采用比较分子场分析(comparative molecular field analysis,CoMFA)和比较分子相似性指数分析(comparative similarity indices analysis,CoMSIA)方法,分别对一系列新烟碱类杀虫剂及相关化合物烟碱乙酰胆碱受体激动剂进行了结构与活性...采用比较分子场分析(comparative molecular field analysis,CoMFA)和比较分子相似性指数分析(comparative similarity indices analysis,CoMSIA)方法,分别对一系列新烟碱类杀虫剂及相关化合物烟碱乙酰胆碱受体激动剂进行了结构与活性关系的研究,构建了CoMFA及CoMSIA模型;该模型具有较好的预报能力和拟合能力,CoMFA:q2=0.563,r2=0.950;CoMSIA:q2=0.657,r2=0.973.通过三维等势图分析得出了对新烟碱类活性影响较大的基团或原子,为新烟碱类化合物的进一步研究提供了依据.展开更多
使用比较分子力场分析法(CoMFA)和比较分子相似性指数法(CoMSIA)对33个已报道的喹啉酮类BRD4抑制剂进行3D-QSAR模型建立,研究了其化学结构和生物活性间的关系,并用计算机辅助药物设计(computer-aided drug design,CADD)设计出7个喹啉酮...使用比较分子力场分析法(CoMFA)和比较分子相似性指数法(CoMSIA)对33个已报道的喹啉酮类BRD4抑制剂进行3D-QSAR模型建立,研究了其化学结构和生物活性间的关系,并用计算机辅助药物设计(computer-aided drug design,CADD)设计出7个喹啉酮类抑制剂。结果表明,建立的CoMFA(q^(2)=0.926,r^(2)=0.997,r^(2)_(pred)=0.744)和CoMSIA(q^(2)=0.939,r^(2)=0.991,r^(2)_(pred)=0.786)模型具有较好的预测能力,基于这些模型设计的7个新喹啉酮类BRD4抑制剂具有高活性,并对其进行ADMET性质评价和类药性分析。以上研究结果有助于改造和开发更加有效的喹啉酮类BRD4抑制剂。展开更多
3D-QSAR studies of persistent organic pollutants(POPs)screening for atmosphere persistence were performed by comparative molecular field analysis(CoMFA)and comparative molecular similarity index analysis(CoMSIA)method...3D-QSAR studies of persistent organic pollutants(POPs)screening for atmosphere persistence were performed by comparative molecular field analysis(CoMFA)and comparative molecular similarity index analysis(CoMSIA)methods.The mean and maximum half-life estimations for degradation in air of 49 UNEP POPs and possible POPs were modeled.Both groups’data have been modeled to obtain an average estimate and a predictive value for ranking and screening purposes.CoMFA and CoMSIA models have given cross-validation regre...展开更多
In order to understand the chemical-biological interactions governing their activities toward neuraminidase （NA）, QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were d...In order to understand the chemical-biological interactions governing their activities toward neuraminidase （NA）, QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. The obtained HQSAR （hologram quantitative structure activity relationship）, Topomer CoMFA and CoMSIA （comparative molecular similarity indices analysis） models were robust and had good exterior predictive capabilities. Moreover, QSAR modeling results elucidated that hydrogen bonds highly contributed to the inhibitory activity, then electrostatic and hydrophobic factors. Squared multiple correlation coefficients （R2） of HQSAR, Topomer CoMFA and CoMSIA models were 0.994, 0.978 and 0.996, respectively. Squared cross-validated correlation coefficients （Q2） of HQSAR, Topomer CoMFA and CoMSIA models were in turn 0.951, 919 and 0.820. Furthermore, squared multiple correlation coefficients for the test set （R2test） of HQSAR, CoMFA and CoMSIA models were 0.879, 0.912 and 0.953, respectively. Squared cross-validated correlation coefficients for the test set （Q2ext） of HQSAR, Topomer CoMFA and CoMSIA models were 0.867, 0.884 and 0.899, correspondingly.展开更多
Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level...Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level of whole research to a large extent and guide the discovery of new drugs. In this paper, Melittin and amoebapore three-dimensional quantitative structureactivity relationship(3D-QSAR) models were established by using comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) method. The result shows that, the correlation coefficient(q^2) was 0.583 and non-cross-validation correlation coefficient(r^2) was 0.972 for the melittin CoMFA model. The q^2 and r^2 were 0.630 and 0.995 for the best CoMSIA model, 0.645 and 0.993 for the amoebapore CoMFA model, and 0.738 and 0.996 for the best CoMSIA model. The statistical parameters demonstrated that the CoMFA and CoMSIA models had both good predictive ability and high statistical stability, and can provide theoretical basis for designing new high activity polypeptide drugs.展开更多
Aromatic hydrocarbons,one of the persistent organic pollutants（POPs）,has been usually found in mussels,accumulated for their hard mobility and activities in harbours and estuaries.In this study,based on the 96 hr-LC...Aromatic hydrocarbons,one of the persistent organic pollutants（POPs）,has been usually found in mussels,accumulated for their hard mobility and activities in harbours and estuaries.In this study,based on the 96 hr-LC50 of 12 aromatic hydrocarbons with larval sinonvaculina constricta,three-dimensional quantitative structure-activity relationship（3D-QSAR） technique：comparative molecular similarity indices analysis（CoMSIA） and 2D-QSAR technique：multiple linear regression（MLR） were described to obtain more detailed insight into the structure-activity relationships between the molecular structure and bio-activity.The results show the MLR model based on density functional theory（DFT） calculation carried out at the B3LYP/6-311＊＊ level with Gaussian 03 program yielded a very good correlation with a coefficient squared R2 of 0.716 and a cross-validated Q2 of 0.874.The dipole moment and enthalpy,as the thermodynamic parameters,were two important factors influencing pLC50.Correspondingly,CoMSIA based on the partial least-squares（PLS） methodology with steric,electrostatic,hydrophobic,H-bond donor and acceptor fields contributing simultaneously were employed and the values of R2 and the cross validation with leave-One-Out（LOO） Q2LOO were 0.585 and 0.990,respectively,which reveals the structure features,such as the electronegative substituent（nitro-group）,hydrophobic groups（the benzene ring） and H-bond（nitro-group）,related to the toxicity.The results of 2D-QSAR employing MLR model and 3D-QSAR employing CoMSIA model provide the useful information for predicting the toxicity of other aromatic hydrocarbons by comparing the molecular structures of similar compounds.展开更多
AIM: Inhibitors of catechol-O-methyltransferase (COMT) have always been administered to improve the bioavailability of L-Dopa in the treatment of Parkinson disease (PD). A new three-dimensional quantitative structure-...AIM: Inhibitors of catechol-O-methyltransferase (COMT) have always been administered to improve the bioavailability of L-Dopa in the treatment of Parkinson disease (PD). A new three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis is performed to correlate the molecular fields with percent inhibition values. METHODS: Three predictive models were derived based on 36 previously reported COMT inhibitors employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodologies. RESULTS: The CoMFA model and CoMSIA model with steric and electrostatic field yielded cross-validated rcv2 0.585 and 0.528 respectively, whereas the conventional rncv2 were 0.979 and 0.891. The CoMSIA model with hydrophobic field exhibited rcv2 0.544 and rncv2 0.930. CONCLUSION: The derived models from CoMFA and CoMSIA all exhibit good prediction for both internal and external validations. The individual inspection of 3D contours generated from these models helps in understanding the possible region for structural modification of molecules to improve the inhibitory bioactivity. The 3D-QSAR models may be useful in designing and predicting novel COMT inhibitors.展开更多
文摘采用比较分子场分析(comparative molecular field analysis,CoMFA)和比较分子相似性指数分析(comparative similarity indices analysis,CoMSIA)方法,分别对一系列新烟碱类杀虫剂及相关化合物烟碱乙酰胆碱受体激动剂进行了结构与活性关系的研究,构建了CoMFA及CoMSIA模型;该模型具有较好的预报能力和拟合能力,CoMFA:q2=0.563,r2=0.950;CoMSIA:q2=0.657,r2=0.973.通过三维等势图分析得出了对新烟碱类活性影响较大的基团或原子,为新烟碱类化合物的进一步研究提供了依据.
文摘使用比较分子力场分析法(CoMFA)和比较分子相似性指数法(CoMSIA)对33个已报道的喹啉酮类BRD4抑制剂进行3D-QSAR模型建立,研究了其化学结构和生物活性间的关系,并用计算机辅助药物设计(computer-aided drug design,CADD)设计出7个喹啉酮类抑制剂。结果表明,建立的CoMFA(q^(2)=0.926,r^(2)=0.997,r^(2)_(pred)=0.744)和CoMSIA(q^(2)=0.939,r^(2)=0.991,r^(2)_(pred)=0.786)模型具有较好的预测能力,基于这些模型设计的7个新喹啉酮类BRD4抑制剂具有高活性,并对其进行ADMET性质评价和类药性分析。以上研究结果有助于改造和开发更加有效的喹啉酮类BRD4抑制剂。
基金supported by the Scientific ResearchFoundation for the Returned Overseas Chinese Scholars,State Education Ministry,the National Natural ScienceFoundation of China(No.30470918).
文摘3D-QSAR studies of persistent organic pollutants(POPs)screening for atmosphere persistence were performed by comparative molecular field analysis(CoMFA)and comparative molecular similarity index analysis(CoMSIA)methods.The mean and maximum half-life estimations for degradation in air of 49 UNEP POPs and possible POPs were modeled.Both groups’data have been modeled to obtain an average estimate and a predictive value for ranking and screening purposes.CoMFA and CoMSIA models have given cross-validation regre...
基金supported by the National Natural Science Foundation of China (No. 21202110)
文摘In order to understand the chemical-biological interactions governing their activities toward neuraminidase （NA）, QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. The obtained HQSAR （hologram quantitative structure activity relationship）, Topomer CoMFA and CoMSIA （comparative molecular similarity indices analysis） models were robust and had good exterior predictive capabilities. Moreover, QSAR modeling results elucidated that hydrogen bonds highly contributed to the inhibitory activity, then electrostatic and hydrophobic factors. Squared multiple correlation coefficients （R2） of HQSAR, Topomer CoMFA and CoMSIA models were 0.994, 0.978 and 0.996, respectively. Squared cross-validated correlation coefficients （Q2） of HQSAR, Topomer CoMFA and CoMSIA models were in turn 0.951, 919 and 0.820. Furthermore, squared multiple correlation coefficients for the test set （R2test） of HQSAR, CoMFA and CoMSIA models were 0.879, 0.912 and 0.953, respectively. Squared cross-validated correlation coefficients for the test set （Q2ext） of HQSAR, Topomer CoMFA and CoMSIA models were 0.867, 0.884 and 0.899, correspondingly.
基金Supported by the National Natural Science Foundation of China(21475081)Natural Science Foundation of Shaanxi Province of China(2015JM2057)Graduate Innovation Fund of Shaanxi University of Science and Technology
文摘Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level of whole research to a large extent and guide the discovery of new drugs. In this paper, Melittin and amoebapore three-dimensional quantitative structureactivity relationship(3D-QSAR) models were established by using comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) method. The result shows that, the correlation coefficient(q^2) was 0.583 and non-cross-validation correlation coefficient(r^2) was 0.972 for the melittin CoMFA model. The q^2 and r^2 were 0.630 and 0.995 for the best CoMSIA model, 0.645 and 0.993 for the amoebapore CoMFA model, and 0.738 and 0.996 for the best CoMSIA model. The statistical parameters demonstrated that the CoMFA and CoMSIA models had both good predictive ability and high statistical stability, and can provide theoretical basis for designing new high activity polypeptide drugs.
基金Supported by the Special Research Fund for the National Non-profit Institutes (East China Sea Fisheries Research Institute) (2008M11, 2007M08)National Natural Science Foundation of China (41001188)National Basic Research Program (973) of China (2010CB429005)
文摘Aromatic hydrocarbons,one of the persistent organic pollutants（POPs）,has been usually found in mussels,accumulated for their hard mobility and activities in harbours and estuaries.In this study,based on the 96 hr-LC50 of 12 aromatic hydrocarbons with larval sinonvaculina constricta,three-dimensional quantitative structure-activity relationship（3D-QSAR） technique：comparative molecular similarity indices analysis（CoMSIA） and 2D-QSAR technique：multiple linear regression（MLR） were described to obtain more detailed insight into the structure-activity relationships between the molecular structure and bio-activity.The results show the MLR model based on density functional theory（DFT） calculation carried out at the B3LYP/6-311＊＊ level with Gaussian 03 program yielded a very good correlation with a coefficient squared R2 of 0.716 and a cross-validated Q2 of 0.874.The dipole moment and enthalpy,as the thermodynamic parameters,were two important factors influencing pLC50.Correspondingly,CoMSIA based on the partial least-squares（PLS） methodology with steric,electrostatic,hydrophobic,H-bond donor and acceptor fields contributing simultaneously were employed and the values of R2 and the cross validation with leave-One-Out（LOO） Q2LOO were 0.585 and 0.990,respectively,which reveals the structure features,such as the electronegative substituent（nitro-group）,hydrophobic groups（the benzene ring） and H-bond（nitro-group）,related to the toxicity.The results of 2D-QSAR employing MLR model and 3D-QSAR employing CoMSIA model provide the useful information for predicting the toxicity of other aromatic hydrocarbons by comparing the molecular structures of similar compounds.
文摘AIM: Inhibitors of catechol-O-methyltransferase (COMT) have always been administered to improve the bioavailability of L-Dopa in the treatment of Parkinson disease (PD). A new three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis is performed to correlate the molecular fields with percent inhibition values. METHODS: Three predictive models were derived based on 36 previously reported COMT inhibitors employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodologies. RESULTS: The CoMFA model and CoMSIA model with steric and electrostatic field yielded cross-validated rcv2 0.585 and 0.528 respectively, whereas the conventional rncv2 were 0.979 and 0.891. The CoMSIA model with hydrophobic field exhibited rcv2 0.544 and rncv2 0.930. CONCLUSION: The derived models from CoMFA and CoMSIA all exhibit good prediction for both internal and external validations. The individual inspection of 3D contours generated from these models helps in understanding the possible region for structural modification of molecules to improve the inhibitory bioactivity. The 3D-QSAR models may be useful in designing and predicting novel COMT inhibitors.
