This paper documents the design, layout and algorithms of the IUPAC International Chemical Identifier, InChI.

Since its public introduction in 2005 the IUPAC InChI chemical structure identifier standard has become the international, worldwide standard for defined chemical structures. This article will describe the extensive use and dissemination of the InChI and InChIKey structure representations by and for the world-wide chemistry community, the chemical information community, and major publishers and disseminators of chemical and related scientific offerings in manuscripts and databases.

Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.

Amino acids Trp, Gly, Ala, Leu are extracted efficiently from aqueous solution at pH 1.5-4.0 (Lys and Arg at pH 1.5-5.5) into the room temperature ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate (BmimPF(6)) with dicyclohexano-18-crown-6 (CE). The most hydrophilic amino acids such as Gly are extracted as efficiently as the less hydrophilic (92-96%). The influence of pH, amino acid and crown ether concentration, volume ratio of aqueous and organic phases, and presence of some cations on amino acid recovery were studied. The ratio of amino acid to crown ether in the extracted species is 1:1 for cationic Trp, Leu, Ala, and Gly and to 1:2 for dicationic Arg and Lys. This ionic liquid extraction system was used successfully for the recovery of amino acids from pharmaceutical samples and fermentation broth, and was followed by fluorimetric determination.

No abstract

The phenolic compounds phenol, 4-nitrophenol, 2,4-dinitrophenol, 2,6-dinitrophenol, 1-naphthol, 2-naphthol, and 4-chlorophenol are extracted nearly quantitatively from aqueous solution into the room temperature ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate (BMImPF6) in molecular form at pH < p Ka. Picric acid is extracted efficiently in anionic form. Recovery of pyrocatechol and resorcinol is much lower. The effect of pH, phenol concentration, and volume ratio of aqueous and organic phases were studied. Ionic liquid BMImPF6 is shown to be suitable for extraction-voltammetric determination of phenols without back-extraction or addition of support electrolyte. The electrochemical window of BMImPF6 at various electrodes was determined, and voltammetric oxidation of phenols and reduction of nitrophenols in BMImPF6 was studied.

Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with wellknown neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.

No abstract