Molecular Systems Design & Engineering最新文献

{"title":"Mesoscale modelling of polymer-mediated adhesion: application to tack tests†","authors":"Aristotelis P. Sgouros, Stefan Knippenberg, Anthony Bocahut, Phillip M. Rauscher, Ben Sikora, Stefano Caputo, Hee-Sung Choi, Vincent Finsy, Maxime Guillaume and Doros N. Theodorou","doi":"10.1039/D4ME00199K","DOIUrl":"https://doi.org/10.1039/D4ME00199K","url":null,"abstract":"<p >We develop a generic computational methodology to understand and predict adhesion between polymers and solid substrates. The motion of coarse-grained polymer segments is tracked <em>via</em> a hybrid particle-field mesoscopic simulation method (BD/kMC) combining Brownian dynamics (BD) and kinetic Monte Carlo (kMC) for the entanglement dynamics as described by the slip-spring model. The method addresses entangled polymer films capped between solid surfaces under both quiescent and nonequilibrium conditions. The latter entail imposing constant rate extension along the aperiodic (normal) direction, while keeping the lateral dimensions constant. Experimentally relevant length scales and elongation rates can be addressed thanks to the coarse-graining inherent in the approach. These simulations are representative of “tack” tests, employed routinely for assessing the performance of soft adhesive materials. The performance of each interface is characterized by the stress–strain curves, yield stress, and toughness. The failure mechanism is determined upon analyzing the evolution of the stress–strain curve and the morphology of the fractured interfaces. The simulations are conducted over a broad parameter space by varying the rate of elongation, the rate constants for attachment/detachment of polymer segments to/from the surface, and the activation length. The latter describes the coupling with the pulling forces exerted on the particles at the interface by the rest of the polymer. Setting the activation length to zero is suitable for describing strong adhesives or highly compressible materials (foams). Under these conditions, toughness is maximized and increases significantly with elongation rate, sometimes leading to chain fracture. With increasing activation length the toughness of the interface decreases and detachment becomes more efficient at higher elongation rates since the increased stress accelerates the detachment process. In all cases considered here, toughness increases monotonically with adhesion. Furthermore, the yield stress increases consistently with increasing elongation rate due to the inability of the polymer to relax the imposed stress.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 394-412"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00199k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vasoactive intestinal peptide amphiphile micelle material properties influence their cell association and internalization","authors":"Xiaofei Wang, Agustin T. Barcellona, Fateme Nowruzi, Kambrie M. Brandt, Megan C. Schulte, Luke E. Kruse, Eric Dong, Adam G. Schrum, Esma S. Yolcu and Bret D. Ulery","doi":"10.1039/D4ME00167B","DOIUrl":"https://doi.org/10.1039/D4ME00167B","url":null,"abstract":"<p >Vasoactive intestinal peptide (VIP) is a promising anti-inflammatory peptide therapeutic that is known to induce biological effects by interacting with its cognate receptor (<em>i.e.</em>, VPAC) on the surface of antigen presenting cells (APCs). Little is known about how VPAC targeting affects APC behavior for VIP-based drug delivery systems like nano- and microparticles. This is further influenced by the fact that particulate material properties including chemistry, shape, and size are all known to influence APC behavior. In this study, peptide amphiphile micelles (PAMs) were employed as a modifiable platform to study the impact VPAC targeting and physical particle properties have on their association with macrophages. VIP amphiphile micelles (VIPAMs) and their scrambled peptide amphiphile micelle analogs (<small>^S</small>VIPAMs) were fabricated from various chemistries yielding particle batches that were comprised of spheres (10–20 nm in diameter) and/or cylinders of varying lengths (<em>i.e.</em>, 20–9000 nm). Micelle surface attachment to and internalization by macrophages were observed using confocal microscopy and their association was characterized by flow cytometry. The enclosed work provides strong evidence that macrophages rapidly bind VPAC specific micelles, and that micelle shape, size, and receptor-specificity all influence macrophage association and internalization. Specifically, a mixture of spherical and short cylindrical VIPAMs were able to achieve the greatest cell association which may correlate to their capacity to fully bind the VPAC receptors available on the surface of macrophages. These results provide the foundation of how nano- and microparticle physical properties and targeting capacity combine to influence their capacity to associate with APCs.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 7","pages":" 534-548"},"PeriodicalIF":3.2,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00167b?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards stable biologics: understanding co-excipient effects on hydrophobic interactions and solvent network integrity†","authors":"Jonathan W. P. Zajac, Praveen Muralikrishnan, Caryn L. Heldt, Sarah L. Perry and Sapna Sarupria","doi":"10.1039/D4ME00201F","DOIUrl":"https://doi.org/10.1039/D4ME00201F","url":null,"abstract":"<p >The formulation of biologics for increased shelf life stability is a complex task that depends on the chemical composition of both the active ingredient and any excipients in solution. A large number of unique excipients are typically required to stabilize biologics. However, it is not well-known how these excipient combinations influence biologics stability. To examine these formulations at the molecular level, we performed molecular dynamics simulations of arginine – a widely used excipient with unique properties – in solution both alone and with equimolar concentrations of lysine or glutamate. We studied the effects of these mixtures on a hydrophobic polymer model to isolate excipient mechanisms on hydrophobic interactions relevant in both protein folding and aggregation, crucial phenomena in biologics stability. We observed that arginine is the most effective single excipient in stabilizing hydrophobic polymer folding, and its effectiveness is augmented by lysine or glutamate addition. We decomposed the free energy of polymer folding/unfolding to identify that the key source of arginine–lysine and arginine–glutamate synergy is a reduction in destabilizing polymer–excipient interactions. We additionally applied principles from network theory to characterize the local solvent network embedding the hydrophobic polymer. Through this approach, we found arginine supports a more highly connected and stable local solvent network than in water, lysine, or glutamate solutions. These network properties are preserved when lysine or glutamate are added to arginine solutions. Taken together, our results highlight important molecular features in excipient solutions that establish the foundation for rational formulation design.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 6","pages":" 432-446"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks†","authors":"Rodrigo P. Ferreira, Rui Ding, Fengxue Zhang, Haihui Pu, Claire Donnat, Yuxin Chen and Junhong Chen","doi":"10.1039/D4ME00203B","DOIUrl":"https://doi.org/10.1039/D4ME00203B","url":null,"abstract":"<p >Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 345-356"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00203b?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The preparation of flame-retardant materials with complex shapes based on a dual-modulus network strategy†","authors":"Xiaoyu Dong, Lingyu Xu, Jiawei Li, Qiangkun Zhang, Zhongjun Cheng, Zhimin Xie, Hanyu Ma, Dongjie Zhang and Yuyan Liu","doi":"10.1039/D4ME00140K","DOIUrl":"https://doi.org/10.1039/D4ME00140K","url":null,"abstract":"<p >Flame-retardant thermosetting polymers are extensively used in construction materials and aerospace applications due to their inherent stability and performance characteristics. Traditional processing methods, however, are limited to producing simple geometries such as strips, blocks, and plates. Additionally, small molecule flame retardants exist in the resin matrix in a free form, and as the resin is used over time, these small molecule flame retardants tend to migrate, which deteriorates the flame-retardant performance of the material. Herein, we synthesized a flame retardant containing P and N elements with a double bond, which also serves as a curing agent, through molecular design and applied it in an acrylate–epoxy resin dual-modulus network system. Initial photopolymerization facilitated the creation of a low-modulus acrylate network, endowing the material with significant flexibility and allowing for arbitrary shaping. The double bonds present in the designed flame retardant ensure its integration into the acrylate network during photopolymerization, thereby mitigating migration issues. Subsequently, this flexible material undergoes thermal curing to form a high-modulus epoxy resin network, increasing the material's tensile modulus by up to 2500 times, tensile strength by up to 300 times, and glass transition temperature by up to 180 °C, resulting in a rigid material. Therefore, this work introduces an innovative approach to fabricating flame-retardant thermosetting materials with complex shapes while effectively reducing the migration of flame retardant molecules within the resin matrix.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 364-376"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating structural biophysical features for antigen-binding fragment crystallization via machine learning†","authors":"Krishna Gopal Chattaraj, Joana Ferreira, Allan S. Myerson and Bernhardt L. Trout","doi":"10.1039/D4ME00187G","DOIUrl":"https://doi.org/10.1039/D4ME00187G","url":null,"abstract":"<p >Antibody-based therapeutics continue to be an important pharmaceutical development modality. Crystallization of antibodies is important for structural characterization, but in addition has the potential for use as a separation method and for use as a dosage form. Nevertheless, bringing about controlled crystallization of an antibody remains a challenging task due to its large size, high degree of segmental flexibility, and the intricacy of all the occurring interactions (<em>e.g.</em>, protein–protein interactions, protein–solvent interactions, <em>etc.</em>). Methods to predict important contact sites could help to develop such crystallization methods. However, limited data and understanding have hitherto not allowed the development of such robust methods. This study employs machine learning combined with <em>in silico</em> modelling of crystal structures using available experimental structures to identify the crucial physicochemical features necessary for successful antibody crystallization in an attempt to remedy that gap. The developed method can with good accuracy distinguish crystal-site residues from non-crystal-site residues. A set of 510 descriptors is utilized to characterize each residue, which is treated as a distinct data point. Moreover, new algorithms have been developed to design novel descriptors that improve the model's predictive capabilities. Fragment antigen-binding (Fab) regions are investigated due to the scarcity of full-length monoclonal antibodies (mAbs) crystal structures. The current findings show that the extreme gradient boosting (XGBoost) algorithm effectively identifies crystal site residues, as evidenced by an AUPRC value that is more than 3-fold higher than that of the baseline model. The top-ranked descriptors indicate that crystal-site residues are primarily characterized by solvent-exposed residues with high spatial aggregation propensity (SAP), signifying hydrophobic patches, and their immediate surface-exposed neighbors. Moreover, these high SAP residues are often surrounded by other solvent-exposed residues that are either polar, charged, or both. In contrast, residues not involved in crystal interfaces generally lack these essential features, though some might be excluded due to specific crystal lattice arrangements. Additionally, reducing the feature set from 510 to the top 15% in the XGBoost model yields similar performance while significantly simplifying the model.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 5","pages":" 377-393"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00187g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bio-inspired approach to engineering water-responsive, mechanically-adaptive materials†","authors":"Daseul Jang, Yu-Tai Wong and LaShanda T. J. Korley","doi":"10.1039/D4ME00177J","DOIUrl":"https://doi.org/10.1039/D4ME00177J","url":null,"abstract":"<p >Inspired by a diverse array of hierarchical structures and mechanical function in spider silk, we leverage building blocks that can form non-covalent interactions to develop mechanically-tunable and water-responsive composite materials <em>via</em> hydrogen bonding modulation. Specifically, self-assembling peptide blocks consisting of poly(β-benzyl-<small>L</small>-aspartate) (PBLA) are introduced into a hydrophilic polyurea system. Using these peptide–polyurea hybrids (PPUs) as a hierarchical matrix, cellulose nanocrystals (CNCs) are incorporated to diversify the self-assembled nanostructures of PPUs through matrix–filler interactions. Our findings reveal that higher PBLA content in the PPUs reduces the magnitude of the stiffness differential due to the physical crosslinking induced by the peptide blocks. Additionally, the inclusion of CNCs in the PPU matrix increases the storage modulus in the dry state <img> but also diminishes the wet-state modulus <img> due to the shift of physical associations from peptidic arrangements to PBLA–CNC interactions, resulting in variations in the morphology of the PPU/CNC nanocomposites. This molecular design strategy allows for the development of adaptable materials with a broad range of water-responsive storage modulus switching <img>, spanning from ∼70 MPa to ∼400 MPa. This investigation highlights the potential of harnessing peptide assembly and peptide–cellulose interactions to achieve mechanical enhancement and water-responsiveness, providing insights for engineering next-generation responsive materials.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 4","pages":" 264-278"},"PeriodicalIF":3.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00177j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reweighting configurations generated by transferable, machine learned models for protein sidechain backmapping†","authors":"Jacob I. Monroe","doi":"10.1039/D4ME00198B","DOIUrl":"https://doi.org/10.1039/D4ME00198B","url":null,"abstract":"<p >Multiscale modeling requires the linking of models at different levels of detail, with the goal of gaining accelerations from lower fidelity models while recovering fine details from higher resolution models. Communication across resolutions is particularly important in modeling soft matter, where tight couplings exist between molecular-level details and mesoscale structures. While multiscale modeling of biomolecules has become a critical component in exploring their structure and self-assembly, backmapping from coarse-grained to fine-grained, or atomistic, representations presents a challenge, despite recent advances through machine learning. A major hurdle, especially for strategies utilizing machine learning, is that backmappings can only approximately recover the atomistic ensemble of interest. We demonstrate conditions for which backmapped configurations may be reweighted to exactly recover the desired atomistic ensemble. By training separate decoding models for each sidechain type, we develop an algorithm based on normalizing flows and geometric algebra attention to autoregressively propose backmapped configurations for any protein sequence. Critical for reweighting with modern protein force fields, our trained models include all hydrogen atoms in the backmapping and make probabilities associated with atomistic configurations directly accessible. We also demonstrate, however, that reweighting is extremely challenging despite state-of-the-art performance on recently developed metrics and generation of configurations with low energies in atomistic protein force fields. Through detailed analysis of configurational weights, we show that machine-learned backmappings must not only generate configurations with reasonable energies, but also correctly assign relative probabilities under the generative model. These are broadly important considerations in generative modeling of atomistic molecular configurations.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 4","pages":" 298-313"},"PeriodicalIF":3.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/me/d4me00198b?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular analysis and design using generative artificial intelligence via multi-agent modeling","authors":"Isabella Stewart and Markus J. Buehler","doi":"10.1039/D4ME00174E","DOIUrl":"10.1039/D4ME00174E","url":null,"abstract":"<p >We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring 7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human–AI and AI–AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a principal component analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed <em>via</em> their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 4","pages":" 314-337"},"PeriodicalIF":3.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The pivotal role of the carbonyl group in methoxy chalcones: comprehensive analyses of the structure and computational insights into binding affinity towards monoamine oxidase enzymes†","authors":"Keshav Kumar Harish, Hussien Ahmed Khamees, Keerthikumara Venkatesha, Omantheswara Nagaraja and Mahendra Madegowda","doi":"10.1039/D4ME00135D","DOIUrl":"https://doi.org/10.1039/D4ME00135D","url":null,"abstract":"<p >The present study explores the comprehensive investigations of two methoxy-oriented chalcone structures (HK1 and HK2), each featuring distinct halogen substituents (chlorine and bromine). The crystals of the derivatives were grown and confirmed <em>via</em> single-crystal X-ray diffraction (XRD), revealing that HK1 crystallizes in the orthorhombic system with the space group <em>Pbca</em>, while HK2 crystallizes in the monoclinic system with the space group <em>P</em>2<small>₁</small>/<em>c</em>. Intermolecular interactions, such as hydrogen bonding, π–π stacking, and van der Waals forces, were examined for their role in molecular assembly. Hirshfeld surface analysis and enrichment ratio provided further insights into these intermolecular interactions within the lattice. Density functional theory (DFT) calculations using the B3LYP functional and 6-311++G (d,p) basis set was employed to explore the electronic structure and physicochemical properties. Quantum theory of atoms in molecules (QTAIM) and non-covalent interaction (NCI) analyses elucidated the topology of these compounds. <em>In silico</em> biological studies of the derivatives were also carried out, focusing on their inhibitory potential targeting monoamine oxidase (MAO-A and MAO-B) enzymes. Drug-likeness was evaluated through ADME-T profiling predictions, followed by molecular docking and dynamics simulations to determine the favorable binding configurations within the MAOs. Dynamics simulations over a 100 ns period confirmed the stability of the ligand–protein complexes. Overall, the present study offers a deeper understanding of the structural intricacies of the reported molecules by providing valuable insights into their chemical and biological properties through molecular interactions.</p>","PeriodicalId":91,"journal":{"name":"Molecular Systems Design & Engineering","volume":" 4","pages":" 236-263"},"PeriodicalIF":3.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}