For discovering uncharted chemical space of ionic liquids(ILs)for CO_(2)dissolution,a reliable generative framework combining re-balanced variational autoencoder(VAE),artificial neural network(ANN),and particle swarm ...For discovering uncharted chemical space of ionic liquids(ILs)for CO_(2)dissolution,a reliable generative framework combining re-balanced variational autoencoder(VAE),artificial neural network(ANN),and particle swarm optimization(PSO)is developed based on a comprehensive experimental solubility database from literature.The re-balanced VAE transforms the chemical space of ILs into continuous latent space,which is demonstrated by tdistributed stochastic neighbor embedding(t-SNE)visualization and sampled ions of the latent space.ANN is connected with the re-balanced VAE to predict the CO_(2)solubility and the resultant VAE-ANN model achieves a low mean absolute error(MAE)of 0.022 on the test set.Lastly,the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility.A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO.Their CO_(2)solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO_(2)solubility database under 298.15 K and 100 kPa.The results demonstrate a notably larger distribution of higher CO_(2)solubility in optimized ILs after PSO,which effectively points out the significance and directions for exploring the wide IL chemical space.展开更多
Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold...Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC_(50))of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal“dead ends”and“safe bets”for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogeneoxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.展开更多
A discharge plasma reactor with a point-to-plane structure was widely studied experimentally in wastewater treatment.In order to improve the utilization efficiency of active species and the energy efficiency of this k...A discharge plasma reactor with a point-to-plane structure was widely studied experimentally in wastewater treatment.In order to improve the utilization efficiency of active species and the energy efficiency of this kind of discharge plasma reactor during wastewater treatment,the electrode configuration of the point-to-plane corona discharge reactor was studied by evaluating the effects of discharge spacing and adjacent point distance on discharge power and discharge energy density,and then dye-containing wastewater decoloration experiments were conducted on the basis of the optimum electrode configuration.The experimental results of the discharge characteristics showed that high discharge power and discharge energy density were achieved when the ratio of discharge spacing to adjacent point distance（d/s） was 0.5.Reactive Brilliant Blue（RBB） wastewater treatment experiments presented that the highest RBB decoloration efficiency was observed at d/s of 0.5,which was consistent with the result obtained in the discharge characteristics experiments.In addition,the biodegradability of RBB wastewater was enhanced greatly after discharge plasma treatment under the optimum electrode configuration.RBB degradation processes were analyzed by GC-MS and IC,and the possible mechanism for RBB decoloration was also discussed.展开更多
Machine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large datasets.Here,we focus on the spherical precipitation behavior of polymers and computationally extract a h...Machine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large datasets.Here,we focus on the spherical precipitation behavior of polymers and computationally extract a hidden trend that is orthogonal to the availability bias in the chemical space.For constructing a dataset,four polymers were precipitated from 416 solvent/nonsolvent combinations,and the morphology of the resulting precipitates were collected.The dataset was subjected to computational investigations consisting of principal component analysis and machine learning based on random forest model and support vector machine.Thereby,we eliminated the effect of the availability bias and found a linear combination of Hansen parameters to be the most suitable variable for predicting precipitation behavior.The predicted appropriate solvents are those with low hydrogen bonding capability,low polarity,and small molecular volume.Furthermore,we found that the capability for spherical precipitation is orthogonal to the availability bias and forms an extraordinary axis in Hansen space,which is the origin of the conventional difficulty in identifying the trend.The extraordinary axis points toward a void region,indicating the potential value of synthesizing novel solvents located therein.展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
Data-driven machine learning(ML)has demonstrated tremendous potential in material property predictions.However,the scarcity of materials data with costly property labels in the vast chemical space presents a significa...Data-driven machine learning(ML)has demonstrated tremendous potential in material property predictions.However,the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships.Here,we propose a novel hierarchy-boosted funnel learning(HiBoFL)framework,which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity(κ_(L)).By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands,we achieve efficient and interpretable supervised predictions of ultralowκ_(L),thereby circumventing large-scale brute-force ab initio calculations without clear objectives.As a result,we provide a list of candidates with ultralowκ_(L)for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity.This HiBoFL framework offers a novel practical pathway for accelerating the discovery of functional materials.展开更多
X-ray Diffraction analysis is crucial for understanding material structures but is hindered by complex patterns and the need for expert interpretation.Deep learning offers automation in phase identification but faces ...X-ray Diffraction analysis is crucial for understanding material structures but is hindered by complex patterns and the need for expert interpretation.Deep learning offers automation in phase identification but faces challenges such as data scarcity,overconfidence in predictions and lack of interpretability.This study addresses these by employing Template Element Replacement to generate a perovskite chemical space containing physically unstable virtual structures,enhancing model understanding of XRD-crystal structure relationships and improving classification accuracy by~5%.A Bayesian-VGGNet model was developed,achieving 84%accuracy on simulated spectra and 75%on external experimental data,while simultaneously estimating prediction uncertainty.Evaluation using Bayesian methods revealed low entropy values,indicating high model confidence.Quantifying the importance of input features to crystal symmetry,aligning significant features of seven crystal systems with physical principles.These approaches enhance the model’s robustness and reliability,making it suitable for practical applications.展开更多
The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a singl...The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a single catalyst type.In this work,we developed an efficient ML model to predict hydrogen evolution reaction(HER)activity across diverse catalysts.By minimizing features,we introduced a key energy-related featureφ=Nd0^(2)=ψ0,which correlates with HER free energy.Using just ten features,the Extremely Randomized Trees model achieved R^(2)=0.922.We predicted 132 new catalysts from the Material Project database,among which several exhibited promising HER performance.The time consumed by theML model for predictions is one 200,000th of that required by traditional density functional theory(DFT)methods.The model provides an efficient approach for discovering high-performance HER catalysts using a small number of key features and offers insights for the development of other catalysts.展开更多
Objective： To study the pharmacological properties of Tougu Xiaotong Granule （透骨消痛颗粒, TGXTG） in preventing and treating knee osteoarthritis （KOA） at the molecular level. Methods： The computational methods,...Objective： To study the pharmacological properties of Tougu Xiaotong Granule （透骨消痛颗粒, TGXTG） in preventing and treating knee osteoarthritis （KOA） at the molecular level. Methods： The computational methods, including principal component analysis, molecular docking, target-ligand space distribution, and the predictions of absorption, distribution, metabolism, excretion and toxicity （ADMET）, were introduced to characterize the molecules in TGXTG. Results： The structural properties of molecules in TGXTG were more diverse than those of the drug/drug-like molecules, and TGXTG could interact with significant target enzymes related to KOA. In addition, the cluster of effective components was preliminarily identified by the target-ligand space distributions. As to the results of ADMET properties, some of them were unsatisfactory, and were merely regarded as references here. Conclusion： Based on this computational pharmacology study, TGXTG is a broad- spectrum recipe inhibiting many important target enzymes, which could effectively postpone the degeneration of cartilage by coordinately inhibiting the biological effects of cytokines, matrix metallopeptidase 3, and oxygen free radicals.展开更多
Objective：To study the polypharmacological mechanism of herbal pair Chuanxiong Rhizome-Paeonia Albifora Pall（HP CXR-PAP） on the treatment for osteoarthritis（OA）.Methods：Chemical space was used to discuss the sim...Objective：To study the polypharmacological mechanism of herbal pair Chuanxiong Rhizome-Paeonia Albifora Pall（HP CXR-PAP） on the treatment for osteoarthritis（OA）.Methods：Chemical space was used to discuss the similarities and differences between the molecule sets of HP CXR-PAP and drugs.Docking protocol was used to study the interaction between HP CXR-PAP and OA target enzymes.The similarities and differences of HP CXR-PAP and drugs in target spaces were elucidated by network features.Results：The plots between the molecule sets of HP CXR-PAP and drugs in chemical space had the majority in the same region, and compounds from HP CXR-PAP covered a much larger additional region of space than drug molecules, which denoted the diverse structural properties in the molecule set of HP CXR-PAP.The molecules in HP CXR-PAP had the properties of promiscuous drugs and combination drug,and both HP CXR-PAP ligand-target interaction network and drug ligand-target interaction network were similar in the interaction profiles and network features,which revealed the effects of multicomponent and multitarget.Conclusion：The clue of potential synergism was obtained in curing OA disease by Chinese medicine,which revealed the advantages of Chinese medicine for targeting osteoarthritis disease.展开更多
Two-dimensional(2D)ferromagnets with out-of-plane(OOP)magnetic anisotropy are potential candidates for realizing the next-generation memory devices with ultra-low power consumption and high storage density.However,a s...Two-dimensional(2D)ferromagnets with out-of-plane(OOP)magnetic anisotropy are potential candidates for realizing the next-generation memory devices with ultra-low power consumption and high storage density.However,a scalable approach to synthesize 2D magnets with OOP anisotropy directly on the complimentary metal-oxide semiconductor(CMOS)compatible substrates has not yet been mainly explored,which hinders the practical application of 2D magnets.This work demonstrates a cascaded space confined chemical vapor deposition(CS-CVD)technique to synthesize 2D FexGeTe_(2) ferromagnets.The weight fraction of iron(Fe)in the precursor controls the phase purity of the as-grown FexGeTe2.As a result,high-quality Fe_(3)GeTe_(2) and Fe_(5)GeTe_(2) flakes have been grown selectively using the CS-CVD technique.Curie temperature(Tc)of the as-grown FexGeTe2 can be up to-280 K,nearly room temperature.The thickness and temperature-dependent magnetic studies on the Fe_(5)GeTe_(2) reveal a 2D Ising to 3D XY behavior.Also,Terahertz spectroscopy experiments on Fe_(5)GeTe_(2) display the highest conductivity among other FexGeTe_(2) 2D magnets.The results of this work indicate a scalable pathway for the direct growth and integration of 2D ternary magnets on CMOS-based substrates to develop spintronic memory devices.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under the grants Nos.22278134,22208098,and 21CAA01709.
文摘For discovering uncharted chemical space of ionic liquids(ILs)for CO_(2)dissolution,a reliable generative framework combining re-balanced variational autoencoder(VAE),artificial neural network(ANN),and particle swarm optimization(PSO)is developed based on a comprehensive experimental solubility database from literature.The re-balanced VAE transforms the chemical space of ILs into continuous latent space,which is demonstrated by tdistributed stochastic neighbor embedding(t-SNE)visualization and sampled ions of the latent space.ANN is connected with the re-balanced VAE to predict the CO_(2)solubility and the resultant VAE-ANN model achieves a low mean absolute error(MAE)of 0.022 on the test set.Lastly,the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility.A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO.Their CO_(2)solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO_(2)solubility database under 298.15 K and 100 kPa.The results demonstrate a notably larger distribution of higher CO_(2)solubility in optimized ILs after PSO,which effectively points out the significance and directions for exploring the wide IL chemical space.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82173699 and 32200531)Shanghai Jiao Tong University Trans-Med Awards Research,China(STAR Project No.:20230101)Shanghai Science and Technol-ogy Commission,China(Grant No.:23DZ2290600).
文摘Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC_(50))of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal“dead ends”and“safe bets”for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogeneoxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.
基金supported by China's Postdoctoral Science Foundation(No.2014M562460)the Initiative Funding Programs for Doctoral Research of Northwest A&F University(No.2013BSJJ121)National Natural Science Foundation of China(No.21107085)
文摘A discharge plasma reactor with a point-to-plane structure was widely studied experimentally in wastewater treatment.In order to improve the utilization efficiency of active species and the energy efficiency of this kind of discharge plasma reactor during wastewater treatment,the electrode configuration of the point-to-plane corona discharge reactor was studied by evaluating the effects of discharge spacing and adjacent point distance on discharge power and discharge energy density,and then dye-containing wastewater decoloration experiments were conducted on the basis of the optimum electrode configuration.The experimental results of the discharge characteristics showed that high discharge power and discharge energy density were achieved when the ratio of discharge spacing to adjacent point distance（d/s） was 0.5.Reactive Brilliant Blue（RBB） wastewater treatment experiments presented that the highest RBB decoloration efficiency was observed at d/s of 0.5,which was consistent with the result obtained in the discharge characteristics experiments.In addition,the biodegradability of RBB wastewater was enhanced greatly after discharge plasma treatment under the optimum electrode configuration.RBB degradation processes were analyzed by GC-MS and IC,and the possible mechanism for RBB decoloration was also discussed.
基金CREST,Grant/Award Number:JPMJCR20T4ACT-X,Grant/Award Number:JPMJAX201JGrant-in-Aid for Young Scientist,Grant/Award Number:JP22K14656。
文摘Machine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large datasets.Here,we focus on the spherical precipitation behavior of polymers and computationally extract a hidden trend that is orthogonal to the availability bias in the chemical space.For constructing a dataset,four polymers were precipitated from 416 solvent/nonsolvent combinations,and the morphology of the resulting precipitates were collected.The dataset was subjected to computational investigations consisting of principal component analysis and machine learning based on random forest model and support vector machine.Thereby,we eliminated the effect of the availability bias and found a linear combination of Hansen parameters to be the most suitable variable for predicting precipitation behavior.The predicted appropriate solvents are those with low hydrogen bonding capability,low polarity,and small molecular volume.Furthermore,we found that the capability for spherical precipitation is orthogonal to the availability bias and forms an extraordinary axis in Hansen space,which is the origin of the conventional difficulty in identifying the trend.The extraordinary axis points toward a void region,indicating the potential value of synthesizing novel solvents located therein.
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
基金the support from the National Natural Science Foundation of China(No.11935010)the National Key R&D Program of China(No.2023YFA1406900 and No.2022YFA1404400)+2 种基金the Natural Science Foundation of Shanghai(No.23ZR1481200)the Program of Shanghai Academic Research Leader(No.23XD1423800)the Opening Project of Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology.
文摘Data-driven machine learning(ML)has demonstrated tremendous potential in material property predictions.However,the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships.Here,we propose a novel hierarchy-boosted funnel learning(HiBoFL)framework,which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity(κ_(L)).By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands,we achieve efficient and interpretable supervised predictions of ultralowκ_(L),thereby circumventing large-scale brute-force ab initio calculations without clear objectives.As a result,we provide a list of candidates with ultralowκ_(L)for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity.This HiBoFL framework offers a novel practical pathway for accelerating the discovery of functional materials.
基金support from the National Key Research and Development Program of China(grant nos.2021YFA1200904,2020YFA0710700)the National Natural Science Foundation of China(grant nos.12375326)+1 种基金the Innovation Program for IHEP(grant nos.E35457U210)the Postdoctoral Fellowship Program and China Postdoctoral Science Foundation(grant nos.BX20240205),and the directional institutionalized scientific research platform relying on Beijing Synchrotron Radiation Facility of Chinese Academy of Sciences.
文摘X-ray Diffraction analysis is crucial for understanding material structures but is hindered by complex patterns and the need for expert interpretation.Deep learning offers automation in phase identification but faces challenges such as data scarcity,overconfidence in predictions and lack of interpretability.This study addresses these by employing Template Element Replacement to generate a perovskite chemical space containing physically unstable virtual structures,enhancing model understanding of XRD-crystal structure relationships and improving classification accuracy by~5%.A Bayesian-VGGNet model was developed,achieving 84%accuracy on simulated spectra and 75%on external experimental data,while simultaneously estimating prediction uncertainty.Evaluation using Bayesian methods revealed low entropy values,indicating high model confidence.Quantifying the importance of input features to crystal symmetry,aligning significant features of seven crystal systems with physical principles.These approaches enhance the model’s robustness and reliability,making it suitable for practical applications.
基金supported by the National Key R&D Program of China(Grant No.2021YFB3500403)the Youth Fund of the National Natural Science Foundation of China(Grant no.52305443).We gratefully acknowledge HZWTECH for providing computational facilities.
文摘The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a single catalyst type.In this work,we developed an efficient ML model to predict hydrogen evolution reaction(HER)activity across diverse catalysts.By minimizing features,we introduced a key energy-related featureφ=Nd0^(2)=ψ0,which correlates with HER free energy.Using just ten features,the Extremely Randomized Trees model achieved R^(2)=0.922.We predicted 132 new catalysts from the Material Project database,among which several exhibited promising HER performance.The time consumed by theML model for predictions is one 200,000th of that required by traditional density functional theory(DFT)methods.The model provides an efficient approach for discovering high-performance HER catalysts using a small number of key features and offers insights for the development of other catalysts.
基金Supported by the National Natural Science Foundation of China (No.30672701)Chen Ke-ji Integrative Medicine Development Foundation(No.2008J1004-27CKJ2008064)Major Project of Department of Science & Technology,Fujian Province(No. 2006Y0016,2009Y0029)
文摘Objective： To study the pharmacological properties of Tougu Xiaotong Granule （透骨消痛颗粒, TGXTG） in preventing and treating knee osteoarthritis （KOA） at the molecular level. Methods： The computational methods, including principal component analysis, molecular docking, target-ligand space distribution, and the predictions of absorption, distribution, metabolism, excretion and toxicity （ADMET）, were introduced to characterize the molecules in TGXTG. Results： The structural properties of molecules in TGXTG were more diverse than those of the drug/drug-like molecules, and TGXTG could interact with significant target enzymes related to KOA. In addition, the cluster of effective components was preliminarily identified by the target-ligand space distributions. As to the results of ADMET properties, some of them were unsatisfactory, and were merely regarded as references here. Conclusion： Based on this computational pharmacology study, TGXTG is a broad- spectrum recipe inhibiting many important target enzymes, which could effectively postpone the degeneration of cartilage by coordinately inhibiting the biological effects of cytokines, matrix metallopeptidase 3, and oxygen free radicals.
基金Supported by the National Natural Science Foundation of China (No.81072826)CHEN Ke-ji Integrative Medicine Development Foundation(No.CKJ2010032)Emphasis Foundation of Fujian Provincial Department of Science and Technology(No. 2010Y0029)
文摘Objective：To study the polypharmacological mechanism of herbal pair Chuanxiong Rhizome-Paeonia Albifora Pall（HP CXR-PAP） on the treatment for osteoarthritis（OA）.Methods：Chemical space was used to discuss the similarities and differences between the molecule sets of HP CXR-PAP and drugs.Docking protocol was used to study the interaction between HP CXR-PAP and OA target enzymes.The similarities and differences of HP CXR-PAP and drugs in target spaces were elucidated by network features.Results：The plots between the molecule sets of HP CXR-PAP and drugs in chemical space had the majority in the same region, and compounds from HP CXR-PAP covered a much larger additional region of space than drug molecules, which denoted the diverse structural properties in the molecule set of HP CXR-PAP.The molecules in HP CXR-PAP had the properties of promiscuous drugs and combination drug,and both HP CXR-PAP ligand-target interaction network and drug ligand-target interaction network were similar in the interaction profiles and network features,which revealed the effects of multicomponent and multitarget.Conclusion：The clue of potential synergism was obtained in curing OA disease by Chinese medicine,which revealed the advantages of Chinese medicine for targeting osteoarthritis disease.
基金supported from National Research Foundation Singapore programme NRF-CRP22-2019-0007,NRF-CRP22-2019-0004 and NRF-CRP21-2018-0007supported by the Ministry of Education,Singapore,under its AcRF Tier 3 Programme‘Geometrical Quantum Materials’(MOE2018-T3-1-002),AcRF Tier 2(MOE2019-T2-2-105)and AcRF Tier 1 RG4/17 and RG7/18We also thank the funding support from National Research foundation(NRF-CRP22-2019-0004).
文摘Two-dimensional(2D)ferromagnets with out-of-plane(OOP)magnetic anisotropy are potential candidates for realizing the next-generation memory devices with ultra-low power consumption and high storage density.However,a scalable approach to synthesize 2D magnets with OOP anisotropy directly on the complimentary metal-oxide semiconductor(CMOS)compatible substrates has not yet been mainly explored,which hinders the practical application of 2D magnets.This work demonstrates a cascaded space confined chemical vapor deposition(CS-CVD)technique to synthesize 2D FexGeTe_(2) ferromagnets.The weight fraction of iron(Fe)in the precursor controls the phase purity of the as-grown FexGeTe2.As a result,high-quality Fe_(3)GeTe_(2) and Fe_(5)GeTe_(2) flakes have been grown selectively using the CS-CVD technique.Curie temperature(Tc)of the as-grown FexGeTe2 can be up to-280 K,nearly room temperature.The thickness and temperature-dependent magnetic studies on the Fe_(5)GeTe_(2) reveal a 2D Ising to 3D XY behavior.Also,Terahertz spectroscopy experiments on Fe_(5)GeTe_(2) display the highest conductivity among other FexGeTe_(2) 2D magnets.The results of this work indicate a scalable pathway for the direct growth and integration of 2D ternary magnets on CMOS-based substrates to develop spintronic memory devices.
