Patenting computer-designed peptides

The problem of designing new peptides that possess specific properties, such as bactericidal activity, is of wide interest. Recently, attention has focused on the use of Computer-Aided Molecular Design techniques in parallel with more traditional 'synthesise and test' methods. These techniques may typically use Genetic Algorithms to optimise molecules based on Neural Network models that predict activity. In this paper we describe a successful application of this Molecular Design methodology that has resulted in novel bactericidal peptides of real value. A key issue for commercial utilisation of such results is the ability to protect the intellectual property rights associated with the discovery of new molecules. Typically peptide patents use structural templates of amino acid hydrophobicity-hydrophilicity that define highly regular peptide patent spaces. In an extension of established patenting practice we describe a patent application that uses a Neural Net predictive model to define the regions of peptide space that we claim within the patent. This formalism makes no a priori assumptions about the regularity of the patent space. A preliminary comparative investigation of the shape and size of this and other bactericidal peptide patent spaces is conducted.     

Kinetic Resolution of Oxazinones: Rational Exploration of Chemical Space through the Design of Experiments

The organocatalytic kinetic resolution of 4‐substituted oxazinones has been optimised (selectivity factor S up to 98, chiral oxazinone ee values up to 99.6 % ( 1 a – g ) and product ee values up to 90 % ( 3 a – g )) in a rational way by applying the Design of Experiments (DoE) approach.     

Optimization of a solid-phase microextraction procedure for the determination of herbicides by micellar electrokinetic chromatography

The use of a different optimization procedure that involves Experimental Design (ED) and Artificial Neural Networks (ANN) for the off-line coupling solid-phase microextraction-micellar electokinetic chomatography (SPME-MEKC) is presented. This combination of ED and ANN, mathematical tools not previously used in SPME-MEKC optimization, allowed us to obtain good extraction efficiencies in the SPME procedure for the determination of a group of eleven triazine herbicides in groundwater samples. Both extraction and desorption steps were carried out by solution stirring at 900 rpm. Optimal conditions for the off-line SPME procedure were: extraction with a poly(dimethylsiloxane)/divinylbenzene SPME fiber for 120 min, 10% (w/v) NaCl, desorption time 40 min, and 70% (v/v) of methanol/buffer as desorption mixture. Detection limits lay between 0.80 microg L(-1) and 4.89 microg L(-1). Finally, the optimized method was applied to the determination of these compounds in spiked and non-spiked groundwater samples using a previously optimized MEKC separation.     

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Summary Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substitured benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.     

Extraction of flavonoids from Rhodiola sachlinesis A. Bor by UPE and the antioxidant activity of its extract

Rhodiola sachlinesis, A. Bor is one of the most popular traditional Chinese medicines and has been proved to have many effective compounds, such as flavonoids, anthraglycosides, essential oil with cinnamic aldehyde and citral and organic acids. In this article, a new method of ultrahigh pressure extraction (UPE) was used to extract flavonoids from R. sachlinesis. Uniform Design was employed to get the optimum extraction conditions. The optimum UPE conditions were as follows: pressure was 500 MPa; ethanol concentration was 40%; solid/liquid ratio (g mL(-1)) was 1 : 70. The optimum extraction yield of flavonoids was 52.33 mg g(-1). The antioxidant activity of the crude extract was tested with DPPH assay. It showed a higher activity, compared with tertiary butyhydroquinone (TBHQ) at the same concentration (1 mg mL(-1)).     

“Design of Experiments” as a Method to Optimize Dynamic Disulfide Assemblies: Cages and Functionalizable Macrocycles

Cyclophanes are a venerable class of macrocyclic and cage compounds that often contain unusual conformations, high strain, and unusual properties. However, synthesis of complex, functional derivatives remains difficult due to low functional group tolerance, high dilution, extreme reaction conditions, and sometimes low yields using traditional stepwise synthetic methods. “Design of experiments” (DOE) is a method employed for the optimization of reaction conditions, and we showcase this approach to generate a dramatic increase in the yield of specific targets from two different self‐assembling systems. These examples demonstrate that DOE provides an additional tool in tuning self‐assembling, dynamic covalent systems.     

微波消解技术在难溶贵金属(铂)化合物分析中的应用

本文提出了微波密闭消解处理难溶贵金属化合物Pt（NH3）4Cl2、Pt（NH3）2Cl2、Pt（NH3）2（NO2）2和K2PtCl4的方法；对比了它们的微波密闭消解和传统分解条件；分析了消解后Pt的含量。结果表明：上述各类化合物对应消解时间分别为传统分解法的1／15、1／13和1／11；分析手段随之简化且总分析流程大大缩短；两种方法测得的Pt含量吻合。     

Direct and two-stage data analysis procedures based on PCA, PLS-DA and ANN for ISE-based electronic tongue-Effect of supervised feature extraction

A novel strategy of data analysis for artificial taste and odour systems is presented in this work. It is demonstrated that using a supervised method also in feature extraction phase enhances fruit juice classification capability of sensor array developed at Warsaw University of Technology. Comparison of direct processing (raw data processed by Artificial Neural Network (ANN), raw data processed by Partial Least Squares-Discriminant Analysis (PLS-DA)) and two-stage processing (Principal Components Analysis (PCA) outputs processed by ANN, PLS-DA outputs processed by ANN) is presented. It is shown that considerable increase of classification capability occurred in the case of the new method proposed by the authors.     

Preparation of New Multiarmed RAFT Agents for the Mediation of Vinyl Acetate Polymerization

Summary: In this study five xanthate (Reversible Addition-Fragmentation chain Transfer (RAFT)/Macromolecular Design through Interchange of Xanthates (MADIX)) agents were synthesized, namely monofunctional, difunctional, trifunctional and tetrafunctional species of the form SC(O-Z)-S-R, with different leaving groups and different activating moieties some of which are completely novel. Polyvinyl acetates (PVAc) in the form of linear, three armed and four armed star shaped polymers were then successfully synthesized in reactions mediated by these xanthate RAFT/MADIX agents.     

Application of Experimental Design Methodologies in the Enantioseparation of Pharmaceuticals by Capillary Electrophoresis: A Review

Chirality is one of the major issues in pharmaceutical research and industry. Capillary electrophoresis (CE) is an interesting alternative to the more frequently used chromatographic techniques in the enantioseparation of pharmaceuticals, and is used for the determination of enantiomeric ratio, enantiomeric purity, and in pharmacokinetic studies. Traditionally, optimization of CE methods is performed using a univariate one factor at a time (OFAT) approach; however, this strategy does not allow for the evaluation of interactions between experimental factors, which may result in ineffective method development and optimization. In the last two decades, Design of Experiments (DoE) has been frequently employed to better understand the multidimensional effects and interactions of the input factors on the output responses of analytical CE methods. DoE can be divided into two types: screening and optimization designs. Furthermore, using Quality by Design (QbD) methodology to develop CE-based enantioselective techniques is becoming increasingly popular. The review presents the current use of DoE methodologies in CE-based enantioresolution method development and provides an overview of DoE applications in the optimization and validation of CE enantioselective procedures in the last 25 years. Moreover, a critical perspective on how different DoE strategies can aid in the optimization of enantioseparation procedures is presented.     

Chaotropic chromatography method development for the determination of aripiprazole and its impurities following analytical quality by design principles

In this paper, development of robust and reliable chaotropic chromatography method for the determination of aripiprazole and its impurities, following Analytical Quality by Design principles is presented. The efficient baseline separation and accurate determination of aripiprazole and its four impurities from tablets were set as Analytical Target Profile. In line with it, the influence of Critical Method Parameters (acetonitrile content, concentration of perchloric acid in water phase, and column temperature) on predefined Critical Method Attributes (separation of the critical pair of peaks, retention of the first and last eluting peak) was investigated with aid of the Central Composite Design. Further on Design Space, where Critical Method Parameters meet predefined acceptance limits with a high level of probability (π ≥ 85%), was computed as a result of performed Monte Carlo simulations. A normal operating conditions corresponding to 34% of acetonitrile, 66% of 42.5 mM perchloric acid, and column temperature at 35°C were selected from created Design Space. Robustness testing of the quantitative performances of the developed method was conducted combining Plackett–Burman design with alias matrix approach. Through the additional validation testing, reliability of the developed method for the use in the routine practice was completely confirmed.     

Fluoride-selective optical sensor based on the dipyrrolyl-tetrathiafulvalene chromophore

A chemosensor bearing dipyrrolyl motifs as recognition sites and a tetrathiafulvalene redox tag has been evaluated as an optical and redox sensor for a series of anions (F(-), Cl(-), Br(-), HSO(4)(-), CH(3)COO(-), and H(2)PO(4)(-)) in DCM solution. The receptor shows specific optical signaling for fluoride but little electrochemical effect in solution. The solid-state performance of the sensor leads to measurable changes in water. Design implications towards better systems based on these results and other examples are discussed.     

A Simple and Efficient One‐pot Synthesis of 1,4‐dihydropyridines Using Nano‐WO3‐ supported Sulfonic Acid as an Heterogeneous Catalyst under Solvent‐free Conditions

Nano‐tungsten trioxide‐supported sulfonic acid (n‐WSA) was found to be an effective heterogeneous cat‐ alyst for the one‐pot reaction of aromatic aldehydes, β‐dicarbonyl compounds and ammonium acetate to afford 1,4‐dihydropyridine derivatives in good to excellent yields. The other main advantages of the pres‐ ent method are short reaction times, simple workup, ease in purification and environmentally benign methodology. The reaction conditions were optimized employing Response Surface Method technique (Central Composite Design (CCD)) which is economically considerable because of the minimum number of experiments required to evaluate the effects of multiple parameters on the response.     

Novel multidentate sulfur-nitrogen ligands with enhanced complexation properties

Di(tert-butyl)sulfur diimide and bis(trimethylsilyl)sulfur diimide were reacted with different metalated amines to form versatile novel multidentate ligand systems with side-arm donation. Their complexation properties in terms of ligand design, denticity and the cation size are discussed. We report herein the synthesis and structure elucidation of [(tBuN)(2)S{LiMe(2)N(C(6)H(4))S(NtBu)(2)}(2)] (1), [(Li{Me(2)N(C(6)H(4))S(NSiMe(3))(2)})(2)] (2), [(Li(thf){Me(2)N(C(6)H(4))S(NSiMe(3))(2)})(2)] (3), [(Li{2-PicS(NSiMe(3))(2)})(2)] (4), [(Li{Me(2)N(CH(2))(2)N(Me)S(NSiMe(3))(2)})(2)] (5), [(Na{Me(2)N(CH(2))(2)N(Me)S(NSiMe(3))(2)})(2)] (6) and [(K{Me(2)N(C(6)H(4))S(NSiMe(3))(2)})(2)] (7).     

A nonlinear partial least squares algorithm using quadratic fuzzy inference system

We introduce a new nonlinear partial least squares algorithm ‘Quadratic Fuzzy PLS (QFPLS)’ that combines the outer linear Partial Least Squares (PLS) framework and the Takagi–Sugeno–Kang (TSK) fuzzy inference system. The inner relation between the input and the output PLS score vectors is modeled by a quadratic TSK fuzzy inference system. The performance of the proposed QFPLS method is tested and compared against four other well‐known partial least squares methods (Linear PLS (LPLS), Quadratic PLS (QPLS), Linear Fuzzy PLS (LFPLS), and Neural Network PLS (NNPLS)) on various different types of randomly generated test data. QFPLS outperformed competitors based on two comparison measures: the output variables cumulative per cent variance captured by the PLS latent variables and the root mean‐square error of prediction (RMSEP). Copyright © 2009 John Wiley & Sons, Ltd.     

Iron oxide and alumina nanocomposites applied to Fischer-Tropsch synthesis

Nanocomposites composed of nanoparticles of iron oxide and aluminium oxide (Fe(2)O(3)-Al(2)O(3)) with a unique structure show a remarkable catalytic performance in Fischer-Tropsch synthesis (FTS), compared with the traditional iron-based catalyst.     

On the optimal partitioning of data with K-means,growing K-means,neural gas,and growing neural gas

In this paper, the performance of new clustering methods such as Neural Gas (NG) and Growing Neural Gas (GNG) is compared with the K-means method for real and simulated data sets. Moreover, a new algorithm called growing K-means, GK, is introduced as the alternative to Neural Gas and Growing Neural Gas. It has small input requirements and is conceptually very simple. The GK leads to nearly optimal values of the cost function, and, contrary to K-means, it is independent of the initial data set partition. The incremental property of GK additionally helps to estimate the number of "natural" clusters in data, i.e., the well-separated groups of objects in the data space.