The structure–activity relationship（SAR） study of a 1 2 3 4 4a 9a-hexahydro-1H-xanthene series of selective,human glucocorticoid receptor a（hGRa） antagonists is reported.Compounds were screened using hydroxyapati...The structure–activity relationship（SAR） study of a 1 2 3 4 4a 9a-hexahydro-1H-xanthene series of selective,human glucocorticoid receptor a（hGRa） antagonists is reported.Compounds were screened using hydroxyapatite-based GR binding and MMTV-Luc co-transfection reporter gene assays.Four different regions of the scaffold were modified to assess the effects on hGRa antagonism and related potency.Compound 8d exhibits an 8-fold better bioactivity than the original hit 1a,as well as an improved chemical stability,which make it a promising lead for the subsequent optimization.展开更多
An approach for designing the compliant adaptive wing leading edge with composite material is proposed based on the topology optimization. Firstly, an equivalent constitutive relationship of laminated glass fiber rein...An approach for designing the compliant adaptive wing leading edge with composite material is proposed based on the topology optimization. Firstly, an equivalent constitutive relationship of laminated glass fiber reinforced epoxy composite plates has been built based on the symmetric laminated plate theory. Then, an optimization objective function of compliant adaptive wing leading edge was used to minimize the least square error（LSE） between deformed curve and desired aerodynamics shape. After that, the topology structures of wing leading edge of different glass fiber ply-orientations were obtained by using the solid isotropic material with penalization（SIMP） model and sensitivity filtering technique. The desired aerodynamics shape of compliant adaptive wing leading edge was obtained based on the proposed approach. The topology structures of wing leading edge depend on the glass fiber ply-orientation. Finally, the corresponding morphing experiment of compliant wing leading edge with composite materials was implemented, which verified the morphing capability of topology structure and illustrated the feasibility for designing compliant wing leading edge. The present paper lays the basis of ply-orientation optimization for compliant adaptive wing leading edge in unmanned aerial vehicle（UAV） field.展开更多
Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distr...Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.展开更多
This paper deals with the problem of planned lead time calculation in a Material Requirement Planning （MRP） environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging co...This paper deals with the problem of planned lead time calculation in a Material Requirement Planning （MRP） environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered： multi-level serial-production and assembly systems. For the serial production systems （one type of component at each level）, a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and se＇~eral numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.展开更多
Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous r...Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.展开更多
Graph neural networks(GNNs)are revolutionizing drug design processes.Over the past five years,GNNs have emerged as transformative tools by accurately modeling molecular structures and interactions with binding targets...Graph neural networks(GNNs)are revolutionizing drug design processes.Over the past five years,GNNs have emerged as transformative tools by accurately modeling molecular structures and interactions with binding targets.Breakthroughs in predicting molecular properties,drug repurposing,toxicity assessment,and interaction analysis,along with generative GNNs enhancing virtual screening and novel molecule design,have significantly sped up drug discovery.These GNN-driven innovations improve predictive accuracy,cut development costs,and reduce late-stage failures.This review focuses on the interdisciplinary integration of GNNs throughout the discovery process,including lead discovery and optimization,synthetic route design,drug-target interaction prediction,and molecular property profiling,while critically evaluating the challenges in translational medicine.展开更多
As the second most abundant element on the Earth,silicon is widely used in materials science,medicinal chemistry,and organic synthesis[1].Especially in drug discovery,increasing attention has been paid to the“silicon...As the second most abundant element on the Earth,silicon is widely used in materials science,medicinal chemistry,and organic synthesis[1].Especially in drug discovery,increasing attention has been paid to the“silicon–carbon switch”strategy in optimizing lead molecules,as silicon can be considered as a carbon isostere due to the similarity in their chemical properties.The replacement of carbon atoms in bioactive molecules with silicon atoms can often result in higher activities compared to their non-silylated counterparts[2].展开更多
The PA-PB1 interface of the influenza polymerase is an attractive site for antiviral drug design.In this study,we designed and synthesized a mini-library of indazole-containing compounds based on rational structure-ba...The PA-PB1 interface of the influenza polymerase is an attractive site for antiviral drug design.In this study,we designed and synthesized a mini-library of indazole-containing compounds based on rational structure-based design to target the PB1-binding interface on PA.Biological evaluation of these compounds through a viral yield reduction assay revealed that compounds 27 and 31 both had a low micromolar range of the half maximal effective concentration(EC_(50))values against A/WSN/33(H1N1)(8.03 mmol/L for 27;14.6 mmol/L for 31),while the most potent candidate 24 had an EC_(50) value of 690 nM.Compound 24 was effective against different influenza strains including a pandemic H1N1 strain and an influenza B strain.Mechanistic studies confirmed that compound 24 bound PA with a K_(d) which equals to 1.88 mmol/L and disrupted the binding of PB1 to PA.The compound also decreased the lung viral titre in mice.In summary,we have identified a potent anti-influenza candidate with potency comparable to existing drugs and is effective against different viral strains.The therapeutic options for influenza infection have been limited by the occurrence of antiviral resistance,owing to the high mutation rate of viral proteins targeted by available drugs.To alleviate the public health burden of this issue,novel anti-influenza drugs are desired.In this study,we present our discovery of a novel class of indazolecontaining compounds which exhibited favourable potency against both influenza A and B viruses.The EC_(50) of the most potent compounds were within low micromolar to nanomolar concentrations.Furthermore,we show that the mouse lung viral titre decreased due to treatment with compound 24.Thus our findings identify promising candidates for further development of anti-influenza drugs suitable for clinical use.展开更多
KRAS-PDEδinteraction is revealed as a promising target for suppressing the function of mutant KRAS.The bottleneck in clinical development of PDEδinhibitors is the poor antitumor activity of known chemotypes.Here,we ...KRAS-PDEδinteraction is revealed as a promising target for suppressing the function of mutant KRAS.The bottleneck in clinical development of PDEδinhibitors is the poor antitumor activity of known chemotypes.Here,we identified novel spiro-cyclic PDEδinhibitors with potent antitumor activity both in vitro and in vivo.In particular,compound 36 l(KD=127±16 nmol/L)effectively bound to PDEδand interfered with KRAS-PDEδinteraction.It influenced the distribution of KRAS in Mia PaCa-2 cells,downregulated the phosphorylation of t-ERK and t-AKT and promoted apoptosis of the cells.The novel inhibitor 36 l exhibited significant in vivo antitumor potency in pancreatic cancer patient-derived xenograft(PDX)models.It represents a promising lead compound for investigating the druggability of KRAS-PDEδinteraction.展开更多
Receptor-interacting serine/threonine-protein kinase 1(RIPK1)functions as a key regulator in inflammation and cell death and is involved in mediating a variety of inflammatory or degenerative diseases.A number of allo...Receptor-interacting serine/threonine-protein kinase 1(RIPK1)functions as a key regulator in inflammation and cell death and is involved in mediating a variety of inflammatory or degenerative diseases.A number of allosteric RIPK1 inhibitors(RIPK1i)have been developed,and some of them have already advanced into clinical evaluation.Recently,selective RIPK1i that interact with both the allosteric pocket and the ATP-binding site of RIPK1 have started to emerge.Here,we report the rational development of a new series of type-II RIPK1i based on the rediscovery of a reported but mechanistically atypical RIPK3i.We also describe the structure-guided lead optimization of a potent,selective,and orally bioavailable RIPK1i,62,which exhibits extraordinary efficacies in mouse models of acute or chronic inflammatory diseases.Collectively,62 provides a useful tool for evaluating RIPK1 in animal disease models and a promising lead for further drug development.展开更多
基金supported in part by grants from the Ministry of Health of China (Nos. 2012ZX09304-011, 2013ZX09401003-005, 2013ZX09507001 and 2013ZX09507002)Shanghai Science and Technology Development Fund (No. 13DZ2290300)Thousand Talents Program in China
文摘The structure–activity relationship（SAR） study of a 1 2 3 4 4a 9a-hexahydro-1H-xanthene series of selective,human glucocorticoid receptor a（hGRa） antagonists is reported.Compounds were screened using hydroxyapatite-based GR binding and MMTV-Luc co-transfection reporter gene assays.Four different regions of the scaffold were modified to assess the effects on hGRa antagonism and related potency.Compound 8d exhibits an 8-fold better bioactivity than the original hit 1a,as well as an improved chemical stability,which make it a promising lead for the subsequent optimization.
基金co-supported by the National Natural Science Foundation of China (No. 51375383)Graduate Starting Seed Fund of Northwestern Polytechnical University of China (No. Z2014110)
文摘An approach for designing the compliant adaptive wing leading edge with composite material is proposed based on the topology optimization. Firstly, an equivalent constitutive relationship of laminated glass fiber reinforced epoxy composite plates has been built based on the symmetric laminated plate theory. Then, an optimization objective function of compliant adaptive wing leading edge was used to minimize the least square error（LSE） between deformed curve and desired aerodynamics shape. After that, the topology structures of wing leading edge of different glass fiber ply-orientations were obtained by using the solid isotropic material with penalization（SIMP） model and sensitivity filtering technique. The desired aerodynamics shape of compliant adaptive wing leading edge was obtained based on the proposed approach. The topology structures of wing leading edge depend on the glass fiber ply-orientation. Finally, the corresponding morphing experiment of compliant wing leading edge with composite materials was implemented, which verified the morphing capability of topology structure and illustrated the feasibility for designing compliant wing leading edge. The present paper lays the basis of ply-orientation optimization for compliant adaptive wing leading edge in unmanned aerial vehicle（UAV） field.
基金supported by the National Key R&D Program of China(Grant No.:2023YFF1205102)the National Natural Science Foundation of China(Grant Nos.:82273856,22077143,and 21977127)the Science Foundation of Guangzhou,China(No.:2Grant024A04J2172).
文摘Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.
文摘This paper deals with the problem of planned lead time calculation in a Material Requirement Planning （MRP） environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered： multi-level serial-production and assembly systems. For the serial production systems （one type of component at each level）, a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and se＇~eral numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.
基金Under the auspices of National Natural Science Foundation of China(No.52079103)。
文摘Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.
基金supported by the grants from the National Natural Science Foundation of China(82574241)the national key discipline of Second Military Medical University.
文摘Graph neural networks(GNNs)are revolutionizing drug design processes.Over the past five years,GNNs have emerged as transformative tools by accurately modeling molecular structures and interactions with binding targets.Breakthroughs in predicting molecular properties,drug repurposing,toxicity assessment,and interaction analysis,along with generative GNNs enhancing virtual screening and novel molecule design,have significantly sped up drug discovery.These GNN-driven innovations improve predictive accuracy,cut development costs,and reduce late-stage failures.This review focuses on the interdisciplinary integration of GNNs throughout the discovery process,including lead discovery and optimization,synthetic route design,drug-target interaction prediction,and molecular property profiling,while critically evaluating the challenges in translational medicine.
基金supported by the National Key R&D Program of China(2022YFA1506100)the National Natural Science Foundation of China(22471201,21901191,22501209,and 22503064)+2 种基金the China Postdoctoral Science Foundation(2023TQ0252 and 2023M742687)the Postdoctoral Foundation of Hubei Province(211000032)the Postdoctoral Fellowship Program of CPSF(GZC20231960)for financial support.
文摘As the second most abundant element on the Earth,silicon is widely used in materials science,medicinal chemistry,and organic synthesis[1].Especially in drug discovery,increasing attention has been paid to the“silicon–carbon switch”strategy in optimizing lead molecules,as silicon can be considered as a carbon isostere due to the similarity in their chemical properties.The replacement of carbon atoms in bioactive molecules with silicon atoms can often result in higher activities compared to their non-silylated counterparts[2].
基金supported by a Health and Medical Research Fund(HMRF),Hong Kong SAR(No.18170352,China)to Pang-Chui Shaw.
文摘The PA-PB1 interface of the influenza polymerase is an attractive site for antiviral drug design.In this study,we designed and synthesized a mini-library of indazole-containing compounds based on rational structure-based design to target the PB1-binding interface on PA.Biological evaluation of these compounds through a viral yield reduction assay revealed that compounds 27 and 31 both had a low micromolar range of the half maximal effective concentration(EC_(50))values against A/WSN/33(H1N1)(8.03 mmol/L for 27;14.6 mmol/L for 31),while the most potent candidate 24 had an EC_(50) value of 690 nM.Compound 24 was effective against different influenza strains including a pandemic H1N1 strain and an influenza B strain.Mechanistic studies confirmed that compound 24 bound PA with a K_(d) which equals to 1.88 mmol/L and disrupted the binding of PB1 to PA.The compound also decreased the lung viral titre in mice.In summary,we have identified a potent anti-influenza candidate with potency comparable to existing drugs and is effective against different viral strains.The therapeutic options for influenza infection have been limited by the occurrence of antiviral resistance,owing to the high mutation rate of viral proteins targeted by available drugs.To alleviate the public health burden of this issue,novel anti-influenza drugs are desired.In this study,we present our discovery of a novel class of indazolecontaining compounds which exhibited favourable potency against both influenza A and B viruses.The EC_(50) of the most potent compounds were within low micromolar to nanomolar concentrations.Furthermore,we show that the mouse lung viral titre decreased due to treatment with compound 24.Thus our findings identify promising candidates for further development of anti-influenza drugs suitable for clinical use.
基金supported by the National Key R&D Program of China(Grant No.2020YFA0509100)the National Natural Science Foundation of China(Grants 21738002,82030105,81725020 and 81903436)。
文摘KRAS-PDEδinteraction is revealed as a promising target for suppressing the function of mutant KRAS.The bottleneck in clinical development of PDEδinhibitors is the poor antitumor activity of known chemotypes.Here,we identified novel spiro-cyclic PDEδinhibitors with potent antitumor activity both in vitro and in vivo.In particular,compound 36 l(KD=127±16 nmol/L)effectively bound to PDEδand interfered with KRAS-PDEδinteraction.It influenced the distribution of KRAS in Mia PaCa-2 cells,downregulated the phosphorylation of t-ERK and t-AKT and promoted apoptosis of the cells.The novel inhibitor 36 l exhibited significant in vivo antitumor potency in pancreatic cancer patient-derived xenograft(PDX)models.It represents a promising lead compound for investigating the druggability of KRAS-PDEδinteraction.
基金We thank Prof.Junying Yuan(IRCBC of CAS,Shanghai,China)and Dr.Jidong Zhu(Etern Therapeutics,Shanghai,China)for their generous help on this work,Dr.Sudan He(ISM of CAMS,Suzhou,China)for providing RIPK3-FKBP NIH/3T3 cells,and National Facility for Protein Science in Shanghai(China)for the help in animal studies.This work was supported by grants from the National Natural Science Foundation of China(Grants Nos.21837004,82151212,and 32170755)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB39050500,China)Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX02,China).
文摘Receptor-interacting serine/threonine-protein kinase 1(RIPK1)functions as a key regulator in inflammation and cell death and is involved in mediating a variety of inflammatory or degenerative diseases.A number of allosteric RIPK1 inhibitors(RIPK1i)have been developed,and some of them have already advanced into clinical evaluation.Recently,selective RIPK1i that interact with both the allosteric pocket and the ATP-binding site of RIPK1 have started to emerge.Here,we report the rational development of a new series of type-II RIPK1i based on the rediscovery of a reported but mechanistically atypical RIPK3i.We also describe the structure-guided lead optimization of a potent,selective,and orally bioavailable RIPK1i,62,which exhibits extraordinary efficacies in mouse models of acute or chronic inflammatory diseases.Collectively,62 provides a useful tool for evaluating RIPK1 in animal disease models and a promising lead for further drug development.
