied with the aid of sonication to produce a stable emulsion of diesel and bio-oil. 2-Octanol, 2-Heptanol and 2-Octanone were selected as solvents based on computer-aided molecular design (CAMD) method. Bio-oil was blended with 5, 10, 15 and 20 wt% of 2-Octanol using sonication. The solvent-oil blends with different solvents (2-Heptanol and 2-Octanone) at 20 wt% were also investigated for extracting organics from bio-oil. Subsequently, sonication-assisted emulsification of the solvent-extracted bio-oil with diesel was conducted with the aid of a composite surfactant with a hydrophilic-lipophilic balance (HLB) value of 7. Extraction of bio-oil with 20 wt% of 2-Octanol provided a maximum organic yield of 37.9 ± 1.3 % with a Higher Heating Value (HHV) of 31.9 MJ/kg. The desired ratio for emulsification was found to be 80: 15: 5 (diesel: surfactant: solvent-extracted bio-oil). The emulsion samples showed promising quality with an HHV of 44.0 MJ/kg and a pH of 8.0 ± 0.1. Moreover, no phase separation was observed even after the accelerated ageing process at 80 ºC for 24 hours. The results showed that all three solvents had an overall positive effect on the extraction of organics from bio-oil and aided in emulsification with diesel in which 2-Octanol was the most effective solvent for achieving a stable diesel – bio-oil emulsion.

Novel 1,3,4-oxadiazole derivatives of naproxen targeting EGFR: Synthesis, molecular docking studies, and cytotoxic evaluation.

The close association between inflammation and cancer inspired the synthesis of a series of 1,3,4-oxadiazole derivatives (compounds H4-A-F) of 6-methoxynaphtalene. The chemical structures of the new compounds were validated utilizing Fourier-transform infrared, proton nuclear magnetic resonance, and carbon-13 nuclear magnetic resonance spectroscopic techniques and CHN analysis. Computer-aided drug design methods were used to predict the compounds biological target, ADMET properties, toxicity, and to evaluate the molecular similarities between the design compounds and erlotinib, a standard epidermal growth factor receptor (EGFR) inhibitor. The antiproliferative effects of the new compounds were evaluated by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay, cell cycle analysis, apoptosis detection by microscopy, quantitative reverse transcription-polymerase chain reaction, and immunoblotting, and EGFR enzyme inhibition assay. In silico analysis of the new oxadiazole derivatives indicated that these compounds target EGFR, and that compounds H4-A, H4-B, H4-C, and H4-E show similar molecular properties to erlotinib. Additionally, the results indicated that none of the synthesized compounds are carcinogenic, and that compounds H4-A, H4-C, and H4-F are nontoxic. Compound H4-A showed the best-fit score against EGFR pharmacophore model, however, the in vitro studies indicated that compound H4-C was the most cytotoxic. Compound H4-C caused cytotoxicity in HCT-116 colorectal cancer cells by inducing both apoptosis and necrosis. Furthermore, compounds H4-D, H4-C, and H4-B had potent inhibitory effect on EGFR tyrosine kinase that was comparable to erlotinib. The findings of this inquiry offer a basis for further investigation into the differences between the synthesized compounds and erlotinib. However, additional testing will be needed to assess all of these differences and to identify the most promising compound for further research.

Multiphase flow and nozzle wear with CFD-DEM in high-pressure abrasive water jet

The issue of wear failure in High-Pressure Abrasive Water Jet (HP-AWJ) nozzles is an unavoidable challenge, and studying methods to enhance and predict the effective lifetime of nozzles is worth deep exploration. This paper employs a CFD-DEM coupling numerical approach to investigate wear phenomena inside the HP-AWJ nozzle, aiming to capture the realistic particle wear and erosion failure issues at the focusing tube region, which is a high-wear area of the HP-AWJ nozzle. Furthermore, the study considers realistic particles and nozzle wall constitutive models, incorporating material properties into the physical model, and employs computer-aided design methods to reflect wear failure conditions at different time intervals in the inner wall of the focusing tube at the nozzle. The results demonstrate that the number of realistic particles and initial inlet velocity has no impact on the particle exit kinetic energy. However, the particle-wall restitution coefficient affects the average particle kinetic energy at the outlet in the AWJ nozzle. The equivalent model of the realistic particles reflects the influence of the particle roundness on particle kinetic energy, acceleration, and stress concentration variations in the nozzle. These variations further affect the particle erosion rate on the nozzle wall and the actual wear failure problems on the wall surface. Finally, by combining the proposed erosion and wear model, a representative erosion profile at the AWJ focusing tube location comparable to experimental results is obtained, and the wear depth of the focusing tube changing with time is also studied. The results and methodologies presented in this paper provide valuable guidance for controlling the effective service lifetime of the AWJ nozzle, improving machining efficiency, and extending the lifespan of the AWJ nozzle.

Deep learning-designed implant-supported posterior crowns: Assessing time efficiency, tooth morphology, emergence profile, occlusion, and proximal contacts

ObjectivesTo compare implant supported crowns (ISCs) designed using deep learning (DL) software with those designed by a technician using conventional computer-aided design software. MethodsTwenty resin-based partially edentulous casts (maxillary and mandibular) used for fabricating ISCs were evaluated retrospectively. ISCs were designed using a DL-based method with no modification of the as-generated outcome (DB), a DL-based method with further optimization by a dental technician (DM), and a conventional computer-aided design method by a technician (NC). Time efficiency, crown contour, occlusal table area, cusp angle, cusp height, emergence profile angle, occlusal contacts, and proximal contacts were compared among groups. Depending on the distribution of measured data, various statistical methods were used for comparative analyses with a significance level of 0.05. ResultsISCs in the DB group showed a significantly higher efficiency than those in the DM and NC groups (P ≤ 0.001). ISCs in the DM group exhibited significantly smaller volume deviations than those in the DB group when superimposed on ISCs in the NC group (DB–NC vs. DM–NC pairs, P ≤ 0.008). Except for the number and intensity of occlusal contacts (P ≤ 0.004), ISCs in the DB and DM groups had occlusal table areas, cusp angles, cusp heights, proximal contact intensities, and emergence profile angles similar to those in the NC group (P ≥ 0.157). ConclusionsA DL-based method can be beneficial for designing posterior ISCs in terms of time efficiency, occlusal table area, cusp angle, cusp height, proximal contact, and emergence profile, similar to the conventional human-based method. Clinical significanceA deep learning-based design method can achieve clinically acceptable functional properties of posterior ISCs. However, further optimization by a technician could improve specific outcomes, such as the crown contour or emergence profile angle.

Simulation of 2D ReS2/WSe2 based complementary field-effect transistors towards 1 nm technology node

Advanced Integrated Circuit technology demands high-performance channel materials and innovative device architectures to sustain the scaling of field-effect transistors. In this study, we simulate the electrical performance of rhenium disulfide and tungsten diselenide nanosheet FETs (NSFETs) with gate lengths ranging from 12 nm to 8 nm using Technology Computer-Aided Design method. The simulated high performance including 393 μA/μm on-state current and over 105 on/off ratio can meet the criteria for integrated circuit applications in the 1 nm technology node. Complementary FET (CFET) simulations are conducted through the vertical stacking of ReS2 and WSe2 NSFETs, exhibiting a small parasitic capacitance of 1.0 fF/μm, and notable noise margin (>235 mV) at different process corners. The construction and performance simulation of Static Random-Access Memory (SRAM) is realized through CFET interconnection, involving the calculation of read current and read/write noise margin. This research offers a forward-looking analysis of performance metrics for future 2D materials-based NSFETs, CFETs, and SRAMs.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors.

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Discovery of Novel Spike Inhibitors against SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein-ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 μM, 6.7 μM, and 7.6 μM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 μM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 μM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection.

Unlocking Brilliance: A Smart Approach to Icon Colour Design Inspired by Universal Design Principles

Icons are integral to the signature systems within architectural spaces, serving pivotal roles through human–environment interactions. However, previous icon designs often exhibited a considerable randomness and neglected the needs of visually impaired individuals. To address these issues and to overcome the limitations of the computer-aided design methods, such as most of them focusing only on text design which are not compatible with icons, this study presents an intelligent assistance method named “Universal Colour” for icon colour design based on universal design principles. Such a system enables the rapid generation of icon colour schemes and supports visual and quantitative filtering and comparison during the decision-making process for colour scheme optimization. To assess its usability, fifty-two participants conducted icon colour design experiments using this system, resulting in 87% of the design schemes meeting the universality requirements. The results have demonstrated that Universal Colour has the potential to significantly enhance efficiency and cognitive aspects within the decision-making process for users, regardless of their proficiency in icon design, thereby facilitating the generation of universal icon colour schemes in architectural design.

АРХІТЕКТУРНО-КОНСТРУКТИВНІ ЕЛЕМЕНТИ БУДІВЕЛЬ ПРИ 3D-ДРУЦІ: ПРОЕКТУВАННЯ, МАТЕРІАЛИ, ЕФЕКТИВНІСТЬ (ЧАСТИНА ПЕРША - ОГЛЯД)

The article provides an analysis of current approaches to 3D printing of buildings. It focuses on three key areas: 1) computer-aided design methods for 3D printing of buildings; 2) manufacturing methods and materials used; 3) methods for evaluating their effectiveness. The section "Design/Development" covers tools and techniques used from the design stage to the production of building elements. The Technology and Materials section looks at opportunities for engineering applications, categorized by material type. The Performance Evaluation section highlights current strategies for analyzing 3DP buildings. In the first part of this analysis (i.e., this article), the author discusses in detail the general concept of the study, focusing on its key aspects.

AERODROME MAPPING DATA DESIGN DEVELOPMENT APPROACH HIGHLIGHTS

In accordance with the trend in further intellectualization of mapping data design systems, this article deals with the problem of development methods for obtaining aerodrome mapping data more efficient than the existing methods that use manual vectorization on a raster base. Data usage areas were listed, including automated control systems, flight deck automation, simulators and simulation systems. Much attention was given to main aerodrome mapping data design approaches analysis, which shows that existing aerodrome cartographic data obtaining methods lack functionality. A description and basic requirements for an aerodrome mapping data design system, as well as requirements for the data itself, are presented. Additionally, data validation aspects are noted, including those of a taxi route network. Results of the research suggest that computer-aided design methods make it possible to significantly reduce labor intensity and time required to obtain mapping data. A new approach, which consists in using computer-aided design methods for such data, is proposed. Areas in which this approach can provide a significant effect were identified. Methods, algorithms and programs that implement the approach are being developed in context of the new data design system. It will provide a wide range of functions for aerodrome mapping design. General scheme of the system describing its main components was given. An example of aircraft parking elements design as a part of routing network design is provided.

492 Novel BH3 Mimetic Bcl-2 Inhibitor Promotes Autophagic Cell Death and Reduces in Vivo Glioblastoma Tumor Growth

INTRODUCTION: Anti-apoptotic members of the Bcl-2 family proteins play central roles in the regulation of cell death in glioblastoma, the most malignant type of brain tumor. Despite the advances in GBM treatment, there is still an urgent need for new therapeutic approaches. METHODS: In silico studies resulted in 500,000 molecules being screened with computer-aided drug design methods and the molecule with the highest binding affinity was selected and named BAU-243. Cell viability, IC50 value, tumor formation potential, tumor spheroid formation potential, tumor invasion studies were investigated following BAU-243 application. The molecular changes caused by Bcl-2 inhibition were demonstrated by quantitative PCR, western blot and immunofluorescence methods. In in vivo studies, glioma tumors were transplanted intracranially into Nod Scid Gamma(NSG) mice using the mouse xenograft model. RESULTS: Here, we report a novel 4-thiazolidinone derivative BH3 mimetic, BAU-243 that binds to Bcl-2 with a high affinity. BAU-243 effectively reduced overall glioblastoma cell proliferation including a subpopulation of cancer-initiating cells in contrast to the selective Bcl-2 inhibitor ABT-199. While ABT-199 successfully induces apoptosis in high BCL2-expressing neuroblastoma SHSY-5Y cells, BAU-243 triggered autophagic cell death rather than apoptosis in A172 cells, indicated by the upregulation of BECN1, ATG5, and MAP1LC3B expression. Lc3b-II, a potent autophagy marker, was significantly upregulated following BAU-243 treatment. Moreover, BAU-243 significantly reduced tumor growth in vivo in orthotopic brain tumor models when compared to the vehicle group, and ABT-199 treated animals. To elucidate the molecular mechanisms of action of BAU-243, we performed computational modeling simulations that were consistent with in vitro results. CONCLUSIONS: Results indicated that BAU-243 activates autophagic cell death by disrupting Beclin 1:Bcl-2 complex, and may serve as a potential small molecule for the treatment of glioblastoma.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Biogas upgrading—Computer-aided ionic liquid absorbent design and process evaluation

Biogas is a renewable energy source and needs to be upgraded to biomethane for injection into the natural gas grid or use as fuel. To design ionic liquid (IL) solvents for biogas upgrading, a computer-aided ionic liquid design (CAILD) method and the corresponding process simulation are presented. The UNIFAC-IL model is employed to calculate the solubility of gases in ILs, while group contribution (GC) based models are used to predict the physicochemical properties of ILs. By using the performance index (PI) as the objective function and the structural feasibility and physicochemical properties as constraints, a mixed-integer nonlinear programming (MINLP) problem is formulated and solved by the generate-and-test method. Two IL solvents, [MMPY][Tf2N] (1,3-dimethylpyridinium bis(trifluoromethylsulfonyl)imide) and [MMPY][eFAP] (1,3-dimethylpyridinium tris(pentafluoroethyl) trifluorophosphate), are found to be the optimal IL solvents from 880 IL candidates. To perform the process simulation by using the designed ILs, the parameters for the equations of calculation of required properties are regressed. A sensitivity analysis is performed to find the optimal conditions for the process. Finally, the developed process is compared with the water-scrubbing process for biogas upgrading.