http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : uvela, Petar (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Ž,uvela, Petar,Liu, J. Jay,Yi, Myunggi,Pomastowski, Paweł P.,Sagandykova, Gulyaim,Belka, Mariusz,David, Jonathan,Bą,czek, Tomasz,Szafrań,ski, Krzysztof,Ż,ołnowska, Beata,Sławi TaylorFrancis 2018 Journal of enzyme inhibition and medicinal chemist Vol.33 No.1
<P><B>Abstract</B></P><P>In this work, a target-based drug screening method is proposed exploiting the synergy effect of ligand-based and structure-based computer-assisted drug design. The new method provides great flexibility in drug design and drug candidates with considerably lower risk in an efficient manner. As a model system, 45 sulphonamides (33 training, 12 testing ligands) in complex with carbonic anhydrase IX were used for development of quantitative structure-activity-lipophilicity (property)-relationships (QSPRs). For each ligand, nearly 5,000 molecular descriptors were calculated, while lipophilicity (log<I>k</I><SUB>w</SUB>) and inhibitory activity (log<I>K</I><SUB>i</SUB>) were used as drug properties. Genetic algorithm-partial least squares (GA-PLS) provided a QSPR model with high prediction capability employing only seven molecular descriptors. As a proof-of-concept, optimal drug structure was obtained by inverting the model with respect to reference drug properties. 3509 ligands were ranked accordingly. Top 10 ligands were further validated through molecular docking. Large-scale MD simulations were performed to test the stability of structures of selected ligands obtained through docking complemented with biophysical experiments.</P>
-
Ž,uvela, Petar,Liu, J. Jay,Macur, Katarzyna,Bą,czek, Tomasz American Chemical Society 2015 ANALYTICAL CHEMISTRY - Vol.87 No.19
<P>In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (EPA), was compared in molecular descriptor selection for development of quantitative structure retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.</P>
-
Brigljević,, Boris,Ž,uvela, Petar,Liu, J. Jay,Woo, Hee-Chul,Choi, Jae Hyung Elsevier 2018 APPLIED ENERGY Vol.215 No.-
<P><B>Abstract</B></P> <P>The prominence of biofuel research is growing as the global energy policies focus on renewable energy technologies. Accurate process design and simulation is required when evaluating technological and market capabilities of large scale, novel, fuel production processes. Thermochemical decomposition, employed in various biofuel production routes (pyrolysis, liquefaction, and so on) yields complex liquid mixtures (bio-crudes) containing numerous compounds. The process simulation of such processes must accurately represent the physical, thermodynamic and chemical properties of bio-crudes, while reducing complexity to a point where it can be handled by a process simulator in a time effective manner. In this work, a software employing automated modelling of bio-crudes based on raw experimental data, has been developed. The program output is a ready-to-use reduced mixture, including all product phases and in mass balance with the Proximate and Ultimate analyses of the feedstock biomass material. As there are many approaches to bio-crude modelling, the novelty of this method lies in the combination of the minimization of the number of components needed and the minimization of the level of artificiality introduced in the system. The automation of the method allowed for fast reduction and optimization of seven experimental data sets which were then validated by process simulation.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A method utilizing experimental data and producing a reduced mixture was developed. </LI> <LI> Software solution was developed for method automation. </LI> <LI> Results were tested in Aspen and validated against seven experimental datasets. </LI> <LI> Swift and accurate reduced representation of complex mixtures was produced. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
