Computational Design Approach Research Articles

Optimizing quantitative photoacoustic imaging systems: The Bayesian Cramér-Rao bound approach

Abstract Quantitative photoacoustic computed tomography (qPACT) is an emerging medical imaging modality that carries the promise of high-contrast, fine-resolution imaging of clinically relevant quantities like hemoglobin concentration and blood-oxygen saturation. However, qPACT image reconstruction is governed by a multiphysics, partial differential equation (PDE) based inverse problem that is highly non-linear and severely ill-posed. Compounding the di culty of the problem is the lack of established design standards for qPACT imaging systems, as there is currently a proliferation of qPACT system designs for various applications and it is unknown which ones are optimal or how to best modify the systems under various design constraints. This work introduces a novel computational approach for the optimal experimental design (OED) of qPACT imaging systems based on the Bayesian Cramér-Rao bound (CRB). Our approach incorporates several techniques to address challenges associated with forming the bound in the infinite-dimensional function space setting of qPACT, including priors with trace-class covariance operators and the use of the variational adjoint method to compute derivatives of the log-likelihood function needed in the bound computation. The resulting Bayesian CRB based design metric is computationally efficient and independent of the choice of estimator used to solve the inverse problem. The efficacy of the bound in guiding experimental design was demonstrated in a numerical study of qPACT design schemes under a stylized two-dimensional imaging geometry. To the best of our knowledge, this is the first work to propose a Bayesian CRB-based design for systems governed by PDEs.


Challenges and perspectives in using unspecific peroxygenases for organic synthesis

In the past 20 years, unspecific peroxygenases (UPOs) have emerged as promising biocatalysts for various organic transformations. Particularly, we have witnessed great attention being paid to the screening of new enzymes and expansion of the substrates/products. However, challenges such as enzyme stability, low turnover numbers, and substrate specificity hinder their widespread utilization in practical organic synthesis. This review article provides a concrete and mini-overview of the challenges associated with using UPOs in organic synthesis and discusses strategies for enzyme engineering to overcome these limitations. The article highlights recent advancements in UPO research and presents potential solutions to enhance their catalytic efficiency, stability, substrate specificity, and regioselectivity. Additionally, the review outlines the current methodologies employed for directed evolution and protein engineering of UPOs, along with computational modeling approaches for rational enzyme design. By addressing the challenges and exploring avenues for enzyme engineering, this review aims to shed light on the prospects of UPOs in organic synthesis.

The Anti-Leukemic Activities of Campesterol and Α-Tocopherol Against BCL-2 Target through Computational Drug Design Approaches.

Heterogeneous Acute Myeloid Leukemia (AML) causes substantial worldwide morbidity and death. AML is characterized by excessive proliferation of immature myeloid cells in the bone marrow and impaired apoptotic regulator expression. B-Cell Lymphoma 2 (BCL-2), an anti-apoptotic protein overexpressed in AML, promotes leukemic cell survival and chemoresistance. Thus, reducing BCL-2 may treat AML. Anticancer activities are found in Aloe barbadensis Miller (Aloe vera). Thus, this work used molecular modeling to assess Aloe vera bioactive chemicals as BCL-2 inhibitors. Molecular docking simulation showed that all identified Aloe vera phytocompounds have strong BCL-2 binding affinities (-6.7 to -8.7 kcal/mol). Campesterol and α-tocopherol were identified as promising compounds for BCL-2 inhibitor research based on their drug-likeness, pharmacokinetics, and toxicity profiles. The stability and conformational of the BCL-2-compound complexes showed that the compounds were stable in BCL-2's binding pocket. Campesterol and α-tocopherol are promising BCL-2 inhibitors that might become effective anti-leukemic therapies with additional in vitro and in vivo research.

Computational Drug Design Approaches for the Identification of Novel Antidiabetic Compounds from Natural Resources through Molecular Docking, ADMET, and Toxicological Studies.

Type 2 diabetes mellitus (T2DM) is usually depicted by relative insulin deficiency, raised blood glucose levels, and the predominant risk factor, insulin resistance. Hence, the development of insulin sensitizer drugs targeting PPAR-γ receptors has expanded enormous interest as an attractive choice for T2DM treatment. Thiazolidinediones (TZD) enhance insulin sensitivity either by directly functioning on gene transcription of the PPARγ receptor related to glucose homeostasis or by systemic sensitization of insulin and, therefore, improved hyperglycemia in a wide range of patients. However, severe complications and adverse effects of TZDs necessitate the development of an efficacious and reliable insulin sensitizer from alternative resources. On the contrary, Nature is a rich source of anticipated effective and safer medicine; more than fifty percent of drugs on the market are developed from natural products. Hence, searching for a new PPAR-γ agonist from bioactive secondary compounds of medicinal plants along with greater efficacy and safety is a recognized and consistent tactic for developing novel antidiabetic agents. Pulicaria jaubertii is a fragrant perennial aromatic plant with anti-inflammatory, antidiabetic, antimicrobial, antimalarial, and insecticidal properties. The current study was designed to use a computer-aided drug design to explore the best antidiabetic compounds from P. jaubertii. Herein, the molecular docking study of 80 investigated ligands against the PPAR-γ receptor identifies the highest docking score for five ligands ranging from -8.9 kcal/mol to 8.0 kcal/mol, which is also more significant than the standard drug pioglitazone (-7.7 kcal/mol) determined by the PyRx 8.0 virtual screening software. GLN286, CYS285, SER289, TYR473, MET364, ARG288, ILE341, and LEU333 residues are found to be significant contributors to the non-bonded interaction between ligands and receptors. Molecular electrostatic potential (MEP), DFT, molecular orbital (MO), ADMET, and toxicological analyses were performed on the selected five high-scored ligands of P. jaubertii. Results documented that all investigated ligands, especially L4, show considerably excellent profiles in molecular docking, MEP, DFT, MO, ADMET, and toxicological predictions, suggesting our drug-designing approaches may contribute to the development of a novel antidiabetic drug for the treatment of T2DM from natural resources.

Exploration of novel boron nitride polymorphs: High-throughput screening combined with multi-task orbital crystal graph convolutional neural network (MT-OCGCN)

In response to the limitations of traditional material property prediction methods, we have developed a Multi-task Orbital Crystal Graph Convolutional Neural Network (MT-OCGCN). This innovative model allows for sharing parameters and computational resources, significantly improving prediction efficiency and accuracy. Experimental comparisons show that the model can achieve multi-task prediction with high accuracy for both strong and weak correlation properties, this offers an efficient approach for computational materials screening and design. Combined with high-throughput screening has resulted in the discovery of 13 new boron nitride polymorphs in the P21/c phase, among which 7 are characterized as wide direct band gap semiconductors. Furthermore, we have comprehensively analyzed the mechanical, thermal, and electronic properties of these new boron nitride polymorphs. It can be asserted that MT-OCGCN has the capability to precisely forecast the physical properties of novel structures that are absent from current databases.

Designed endocytosis-inducing proteins degrade targets and amplify signals.

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by endogenous ligands. Therapeutic approaches such as lysosome-targeting chimaeras1,2 (LYTACs) and cytokine receptor-targeting chimeras3 (KineTACs) have used this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. Although powerful, these approaches can be limited by competition with native ligands and requirements for chemical modification that limit genetic encodability and can complicate manufacturing, and, more generally, there may be no native ligands that stimulate endocytosis through a given receptor. Here we describe computational design approaches for endocytosis-triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for insulin-like growth factor 2 receptor (IGF2R) and asialoglycoprotein receptor (ASGPR), sortilin and transferrin receptors, and show that fusing these tags to soluble or transmembrane target protein binders leads to lysosomal trafficking and target degradation. As these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. EndoTag fusion to a PD-L1 antibody considerably increases efficacy in a mouse tumour model compared to antibody alone. The modularity and genetic encodability of EndoTags enables AND gate control for higher-specificitytargeted degradation, and the localized secretion of degraders from engineered cells. By promoting endocytosis, EndoTag fusion increases signalling through an engineered ligand-receptor system by nearly 100-fold. EndoTags have considerable therapeutic potential as targeted degradation inducers, signalling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody-drug and antibody-RNA conjugates.

Anti- COVID-19 drug discovery by flavonoid derivatives: an extensive computational drug design approach.

The present study deals with the in-silico analyses of several flavonoid derivatives to explore COVID-19 through pharmacophore modelling, molecular docking, molecular dynamics, drug-likeness, and ADME properties. The initial literature study revealed that many flavonoids, including luteolin, quercetin, kaempferol, and baicalin may be useful against SARS β-coronaviruses, prompting the selection of their potential derivatives to investigate their abilities as inhibitors of COVID-19. The findings were streamlined using in silico molecular docking, which revealed promising energy-binding interactions between all flavonoid derivatives and the targeted protein. Notably, compounds 8, 9, 13, and 15 demonstrated higher potency against the coronavirus Mpro protein (PDB ID 6M2N). Compound 8 has a -7.2 Kcal/mol affinity for the protein and binds to it by hydrogen bonding with Gln192 and π-sulfur bonding with Met-165. Compound 9 exhibited a significant interaction with the main protease, demonstrating an affinity of -7.9 kcal/mol. Gln-192, Glu-189, Pro-168, and His-41 were the principle amino acid residues involved in this interaction. The docking score for compound 13 is -7.5 Kcal/mol, and it binds to the protease enzyme by making interactions with Leu-41, π-sigma, and Gln-189. These interactions include hydrogen bonding and π-sulfur. The major protease and compound 15 were found to bind with a favourable affinity of -6.8 Kcal/mol. This finding was further validated through molecular dynamic simulation for 1ns, analysing parameters such as RMSD, RMSF, and RoG profiles. The RoG values for all four of the compounds varied significantly (35.2-36.4). The results demonstrated the stability of the selected compounds during the simulation. After passing the stability testing, the compounds underwent screening for ADME and drug-likeness properties, fulfilling all the necessary criteria. The findings of the study may support further efforts for the discovery and development of safe drugs to treat COVID-19.

An Integrative Computational Approach for Design and Evaluation of Novel [1,2,4]triazolo[3,4-b][1,3,4]thiadiazole Analogues as Dual-Action Anti-Retroviral and Anti-Bacterial Agents: Insights into Rational Drug Design Strategies

The development of effective antiretroviral inhibitors (HIV-1 Reverse Transcriptase) and glucosamine-6-phosphate (GlcN6P) agents represent a significant challenge for combating viral infections and related diseases. In this study, we employed a multidisciplinary approach to design and evaluate a series of novel triazole analogs as potential candidates for anti-retroviral and glucosamine-6-phosphate agents. Using molecular docking, we explored the binding interactions between the RSA3 analog and the target proteins implicated in viral replication and glucosamine metabolism. Density functional theory (DFT) employing the 6-311++G(d,p) basis set was utilized for computational analysis, with a subsequent examination of the electronic parameters. The HOMO and LUMO orbitals as well as the molecular electrostatic potential map were investigated using DFT to characterize the reactivity of our molecule. Molecular dynamics simulations were performed to investigate the stability and dynamic behavior of the ligand-protein complex (RSA3) over time (300 ns). Furthermore, we employed MM-GBSA calculations of the compound RSA3 (molecular mechanics generalized band surface area) to estimate the binding free energies (−71.30 ± 7.26 kcal/mol and −67.86 ± 7.09 kcal/mol, respectively) and identified the most promising compounds with high affinity for the target proteins. Additionally, predictive absorption, distribution, metabolism, excretion and toxicity (ADMET) studies of compound RSA3 were conducted to assess its drug-like properties and potential safety profiles of the selected compound. Our results revealed that RSA3 has favorable binding interactions, is a stable complex and has significant binding affinity for target proteins. Moreover, predictive ADMET studies indicated that RSA3 possesses desirable drug-like properties, suggesting its potential for further investigation as an anti-retroviral agent and glucosamine-6-phosphate agent. Overall, this integrative computational approach combining molecular docking, DFT analysis, dynamics simulations, MM-GBSA and predictive ADMET studies provides valuable insights into the design and evaluation of novel triazole analogs with potential therapeutic applications, highlighting the importance of rational drug design strategies in the field of antiretroviral and glucosamine research.

Integrating Computational Design and Experimental Approaches for Next-Generation Biologics.

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

(Invited) Computational Design and Analysis of Novel Battery Electrolytes Driven by the Grotthuss Mechanism

Candidate systems for next-generation electrolyte materials, such as deep eutectic solvents and ionic liquids, often suffer from high viscosities, which suppresses rates of charge transport and limits their performance characteristics. A strategy for circumventing this problem is to leverage the structural or Grotthuss mechanism of proton transport through hydrogen-bond networks, which can exhibit high proton diffusion rates even when rates of vehicular charge transport are poor. In this talk, I will describe a project aimed at leveraging computational design and simulation approaches, in combination with experimental synthesis and characterization, to develop a novel class of battery electrolytes based on the structural/Grotthuss diffusion mechanism. The workflow involves identification of a suitable proton-carrier liquid, a proton source, and a redox-active species capable of undergoing proton-coupled electron transfer (PCET) reactions. I will discuss the computational methods, which involve a combination of machine learning and first-principles simulation techniques, candidate chemical species for each of the components, first simulation and experimental protocols for combining these components into a high-performance electrolyte, preliminary results, and next steps in the evolution of the project. This work will serve to illustrate both the power of modern computational approaches in the design of electrochemical systems but also to broaden the perspective on what constitutes a “breakthrough” electrolyte. Figure 1

(Invited) Structured Electrode Manufacturing for Next-Generation Lithium-Ion Batteries

Achieving high-energy and high-power density Lithium-ion batteries (LIBs) with fast charging capabilities is critical to advancing electric vehicle (EV) and portable electronic technologies. Long-range, fast charge EVs and more compact portable electronics require significant advances in battery performance, lifetime, and cost. Conventional LIBs are composed of flat anode and cathode electrode stacks that can be optimized for energy or power, but not both concurrently. This is due to ion transport limitations that persist with increases in electrode thickness. Several research efforts have focused on optimizing LIB performance through material innovations with lithium metal and silicon anodes or new cathode materials, to name a few. Structured Electrode (SE) designs1 have been investigated over the last couple of decades as an alternate solution to address these challenges. SEs engineer electrode materials into different three-dimensional (3D) architectures on a scale ranging from tens to hundreds of microns to facilitate rapid ion-transport in thicker electrodes. While a promising concept, reliable and scalable manufacturing methods for fabricating SEs over the large and complex areas needed for EV and portable electronic applications remains limited.This talk covers manufacturing approaches investigated by our research group to fabricate SEs rapidly and efficiently. Both computational design and experimental approaches are employed to facilitate the development of new hardware, software, and processing techniques for SEs. Specifically, we highlight Additive Manufacturing (AM) tools for portable electronic applications and the use of acoustophoresis for manufacturing EV SEs. First, we have developed new computational fabrication tools2 and material formulations to push the bounds of AM for SE fabrication. Through this work, we investigate the impact of electrode design and fabrication on the rate capability and charge performance of Li4Ti5O12 and LiFePO4 cells. Next, taking AM towards larger area fabrication, we have combined acoustophoresis with AM principles to facilitate large-area printing and patterning of SEs for LIBs.3 Acoustophoresis utilizes acoustic standing wavesto drive particles into predefined patterns, with micron-scale control at time scales < 1 second. Our initial results with LiNi0.6Mn0.2Co0.2O2 and graphite have defined process conditions that enable acoustophoretic fabrication of SEs with improved rate capability over conventional, flat LIB electrodes. Our work highlights the material and processing considerations that must be given when developing new manufacturing processes for SEs. References C.-H. Hung, P. Huynh, K. Teo, and C.L. Cobb, “Are Three-dimensional Batteries Beneficial? Analyzing Historical Data to Elucidate Performance Advantages,” ACS Energy Letters, 8 (1), pp. 296–305, 2023.F. Fossdal, V. Nguyen, R. Heldal, C.L. Cobb, and N. Peek, “Vespidae: A Programming Framework for Developing Digital Fabrication Workflows,” In Proc. of 2023 Designing Interactive Systems Conference, ACM, New York, NY, USA.K.E. Johnson, D.S. Melchert, E.N. Armstrong, Daniel S. Gianola, C.L. Cobb, M.R. Begley, “A Simple, Validated Approach for Design of Two-dimensional Periodic Particle Patterns via Acoustophoresis,” Materials & Design, Vol. 232, 112165, 2023. Acknowledgements This material is based upon work supported in part by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Materials and Manufacturing Technologies Office (AMMTO) Award Numbers DE-EE0009112 and DE-EE0010226. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.This work was funded in part by a Defense Advanced Research Projects Agency (DARPA) Young Faculty Award and Director's Fellowship under grant number D19AP00038. The views, opinions, and findings expressed in this work are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government, and no official endorsement should be inferred. This is approved for public release and distribution is unlimited.

Designing high-performance ion-exchangeable glasses with multi-objective optimization and machine learning

Ion exchange typically involves replacing smaller ions with larger ions below the glass transition temperature. This study introduces an innovative computational approach for the inverse design of ion-exchangeable glasses. It aims to simultaneously achieve high depth of layer (DOL) and surface compressive stress (CS), a task complicated by the inherent trade-offs between these properties. Traditional ion exchange experimental methods are not only time-consuming and expensive, but they also involve complex practical difficulties. Addressing these challenges, this paper introduces a novel computational approach that synergistically combines a multi-objective genetic algorithm with machine learning models. The approach involves training models on a diverse set of glass compositions to predict DOL and CS, which then guide an evolutionary algorithm to optimize these properties concurrently. Physics-informed parameters further refine the search, enabling a flexible design framework in glass manufacturing. Comprehensive experimentation and subsequent analysis affirm the proposed method's capacity to efficiently and effectively traverse the intricate design landscape of glass materials. The findings elucidated the influence of distinct compositional and process parameters on DOL and CS, facilitating the manual design process. Additionally, the study identified sixteen glass composition candidates within the SiO2–B2O3–Al2O3–MgO–Na2O system, exhibiting remarkable properties. Specifically, some compositions achieved a DOL of up to 95 μm with a corresponding CS of 890 MPa, while others attained a CS of up to 1654 MPa with a DOL of 14.72 μm.

In silico analysis of potential inhibitors for breast cancer targeting 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) catalyses.

Breast cancer (BC) is still one of the major issues in world health, especially for women, which necessitates innovative therapeutic strategies. In this study, we investigated the efficacy of retinoic acid derivatives as inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), which plays a crucial role in the biosynthesis and metabolism of oestrogen and thereby influences the progression of BC and, the main objective of this investigation is to identify the possible drug candidate against BC through computational drug design approach including PASS prediction, molecular docking, ADMET profiling, molecular dynamics simulations (MD) and density functional theory (DFT) calculations. The result has reported that total eight derivatives with high binding affinity and promising pharmacokinetic properties among 115 derivatives. In particular, ligands 04 and 07 exhibited a higher binding affinity with values of -9.9 kcal/mol and -9.1 kcal/mol, respectively, than the standard drug epirubicin hydrochloride, which had a binding affinity of -8.2 kcal/mol. The stability of the ligand-protein complexes was further confirmed by MD simulations over a 100-ns trajectory, which included assessments of hydrogen bonds, root mean square deviation (RMSD), root mean square Fluctuation (RMSF), dynamic cross-correlation matric (DCCM) and principal component analysis. The study emphasizes the need for experimental validation to confirm the therapeutic utility of these compounds. This study enhances the computational search for new BC drugs and establishes a solid foundation for subsequent experimental and clinical research.

Research on computationally generative design of woven braced structure based on fiber composite material in architectural design industry

ABSTRACT With the intervention of computer algorithm-assisted design in the realm of contemporary architectural design, architectural spatial forms and corresponding design methods have improved significantly. However, the limitations of material strength and plasticity impose certain constraints on the architectural design of the new century. Based on this occurrence, this paper studies the development of a computational generative design system for woven braced structure composed of fiber-reinforced materials. Through experimental data of material performance, the research firstly argues feasibility of employing fiber composite materials to braced structure. The conclusion is that the structure designed and generated could produce a support of 1 ton per ㎡. Moreover, based on the ant colony algorithm and the algorithm principle of cellular automata on the Java platform, the study designs a generative system with iterative computing of force as the major axis to achieve the self-organization and adaptability of the braced structure. Above all, this paper proposes a design strategy engaging with computational generative design approach, as well as a design attempt for the material and form of architecture.

Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively.

Identification of new inhibitors for the avian H1N1 virus through molecular docking and dynamic simulation approaches

The new variant of influenza A virus is known as avian H1N1 subtype which particularly affects birds, and poultry. However, recently it is affecting in human, and creating global health problem. So, this research has studied the myricetin derivatives as novel therapeutic agents that target the Hemagglutinin Protein in Avian H1N1 Virus. The study is focused on addressing the critical need for effective antiviral agents through computational drug design approaches. The research assesses the binding affinities, quantum calculation, molecular dynamic simulation, binding free energy calculation, electronic characteristics, DCCM, PCA and stability of myricetin derivatives with the hemagglutinin protein. This extensive investigation has documented that all the compounds showed outstanding binding affinities, more precisely compounds like 03 and 47, which show better binding energies than the conventional antiviral Tamiflu. Furthermore, the reactivity, pharmacokinetics, and stability, molecular dynamics simulations, ADMET characteristics, and quantum chemical descriptors are investigated, and they all are satisfied by favorable drug like properties. Besides that, this research highlights the significance of electrical characteristics, dipole moment, and hydrogen bonding in developing efficient antiviral medications. In addition, the myricetin derivatives show promise for oral delivery based on ADMET (absorption, distribution, metabolism, excretion, and toxicity) characteristics. Finally, the molecular dynamics simulations show that the ligand-protein complexes are stable and flexible over the MD simulation of 100 ns. Although, the discovered compounds show potential interactions with the avian H1N1 influenza A virus, with outstanding binding affinity, however, it is still crucial needed to perform experimental validation in near future.

Multi-objective Optimization-Oriented Generative Adversarial Design for Multi-principal Element Alloys

The discovery of novel alloys, such as multi-principal element alloys (MPEAs)—inclusive of the so-called high-entropy alloys—remains essential for technological advancement. Multi-principal element alloys can manifest uniquely favorable mechanical properties, but the complexity of their compositions results in their design and performance being challenging to understand. With the emergence of the materials genome concept, there is potential to pursue novel materials using computational design approaches. However, the complexity of such design often requires immense computational power and sophisticated data analysis. In an attempt to address this, we introduce the application of a new framework, the non-dominant sorting optimization-based generative adversarial networks (NSGAN) in the discovery and exploration of novel MPEAs. By harnessing the power of genetic algorithms and generative adversarial networks (GANs), NSGANs offer an effective solution for high-dimensional multi-objective optimization challenges in alloy design. The framework is demonstrated to generate MPEAs according to specific alloy properties. Furthermore, an online web tool/software applies the NSGAN framework to disseminate the methodology to the broader scientific arena (along with the supporting code made available).

Computational Design for Multi-Optimized Geometry of Sustainable Flood-Resilient Urban Design Habitats in Indonesia

Unfortunately, flooding is a major worldwide problem that especially affects low-lying cities like Semarang, Indonesia. Therefore, this study focuses on the flood-prone areas of Semarang, where recurring high tides and surges from severe precipitation cause havoc. In order to create water-resistant dwelling topologies, the paper explores the early incorporation of computational design approaches. Ultimately, the objective is to explore the strategic application of generative design techniques to support the development of a highly adaptive urban environment using optimization-based data-driven design approaches. With careful consideration, advanced computational methods were used to find concepts that may manage and lessen possible consequences in an efficient manner, increasing the urban landscape’s overall flexibility. Achieving the best possible solutions, which consider issues like feasibility, sustainability, durability, adaptability, and user comfort, requires the application of computational studies such as microclimatic, rainfall, energy performance, and fluid simulations. Consequently, promising advances in water retention and trajectory control features are shown by evaluations that concentrate on wind dynamics and energy considerations. One such example is GEN_8, the most optimal typology produced by additive massing approaches. In addition to showing less water retention than usual building typologies, GEN_8 optimizes energy performance to improve user experience overall. Accordingly, the computationally created geometry GEN_8’s shaded areas and facades effectively account for between 191.4 and 957 kWh/m2 of yearly solar radiation. In contrast, average building typologies show higher amounts of annual solar radiation, with a minimum of 574.32 kWh/m2 and a maximum of 1148.65 kWh/m2. This paper’s comprehensive approach not only addresses worldwide issues but also highlights how computational design techniques may be used to construct, assess, and validate workable solutions for flood-prone locations within a flexible framework that has been painstakingly designed. As a result, the research also highlights the significance of technological advancements and computational tools in assessing, producing, and validating workable solutions for flood-prone locations by carefully curating a flexible framework that ensures efficiency, comfort, and design optimization.

Rational Design of TDP-43 Derived α-Helical Peptide Inhibitors: An In Silico Strategy to Prevent TDP-43 Aggregation in Neurodegenerative Disorders.

TDP-43, an essential RNA/DNA-binding protein, is central to the pathology of neurodegenerative diseases, such as amyotrophic lateral sclerosis and frontotemporal dementia. Pathological mislocalization and aggregation of TDP-43 disrupt RNA splicing, mRNA stability, and mRNA transport, thereby impairing neuronal function and survival. The formation of amyloid-like TDP-43 filaments is largely facilitated by the destabilization of an α-helical segment within the disordered C-terminal region. In this study, we hypothesized that preventing the destabilization of the α-helical domain could potentially halt the growth of these pathological filaments. To explore this, we utilized a range of in silico techniques to design and evaluate peptide-based therapeutics that bind to pathological TDP-43 amyloid-like filament crystal structures and resist β sheet conversion. Our computational approaches, including biophysical and secondary structure property prediction, molecular docking, 3D structure prediction, and molecular dynamics simulations, were used to assess the structure, stability, and binding affinity of these peptides in relation to pathological TDP-43 filaments. The results of our in silico analyses identified a selection of promising peptides which displayed a stable α-helical structure, exhibited an increased number of intramolecular hydrogen bonds within the helical domain, and demonstrated high binding affinities for pathological TDP-43 amyloid-like filaments. Molecular dynamics simulations provided further support for the structural and thermodynamic stability of these peptides, as they exhibited lower root-mean-square deviation and more favorable free energy landscapes over 300 ns. These findings establish α-helical propensity peptides as potential lead molecules for the development of novel therapeutics against TDP-43 aggregation. This structure-based computational approach for the rational design of peptide inhibitors opens a new direction in the search for effective interventions for ALS, FTD, and other related neurodegenerative diseases. The peptides identified as the most promising candidates in this study are currently subject to further testing and validation through both in vitro and in vivo experiments.

Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning

The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2–4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.