Lead(Pb)is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses.Under shock-wave loading,its dynamic mechanical behavior comprises two key phenomena:pla...Lead(Pb)is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses.Under shock-wave loading,its dynamic mechanical behavior comprises two key phenomena:plastic deformation and shock-induced phase transitions.The underlying mechanisms of these processes are still poorly understood.Revealing these mechanisms remains challenging for experimental approaches.Non-equilibrium molecular dynamics(NEMD)simulations are an alternative theoretical tool for studying dynamic responses,as they capture atomic-scale mechanisms such as defect evolution and deformation pathways.However,due to the limited accuracy of empirical interatomic potentials,the reliability of previous NEMD studies has been questioned.Using our newly developed machine learning potential for Pb-Sn alloys,we revisited the microstructural evolution in response to shock loading under various shock orientations.The results reveal that shock loading along the[001]orientation of Pb exhibits a fast,reversible,and massive phase transition and stacking-fault evolution.The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation.Loading along the[011]orientation leads to slow,irreversible plastic deformation,and a localized FCC-BCC phase transition in the Pitsch orientation relationship.This study provides crucial theoretical insights into the dynamic mechanical response of Pb,offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions.展开更多
Background:Lumbar disc degeneration(LDD)displays considerable heterogeneity in terms of clinical features and pathological changes.However,researchers have not clearly determined whether the transcriptome variations i...Background:Lumbar disc degeneration(LDD)displays considerable heterogeneity in terms of clinical features and pathological changes.However,researchers have not clearly determined whether the transcriptome variations in LDD could be used to identify or interpret the causes of heterogeneity in clinical features.This study aimed to identify the transcriptomic classification of degenerated discs in LDD patients and whether the molecular subtypes of LDD could be accurately predicted using clinical features.Methods:One hundred and twenty-two nucleus pulposus(NP)tissues from 108 patients were consecutively collected for bulk RNA sequencing(RNA-seq).An unsupervised clustering method was employed to analyze the bulk RNA matrix.Differential analysis was performed to characterize the transcriptional signatures and subtype-specific extracellular matrix(ECM)dysregulation.The cell subpopulation states of each subtype were inferred by integrating bulk and single-cell sequencing datasets.Transwell and dual-luciferase reporter gene assays were employed to investigate possible molecular mechanisms involved.Machine learning algorithm diagnostic prediction models were developed to correlate molecular classification with clinical features.Results:LDD was classified into 4 subtypes with distinct molecular signatures and ECM remodeling:C1 with collagenesis,C2 with ossification,C3 with low chondrogenesis,and C4 with fibrogenesis.Chond1-3 in C1 dominated disc collagenesis via the activation of the mechanosensors TRPV4 and PIEZO1;NP progenitor cells in C2 exhibited chondrogenic and osteogenic phenotypes;Chond1 in C3 was linked to a disrupted hypoxic microenvironment leading to reduced chondrogenesis;Macrophages in C4 played a crucial role in disc fibrogenesis via the secretion of tumor necrosis factor-α(TNF-α).Furthermore,the random forest diagnostic prediction model was proven to have a robust performance[area under the receiver operating characteristic(ROC)curve:0.9312;accuracy:0.84]in stratifying the molecular subtypes of LDD based on 12 clinical features.Conclusions:Our study delineates 4 distinct molecular subtypes of LDD that can be accurately stratified on the basis of clinical features.The identification of these subtypes would facilitate precise diagnostics and guide the development of personalized treatment strategies for LDD.展开更多
Electrolyte engineering with fluoroethers as solvents offers promising potential for high-performance lithium metal batteries.Despite recent progresses achieved in designing and synthesizing novel fluoroether solvents...Electrolyte engineering with fluoroethers as solvents offers promising potential for high-performance lithium metal batteries.Despite recent progresses achieved in designing and synthesizing novel fluoroether solvents,a systematic understanding of how fluorination patterns impact electrolyte performance is still lacking.We investigate the effects of fluorination patterns on properties of electrolytes using fluorinated 1,2-diethoxyethane(FDEE)as single solvents.By employing quantum calculations,molecular dynamics simulations,and interpretable machine learning,we establish significant correlations between fluorination patterns and electrolyte properties.Higher fluorination levels enhance FDEE stability but decrease conductivity.The symmetry of fluorination sites is critical for stability and viscosity,while exerting minimal influence on ionic conductivity.FDEEs with highly symmetric fluorination sites exhibit favorable viscosity,stability,and overall electrolyte performance.Conductivity primarily depends on lithium-anion dissociation or association.These findings provide design principles for rational fluoroether electrolyte design,emphasizing the trade-offs between stability,viscosity,and conductivity.Our work underscores the significance of considering fluorination patterns and molecular symmetry in the development of fluoroether-based electrolytes for advanced lithium batteries.展开更多
Machine learning(ML)can optimize the research paradigm and shorten the time from discovery to application of novel functional materials,pharmaceuticals,and fine chemicals.Besides supporting material and drug design,ML...Machine learning(ML)can optimize the research paradigm and shorten the time from discovery to application of novel functional materials,pharmaceuticals,and fine chemicals.Besides supporting material and drug design,ML is a potentially valuable tool for predictive modeling and process optimization.Herein,we first review the recent progress in data-driven ML for molecular crystal design,including property and structure predictions.ML can accelerate the development of the solvates,co-crystals,and colloidal nanocrystals,and improve the efficiency of crystal design.Next,this review summarizes ML algorithms for crystallization behavior prediction and process regulation.ML models support drug solubility prediction,particle agglomeration prediction,and spherical crystal design.ML-based in situ image processing can extract particle information and recognize crystal products.The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented.Finally,emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined.展开更多
The global rapid transition towards sustainable energy systems has heightened the demand for highperformance lithium metal batteries(LMBs),where understanding interfacial phenomena is paramount.In this contribution,we...The global rapid transition towards sustainable energy systems has heightened the demand for highperformance lithium metal batteries(LMBs),where understanding interfacial phenomena is paramount.In this contribution,we present an on-the-fly machine learning molecular dynamics(OTF-MLMD)approach to probe the complex side reactions at lithium metal anode–electrolyte interfaces with exceptional accuracy and computational efficiency.The machine learning force field(MLFF)was firstly validated in a bulk-phase system comprising twenty 1,2-dimethoxyethane(DME)molecules,demonstrating energy fluctuations and structural parameters in close agreement with ab initio molecular dynamics(AIMD)benchmarks.Subsequent simulations of lithium–DME and lithium–electrolyte interfaces revealed minimal discrepancies in energy,bond lengths,and net charge variations(notably in FSI-species),underscoring the method's DFT-level precision of the approach.A further small-scale interfacial model enabled on-the-fly training over a mere of 340 fs,which was then successfully transferred to a large-scale simulation encompassing nearly 300,000 atoms,representing the largest interfacial model in LMB research up to date.The hierarchical validation strategy not only establishes the robustness of the MLFF in capturing both interfacial and bulk-phase chemistry but also paves the way for statistically meaningful simulations of battery interfaces.The fruitful findings highlight the transformative potential of OTF-MLMD in bridging the gap between atomistic accuracy and macroscopic modeling,affording a universal approach to understand interfacial reactions in LMBs.展开更多
Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes.It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and des...Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes.It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems.Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions.This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples.The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data.The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials.The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations.The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering.We will also discuss the perspective of the broad applications of machine learning in chemical engineering.展开更多
GaP has been shown to be a promising photoelectrocatalyst for selective CO_(2)reduction to methanol.Due to the relevance of the interface structure to important processes such as electron/proton transfer,a detailed un...GaP has been shown to be a promising photoelectrocatalyst for selective CO_(2)reduction to methanol.Due to the relevance of the interface structure to important processes such as electron/proton transfer,a detailed understanding of the GaP(110)-water interfacial structure is of great importance.Ab initio molecular dynamics(AIMD)can be used for obtaining the microscopic information of the interfacial structure.However,the GaP(110)-water interface cannot converge to an equilibrated structure at the time scale of the AIMD simulation.In this work,we perform the machine learning accelerated molecular dynamics(MLMD)to overcome the difficulty of insufficient sampling by AIMD.With the help of MLMD,we unravel the microscopic information of the structure of the GaP(110)-water interface,and obtain a deeper understanding of the mechanisms of proton transfer at the GaP(110)-water interface,which will pave the way for gaining valuable insights into photoelectrocatalytic mechanisms and improving the performance of photoelectrochemical cells.展开更多
The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compound...The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.展开更多
Natural molecular machines have inspired the development of artificial molecular machines,which have the potential to revolutionize several areas of technology.Artificial molecular machines commonly employ molecular s...Natural molecular machines have inspired the development of artificial molecular machines,which have the potential to revolutionize several areas of technology.Artificial molecular machines commonly employ molecular switches,molecular motors,and molecular shuttles as fundamental building blocks.The observation of artificial molecular machines constructed by these building blocks can be highly challenging due to their small sizes and intricate behaviors.The use of modern instrumentation and advanced observational techniques plays a crucial role in the observation and characterization of molecular machines.Furthermore,a well-designed molecular structure is also a critical factor in making molecular ma-chines more observable.This review summarizes the common methods from diverse perspectives used to observe molecular machines and emphasizes the significance of comprehending their behaviors in the design of superior artificial molecular machines.展开更多
Abstract Abstract:We have demonstrated using vectorized parallel Lennard-Jones fluid program that vectorizing general-purpose parallel molecular package for simulating biomolecules which currently runs on the Connect...Abstract Abstract:We have demonstrated using vectorized parallel Lennard-Jones fluid program that vectorizing general-purpose parallel molecular package for simulating biomolecules which currently runs on the Connection Machine CM-5 using CMMD message passing would offer a significant improvement over 4 non-vectorized version. Our results indicate that the Lennard-Jones fluid program written in C*/CMNID is five times faster than the same program written in C/CMMD.展开更多
Background:Bladder cancer prognosis remains suboptimal despite advancements in research.Current molecular subtyping methods are resource-intensive,highlighting the need for efficient,cost-effective approaches to predi...Background:Bladder cancer prognosis remains suboptimal despite advancements in research.Current molecular subtyping methods are resource-intensive,highlighting the need for efficient,cost-effective approaches to predict BCa molecular subtypes.Method:We developed a predictive model for BCa molecular subtypes using machine learning(ML)and pathomics derived from Hematoxylin-Eosin stained pathological slides.A cohort of 353 patients from TCGA was employed,and image features were extracted for analysis.Pathomic signatures were constructed using the LASSO Cox regression algorithm,and a pathomic-clinical nomogram was developed and validated in training and testing cohorts.Results:Seventy distinct image features were identified from 150 pathomic signatures.The model demonstrated robust predictive ability,with AUCs of 0.833 and 0.822 in the training and validation cohorts,respectively.The addition of pathomic score,N stage,and M stage improved the model’s discrimination,achieving AUCs of 0.877 and 0.794 in the training and validation cohorts.Limitations include the lack of an external validation cohort.Conclusion:Our ML-based pathomics model shows promise in predicting BCa molecular subtypes and has the potential to enhance prognosis prediction and inform treatment strategies,marking a significant step towards precision medicine for BCa.展开更多
Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this proc...Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this process,molecular dynamics(MD)simulations can reveal the microscopic mechanisms of battery processes,thereby boosting the design of batteries.Compared to other MD simulation techniques,the machine learning force field(MLFF)holds the advantages of first-principles accuracy along with large spatial and temporal scale,offering opportunities to uncover new mechanisms in battery systems.This review presents a detailed overview of the fundamental principles and model types of MLFFs,as well as their applications in simulating the structure,transport properties,and chemical reaction properties of bulk battery materials and interfaces.Notably,we emphasize the long-range interaction corrections and constant-potential methods in the model design of MLFFs.Finally,we discuss the challenges and prospects of applying MLFF models in the research of batteries.展开更多
Machine learning methodologies have been extensively leveraged across diverse domains of chemical research,yielding remarkable outcomes,and exhibit substantial potential for impactful future applications within the fi...Machine learning methodologies have been extensively leveraged across diverse domains of chemical research,yielding remarkable outcomes,and exhibit substantial potential for impactful future applications within the field of supramolecular chemistry.The recognition of alkali metal ions by crown ethers is one of the most classic and widely applied host-vip interactions in supramolecular chemistry.Due to the numerous factors affecting the host-vip interaction,it remains a great challenge to achieve fast and accurate prediction of the binding constants between crown ethers and alkali metal ions.Herein,we report a highly accurate machine learning model that can effectively predict the binding constants between crown ethers and alkali metal ions,i.e.,CrownBind-IA,with a low RMSE of 0.68 logK units.Moreover,this model proves robust extrapolative capabilities by accurately predicting out-of-sample data.The establishment of CrownBind-IA demonstrates the promising application potentials of data-driven machine learning methods in supramolecular chemistry,and it will substantially reduce the time and expense of experimental trials and characterizations,promote the exploration of the mechanism of host-vip interactions,as well as the rational design of novel functional supramolecular host molecules.展开更多
Large-scale molecular dynamics(MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work, through the construction of a ...Large-scale molecular dynamics(MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work, through the construction of a chemical-environment-directing dataset, a reactive machine learning force field that accurately captures both reactive events and thermos-mechanical properties is developed. The force field achieves energy and force root-mean-square errors of 1.3 meV/atom and 159 meV/A, respectively, and operates approximately 1200 times faster than ab initio molecular dynamics. MD simulations demonstrate excellent predictive capabilities across multiple critical thermos-mechanical properties(radial distribution function, density, and elastic modulus), with results being well consistent with experimental values. In particular, the force field can provide accurate prediction of the bond dissociation energies for typical bonds with a mean absolute error of 7.8 kcal/mol(<8%), which enables the simulation of tensile-induced failure caused by chemical bond breaking. Our work demonstrates the capability of the machine learning force field to handle the extraordinary complexity of crosslinked epoxy systems, providing a valuable blueprint for future development of more generalized reactive force fields applicable to most polymers.展开更多
Metal ions play crucial roles in various biologi-cal functions,in-cluding maintain-ing homeostasis,regulating mus-cle contraction,and facilitating enzyme catalysis.However,accurately simulating the interaction between...Metal ions play crucial roles in various biologi-cal functions,in-cluding maintain-ing homeostasis,regulating mus-cle contraction,and facilitating enzyme catalysis.However,accurately simulating the interaction between metal ions and amino acid side chain analogs using high-level wave function theories remains challenging due to the significant computational costs involved.In this study,deep potential molecular dynamics(DeePMD)simulation was employed to investigate the solvation structure of the Mg^(2+)-Ac^(−)ion pair in aqueous solution.To address the computational bottleneck associated with expensive quan-tum mechanics(QM)methods,the Deep Kohn-Sham(DeePKS)approach was utilized,which allows us to generate highly accurate self-consistent energy functionals while significantly re-ducing computational costs.The root mean square error and mean absolute error of energies and atomic forces indicate close agreement between DeePKS predictions and QM strongly constrained and appropriately normed(SCAN)calculations.Moreover,the neural network potential(NNP)generated using the SCAN-level dataset predicted by DeePKS exhibits high-er accuracy compared to previous work,which employed a moderate BLYP functional.The potential of mean force for the Mg^(2+)-Ac−system was further examined,revealing a prefer-ence for monodentate coordination of Mg^(2+)with a~5.8 kcal/mol energy barrier between bidentate and monodentate geometries.Overall,this work provides a comprehensive,precise,and reliable methodology for investigating metal ions’properties in aqueous solutions.展开更多
Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of po...Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of polymer structures and their Tg values were created and represented as adjacency matrices based on molecular graph theory.Four key structural descriptors,flexibility,side chain occupancy length,polarity,and hydrogen bonding capacity,were extracted and used as inputs for ML models:Extra Trees(ET),Random Forest(RF),Gaussian Process Regression(GPR),and Gradient Boosting(GB).Among these,ET and GPR achieved the highest predictive performance,with R2 values of 0.97,and mean absolute errors(MAE)of approximately 7–7.5 K.The use of these extracted features significantly improved the prediction accuracy compared to previous studies.Feature importance analysis revealed that flexibility had the strongest influence on Tg,followed by side-chain occupancy length,hydrogen bonding,and polarity.This work demonstrates the potential of data-driven approaches in polymer science,providing a fast and reliable method for Tg prediction that does not require experimental inputs.展开更多
Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modelling, and optimization. In this work, an enhanced framework for pure component property prediction by...Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modelling, and optimization. In this work, an enhanced framework for pure component property prediction by using explainable machine learning methods is proposed. In this framework, the molecular representation method based on the connectivity matrix effectively considers atomic bonding relationships to automatically generate features. The supervised machine learning model random forest is applied for feature ranking and pooling. The adjusted R^(2) is introduced to penalize the inclusion of additional features, providing an assessment of the true contribution of features. The prediction results for normal boiling point (T_(b)), liquid molar volume (L_(mv)), critical temperature (T_(c)) and critical pressure (P_(c)) obtained using Artificial Neural Network and Gaussian Process Regression models confirm the accuracy of the molecular representation method. Comparison with GC based models shows that the root-mean-square error on the test set can be reduced by up to 83.8%. To enhance the interpretability of the model, a feature analysis method based on Shapley values is employed to determine the contribution of each feature to the property predictions. The results indicate that using the feature pooling method reduces the number of features from 13316 to 100 without compromising model accuracy. The feature analysis results for Tb, Lmv, Tc, and Pc confirms that different molecular properties are influenced by different structural features, aligning with mechanistic interpretations. In conclusion, the proposed framework is demonstrated to be feasible and provides a solid foundation for mixture component reconstruction and process integration modelling.展开更多
Accurately estimating protein–ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson–Boltzmann surfa...Accurately estimating protein–ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson–Boltzmann surface area(MM/PBSA)method in combination with various machine learning techniques to compute the binding free energies of protein–ligand interactions. Our results demonstrate that machine learning outperforms direct screening MM/PBSA calculations in predicting protein–ligand binding free energies. Notably, the random forest(RF) method exhibited the best predictive performance,with a Pearson correlation coefficient(rp) of 0.702 and a mean absolute error(MAE) of 1.379 kcal/mol. Furthermore, we analyzed feature importance rankings in the gradient boosting(GB), adaptive boosting(Ada Boost), and RF methods, and found that feature selection significantly impacted predictive performance. In particular, molecular weight(MW) and van der Waals(VDW) energies played a decisive role in the prediction. Overall, this study highlights the potential of combining machine learning methods with screening MM/PBSA for accurately predicting binding free energies in biosystems.展开更多
Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generat...Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.展开更多
Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mos...Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mostly relies on trial-and-error process with low efficiency.Here liposome formulation prediction models have been built by machine learning(ML)approaches.The important parameters of liposomes,including size,polydispersity index(PDI),zeta potential and encapsulation,are predicted individually by optimal ML algorithm,while the formulation features are also ranked to provide important guidance for formulation design.The analysis of key parameter reveals that drug molecules with logS[-3,-6],molecular complexity[500,1000]and XLogP3(≥2)are priority for preparing liposome with higher encapsulation.In addition,naproxen(NAP)and palmatine HCl(PAL)represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability.The consistency between predicted and experimental value verifies the satisfied accuracy of ML models.As the drug properties are critical for liposome particles,the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations.The modeling structure reveals that NAP molecules could distribute into lipid layer,while most PAL molecules aggregate in the inner aqueous phase of liposome.The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations.In summary,the general prediction models are built to predict liposome formulations,and the impacts of key factors are analyzed by combing ML with molecular modeling.The availability and rationality of these intelligent prediction systems have been proved in this study,which could be applied for liposome formulation development in the future.展开更多
基金supported by the National Key R&D Program of China(Grant No.2022YFA1004300)the National Natural Science Foundation of China(Grant No.12404004)。
文摘Lead(Pb)is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses.Under shock-wave loading,its dynamic mechanical behavior comprises two key phenomena:plastic deformation and shock-induced phase transitions.The underlying mechanisms of these processes are still poorly understood.Revealing these mechanisms remains challenging for experimental approaches.Non-equilibrium molecular dynamics(NEMD)simulations are an alternative theoretical tool for studying dynamic responses,as they capture atomic-scale mechanisms such as defect evolution and deformation pathways.However,due to the limited accuracy of empirical interatomic potentials,the reliability of previous NEMD studies has been questioned.Using our newly developed machine learning potential for Pb-Sn alloys,we revisited the microstructural evolution in response to shock loading under various shock orientations.The results reveal that shock loading along the[001]orientation of Pb exhibits a fast,reversible,and massive phase transition and stacking-fault evolution.The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation.Loading along the[011]orientation leads to slow,irreversible plastic deformation,and a localized FCC-BCC phase transition in the Pitsch orientation relationship.This study provides crucial theoretical insights into the dynamic mechanical response of Pb,offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions.
基金supported by the National Natural Science Foundation of China(32270887,82272507,32200654,82430079,and 82472519)the National Key Research and Development Program of China(2022YFA1103202)+7 种基金the Chongqing High-End Medical Talents for Middle-aged and Young(YXGD202408)the Army Scientific and Technological Innovation Talents Prioritized Suppor t Program(2023-124)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-ZDJO008)the Postdoctoral Innovative Talent Support Program(BX20220397)the Open Project of State Key Laboratory of TraumaBurns and Combined Injury(SFLKF202201)the Project for Enhancing Innovation of Army Medical University(2023XJS39)the Talent Innovation Training Program at the Army Medical Center(ZXZYTSYS09)。
文摘Background:Lumbar disc degeneration(LDD)displays considerable heterogeneity in terms of clinical features and pathological changes.However,researchers have not clearly determined whether the transcriptome variations in LDD could be used to identify or interpret the causes of heterogeneity in clinical features.This study aimed to identify the transcriptomic classification of degenerated discs in LDD patients and whether the molecular subtypes of LDD could be accurately predicted using clinical features.Methods:One hundred and twenty-two nucleus pulposus(NP)tissues from 108 patients were consecutively collected for bulk RNA sequencing(RNA-seq).An unsupervised clustering method was employed to analyze the bulk RNA matrix.Differential analysis was performed to characterize the transcriptional signatures and subtype-specific extracellular matrix(ECM)dysregulation.The cell subpopulation states of each subtype were inferred by integrating bulk and single-cell sequencing datasets.Transwell and dual-luciferase reporter gene assays were employed to investigate possible molecular mechanisms involved.Machine learning algorithm diagnostic prediction models were developed to correlate molecular classification with clinical features.Results:LDD was classified into 4 subtypes with distinct molecular signatures and ECM remodeling:C1 with collagenesis,C2 with ossification,C3 with low chondrogenesis,and C4 with fibrogenesis.Chond1-3 in C1 dominated disc collagenesis via the activation of the mechanosensors TRPV4 and PIEZO1;NP progenitor cells in C2 exhibited chondrogenic and osteogenic phenotypes;Chond1 in C3 was linked to a disrupted hypoxic microenvironment leading to reduced chondrogenesis;Macrophages in C4 played a crucial role in disc fibrogenesis via the secretion of tumor necrosis factor-α(TNF-α).Furthermore,the random forest diagnostic prediction model was proven to have a robust performance[area under the receiver operating characteristic(ROC)curve:0.9312;accuracy:0.84]in stratifying the molecular subtypes of LDD based on 12 clinical features.Conclusions:Our study delineates 4 distinct molecular subtypes of LDD that can be accurately stratified on the basis of clinical features.The identification of these subtypes would facilitate precise diagnostics and guide the development of personalized treatment strategies for LDD.
基金supported by the Major Research Plan of the National Natural Science Foundation of China(92372104)Guangdong Basic and Applied Basic Research Foundation(2022A1515110016)+3 种基金the Recruitment Program of Guangdong(2016ZT06C322)R&D Program of Guangzhou(2023A04J1364)Fundamental Research Funds for the Central Universities(2024ZYGXZR043)TCL Science and Technology Innovation Fund。
文摘Electrolyte engineering with fluoroethers as solvents offers promising potential for high-performance lithium metal batteries.Despite recent progresses achieved in designing and synthesizing novel fluoroether solvents,a systematic understanding of how fluorination patterns impact electrolyte performance is still lacking.We investigate the effects of fluorination patterns on properties of electrolytes using fluorinated 1,2-diethoxyethane(FDEE)as single solvents.By employing quantum calculations,molecular dynamics simulations,and interpretable machine learning,we establish significant correlations between fluorination patterns and electrolyte properties.Higher fluorination levels enhance FDEE stability but decrease conductivity.The symmetry of fluorination sites is critical for stability and viscosity,while exerting minimal influence on ionic conductivity.FDEEs with highly symmetric fluorination sites exhibit favorable viscosity,stability,and overall electrolyte performance.Conductivity primarily depends on lithium-anion dissociation or association.These findings provide design principles for rational fluoroether electrolyte design,emphasizing the trade-offs between stability,viscosity,and conductivity.Our work underscores the significance of considering fluorination patterns and molecular symmetry in the development of fluoroether-based electrolytes for advanced lithium batteries.
基金financially supported by the National Natural Science Foundation of China(22008173,21938009,and 21676179)the Major Key Technology Project of ShandongProvincial Key Research and Development Program(2021CXGC010514)the support of the China Scholarship Council。
文摘Machine learning(ML)can optimize the research paradigm and shorten the time from discovery to application of novel functional materials,pharmaceuticals,and fine chemicals.Besides supporting material and drug design,ML is a potentially valuable tool for predictive modeling and process optimization.Herein,we first review the recent progress in data-driven ML for molecular crystal design,including property and structure predictions.ML can accelerate the development of the solvates,co-crystals,and colloidal nanocrystals,and improve the efficiency of crystal design.Next,this review summarizes ML algorithms for crystallization behavior prediction and process regulation.ML models support drug solubility prediction,particle agglomeration prediction,and spherical crystal design.ML-based in situ image processing can extract particle information and recognize crystal products.The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented.Finally,emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined.
基金supported by the National Key Research and Development Program(2021YFB2500300)the National Natural Science Foundation of China(T2322015,92472101,22393903,22393900,and 52394170)+1 种基金the Beijing Municipal Natural Science Foundation(L247015 and L233004)Tsinghua University Initiative Scientific Research Program。
文摘The global rapid transition towards sustainable energy systems has heightened the demand for highperformance lithium metal batteries(LMBs),where understanding interfacial phenomena is paramount.In this contribution,we present an on-the-fly machine learning molecular dynamics(OTF-MLMD)approach to probe the complex side reactions at lithium metal anode–electrolyte interfaces with exceptional accuracy and computational efficiency.The machine learning force field(MLFF)was firstly validated in a bulk-phase system comprising twenty 1,2-dimethoxyethane(DME)molecules,demonstrating energy fluctuations and structural parameters in close agreement with ab initio molecular dynamics(AIMD)benchmarks.Subsequent simulations of lithium–DME and lithium–electrolyte interfaces revealed minimal discrepancies in energy,bond lengths,and net charge variations(notably in FSI-species),underscoring the method's DFT-level precision of the approach.A further small-scale interfacial model enabled on-the-fly training over a mere of 340 fs,which was then successfully transferred to a large-scale simulation encompassing nearly 300,000 atoms,representing the largest interfacial model in LMB research up to date.The hierarchical validation strategy not only establishes the robustness of the MLFF in capturing both interfacial and bulk-phase chemistry but also paves the way for statistically meaningful simulations of battery interfaces.The fruitful findings highlight the transformative potential of OTF-MLMD in bridging the gap between atomistic accuracy and macroscopic modeling,affording a universal approach to understand interfacial reactions in LMBs.
基金financial supports from the National Natural Science Foundation of China(21676245 and 51933009)the National Key Research and Development Program of China(2017YFB0702502)+1 种基金the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang(2019R01006)financial support provided by the Startup Funds of the University of Kentucky。
文摘Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes.It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems.Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions.This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples.The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data.The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials.The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations.The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering.We will also discuss the perspective of the broad applications of machine learning in chemical engineering.
基金the National Natural Science Foundation of China(22225302,21991151,21991150,22021001,92161113,91945301)the Fundamental Research Funds for the Central Universities(20720220009)+1 种基金the China Postdoctoral Science Foundation(2020 M682079)the Guangdong Basic and Applied Basic Research Foundation(2020A1515110539)。
文摘GaP has been shown to be a promising photoelectrocatalyst for selective CO_(2)reduction to methanol.Due to the relevance of the interface structure to important processes such as electron/proton transfer,a detailed understanding of the GaP(110)-water interfacial structure is of great importance.Ab initio molecular dynamics(AIMD)can be used for obtaining the microscopic information of the interfacial structure.However,the GaP(110)-water interface cannot converge to an equilibrated structure at the time scale of the AIMD simulation.In this work,we perform the machine learning accelerated molecular dynamics(MLMD)to overcome the difficulty of insufficient sampling by AIMD.With the help of MLMD,we unravel the microscopic information of the structure of the GaP(110)-water interface,and obtain a deeper understanding of the mechanisms of proton transfer at the GaP(110)-water interface,which will pave the way for gaining valuable insights into photoelectrocatalytic mechanisms and improving the performance of photoelectrochemical cells.
文摘The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.
基金supported by“Zhishan”Scholars Programs of Southeast University,Jiangsu Innovation Team Program,and the Fundamental Research Funds for the Central Universities.
文摘Natural molecular machines have inspired the development of artificial molecular machines,which have the potential to revolutionize several areas of technology.Artificial molecular machines commonly employ molecular switches,molecular motors,and molecular shuttles as fundamental building blocks.The observation of artificial molecular machines constructed by these building blocks can be highly challenging due to their small sizes and intricate behaviors.The use of modern instrumentation and advanced observational techniques plays a crucial role in the observation and characterization of molecular machines.Furthermore,a well-designed molecular structure is also a critical factor in making molecular ma-chines more observable.This review summarizes the common methods from diverse perspectives used to observe molecular machines and emphasizes the significance of comprehending their behaviors in the design of superior artificial molecular machines.
文摘Abstract Abstract:We have demonstrated using vectorized parallel Lennard-Jones fluid program that vectorizing general-purpose parallel molecular package for simulating biomolecules which currently runs on the Connection Machine CM-5 using CMMD message passing would offer a significant improvement over 4 non-vectorized version. Our results indicate that the Lennard-Jones fluid program written in C*/CMNID is five times faster than the same program written in C/CMMD.
基金supported by the Guangzhou Municipal Basic Research Program Jointly Funded by City,University,and Enterprise Special Project(2024A03J0907)the Natural Science Foundation of Guangdong Province(2024A1515013201)+1 种基金the National Natural Science Foundation of China(82203720,82203188,82002682,81972731,81773026,81972383)the Science and Technology Project of Zhongshan Municipality(No.2024B1032).
文摘Background:Bladder cancer prognosis remains suboptimal despite advancements in research.Current molecular subtyping methods are resource-intensive,highlighting the need for efficient,cost-effective approaches to predict BCa molecular subtypes.Method:We developed a predictive model for BCa molecular subtypes using machine learning(ML)and pathomics derived from Hematoxylin-Eosin stained pathological slides.A cohort of 353 patients from TCGA was employed,and image features were extracted for analysis.Pathomic signatures were constructed using the LASSO Cox regression algorithm,and a pathomic-clinical nomogram was developed and validated in training and testing cohorts.Results:Seventy distinct image features were identified from 150 pathomic signatures.The model demonstrated robust predictive ability,with AUCs of 0.833 and 0.822 in the training and validation cohorts,respectively.The addition of pathomic score,N stage,and M stage improved the model’s discrimination,achieving AUCs of 0.877 and 0.794 in the training and validation cohorts.Limitations include the lack of an external validation cohort.Conclusion:Our ML-based pathomics model shows promise in predicting BCa molecular subtypes and has the potential to enhance prognosis prediction and inform treatment strategies,marking a significant step towards precision medicine for BCa.
基金funding support from the National Natural Science Foundation of China(92472109,T2325012)the Program for HUST Academic Frontier Youth Team+1 种基金support from the Fundamental Research Funds for the Central Universities(HUST,5003120083)supported by the Postdoctoral Fellowship Program of CPSF(GZC20240532)。
文摘Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this process,molecular dynamics(MD)simulations can reveal the microscopic mechanisms of battery processes,thereby boosting the design of batteries.Compared to other MD simulation techniques,the machine learning force field(MLFF)holds the advantages of first-principles accuracy along with large spatial and temporal scale,offering opportunities to uncover new mechanisms in battery systems.This review presents a detailed overview of the fundamental principles and model types of MLFFs,as well as their applications in simulating the structure,transport properties,and chemical reaction properties of bulk battery materials and interfaces.Notably,we emphasize the long-range interaction corrections and constant-potential methods in the model design of MLFFs.Finally,we discuss the challenges and prospects of applying MLFF models in the research of batteries.
基金the financial support of the National Natural Science Foundation of China(Nos.22193020 and 22193022)the financial support of the National Natural Science Foundation of China(No.32301691)+4 种基金Tsinghua University Initiative Scientific Research Programthe financial support of the Science and Technology Innovation Program of Hunan Province(No.2023RC3188)the financial support of the Science and Technology Innovation Program of Hunan Province(No.2022RC1112)the Elite Youth Program by the Department of Education of Hunan Province(No.21B0666)the financial support of the Scientific Research Foundation of Hunan Provincial Education Department(No.24C0380)。
文摘Machine learning methodologies have been extensively leveraged across diverse domains of chemical research,yielding remarkable outcomes,and exhibit substantial potential for impactful future applications within the field of supramolecular chemistry.The recognition of alkali metal ions by crown ethers is one of the most classic and widely applied host-vip interactions in supramolecular chemistry.Due to the numerous factors affecting the host-vip interaction,it remains a great challenge to achieve fast and accurate prediction of the binding constants between crown ethers and alkali metal ions.Herein,we report a highly accurate machine learning model that can effectively predict the binding constants between crown ethers and alkali metal ions,i.e.,CrownBind-IA,with a low RMSE of 0.68 logK units.Moreover,this model proves robust extrapolative capabilities by accurately predicting out-of-sample data.The establishment of CrownBind-IA demonstrates the promising application potentials of data-driven machine learning methods in supramolecular chemistry,and it will substantially reduce the time and expense of experimental trials and characterizations,promote the exploration of the mechanism of host-vip interactions,as well as the rational design of novel functional supramolecular host molecules.
基金supported by the National Natural Science Foundation of China(Nos.52303116,52403125)the Natural Science Foundation of Hunan Province(No.2024JJ6461)+2 种基金the Science and Technology Innovation Program of Hunan Province(Nos.2022RC1080,2023RC3006)the Innovation Research Foundation of NUDT(Nos.22-ZZCX-076 and 23-ZZCX-ZZGC-01-10)the Key Research and Development Program of Hunan Province of China(No.2023ZJ1040).
文摘Large-scale molecular dynamics(MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work, through the construction of a chemical-environment-directing dataset, a reactive machine learning force field that accurately captures both reactive events and thermos-mechanical properties is developed. The force field achieves energy and force root-mean-square errors of 1.3 meV/atom and 159 meV/A, respectively, and operates approximately 1200 times faster than ab initio molecular dynamics. MD simulations demonstrate excellent predictive capabilities across multiple critical thermos-mechanical properties(radial distribution function, density, and elastic modulus), with results being well consistent with experimental values. In particular, the force field can provide accurate prediction of the bond dissociation energies for typical bonds with a mean absolute error of 7.8 kcal/mol(<8%), which enables the simulation of tensile-induced failure caused by chemical bond breaking. Our work demonstrates the capability of the machine learning force field to handle the extraordinary complexity of crosslinked epoxy systems, providing a valuable blueprint for future development of more generalized reactive force fields applicable to most polymers.
基金supported by the National Natural Sci-ence Foundation of China(No.22373065,No.62072296,No.22222303,No.22173032,No.21933010)the Nation-al Key R&D Program of China(No.2023YFF1204903)NYU-ECNU Center for Computational Chemistry at NYU Shanghai,the Opening Project of Shanghai Frontiers Science Research Center for Druggability of Cardiovascular noncoding RNA.
文摘Metal ions play crucial roles in various biologi-cal functions,in-cluding maintain-ing homeostasis,regulating mus-cle contraction,and facilitating enzyme catalysis.However,accurately simulating the interaction between metal ions and amino acid side chain analogs using high-level wave function theories remains challenging due to the significant computational costs involved.In this study,deep potential molecular dynamics(DeePMD)simulation was employed to investigate the solvation structure of the Mg^(2+)-Ac^(−)ion pair in aqueous solution.To address the computational bottleneck associated with expensive quan-tum mechanics(QM)methods,the Deep Kohn-Sham(DeePKS)approach was utilized,which allows us to generate highly accurate self-consistent energy functionals while significantly re-ducing computational costs.The root mean square error and mean absolute error of energies and atomic forces indicate close agreement between DeePKS predictions and QM strongly constrained and appropriately normed(SCAN)calculations.Moreover,the neural network potential(NNP)generated using the SCAN-level dataset predicted by DeePKS exhibits high-er accuracy compared to previous work,which employed a moderate BLYP functional.The potential of mean force for the Mg^(2+)-Ac−system was further examined,revealing a prefer-ence for monodentate coordination of Mg^(2+)with a~5.8 kcal/mol energy barrier between bidentate and monodentate geometries.Overall,this work provides a comprehensive,precise,and reliable methodology for investigating metal ions’properties in aqueous solutions.
文摘Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of polymer structures and their Tg values were created and represented as adjacency matrices based on molecular graph theory.Four key structural descriptors,flexibility,side chain occupancy length,polarity,and hydrogen bonding capacity,were extracted and used as inputs for ML models:Extra Trees(ET),Random Forest(RF),Gaussian Process Regression(GPR),and Gradient Boosting(GB).Among these,ET and GPR achieved the highest predictive performance,with R2 values of 0.97,and mean absolute errors(MAE)of approximately 7–7.5 K.The use of these extracted features significantly improved the prediction accuracy compared to previous studies.Feature importance analysis revealed that flexibility had the strongest influence on Tg,followed by side-chain occupancy length,hydrogen bonding,and polarity.This work demonstrates the potential of data-driven approaches in polymer science,providing a fast and reliable method for Tg prediction that does not require experimental inputs.
基金support from China Scholarship Council(CSC)(202406440073).
文摘Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modelling, and optimization. In this work, an enhanced framework for pure component property prediction by using explainable machine learning methods is proposed. In this framework, the molecular representation method based on the connectivity matrix effectively considers atomic bonding relationships to automatically generate features. The supervised machine learning model random forest is applied for feature ranking and pooling. The adjusted R^(2) is introduced to penalize the inclusion of additional features, providing an assessment of the true contribution of features. The prediction results for normal boiling point (T_(b)), liquid molar volume (L_(mv)), critical temperature (T_(c)) and critical pressure (P_(c)) obtained using Artificial Neural Network and Gaussian Process Regression models confirm the accuracy of the molecular representation method. Comparison with GC based models shows that the root-mean-square error on the test set can be reduced by up to 83.8%. To enhance the interpretability of the model, a feature analysis method based on Shapley values is employed to determine the contribution of each feature to the property predictions. The results indicate that using the feature pooling method reduces the number of features from 13316 to 100 without compromising model accuracy. The feature analysis results for Tb, Lmv, Tc, and Pc confirms that different molecular properties are influenced by different structural features, aligning with mechanistic interpretations. In conclusion, the proposed framework is demonstrated to be feasible and provides a solid foundation for mixture component reconstruction and process integration modelling.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 12222506, 12347102, 12447164, and 12174184)。
文摘Accurately estimating protein–ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson–Boltzmann surface area(MM/PBSA)method in combination with various machine learning techniques to compute the binding free energies of protein–ligand interactions. Our results demonstrate that machine learning outperforms direct screening MM/PBSA calculations in predicting protein–ligand binding free energies. Notably, the random forest(RF) method exhibited the best predictive performance,with a Pearson correlation coefficient(rp) of 0.702 and a mean absolute error(MAE) of 1.379 kcal/mol. Furthermore, we analyzed feature importance rankings in the gradient boosting(GB), adaptive boosting(Ada Boost), and RF methods, and found that feature selection significantly impacted predictive performance. In particular, molecular weight(MW) and van der Waals(VDW) energies played a decisive role in the prediction. Overall, this study highlights the potential of combining machine learning methods with screening MM/PBSA for accurately predicting binding free energies in biosystems.
基金supported by National Key R&D Program of China(2018YFA0901700)National Natural Science Foundation of China(22278241)+1 种基金a grant from the Institute Guo Qiang,Tsinghua University(2021GQG1016)Department of Chemical Engineering-iBHE Joint Cooperation Fund.
文摘Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.
基金supported by the Multi-Year Research Grants from the University of Macao(MYRG2019-00032-ICMS and MYRG2020-00113-ICMS)the Macao FDCT research grant(0108/2021/A)Molecular modeling was performed at the High-Performance Computing Cluster(HPCC),which is supported by the Information and Communication Technology Office(ICTO)of the University of Macao.
文摘Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mostly relies on trial-and-error process with low efficiency.Here liposome formulation prediction models have been built by machine learning(ML)approaches.The important parameters of liposomes,including size,polydispersity index(PDI),zeta potential and encapsulation,are predicted individually by optimal ML algorithm,while the formulation features are also ranked to provide important guidance for formulation design.The analysis of key parameter reveals that drug molecules with logS[-3,-6],molecular complexity[500,1000]and XLogP3(≥2)are priority for preparing liposome with higher encapsulation.In addition,naproxen(NAP)and palmatine HCl(PAL)represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability.The consistency between predicted and experimental value verifies the satisfied accuracy of ML models.As the drug properties are critical for liposome particles,the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations.The modeling structure reveals that NAP molecules could distribute into lipid layer,while most PAL molecules aggregate in the inner aqueous phase of liposome.The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations.In summary,the general prediction models are built to predict liposome formulations,and the impacts of key factors are analyzed by combing ML with molecular modeling.The availability and rationality of these intelligent prediction systems have been proved in this study,which could be applied for liposome formulation development in the future.
