Bibliographic record
Abstract
Free Access Bibliography Prof., Dr. Roberto Todeschini, Prof., Dr. Roberto Todeschini Dept. of Environm. Sciences, University Milano-Bicocca, Piazza della Scienza 1, 0126 Milano, ItalySearch for more papers by this authorDr. Viviana Consonni, Dr. Viviana Consonni Dept. of Environm. Sciences, University Milano-Bicocca, Piazza della Scienza 1, 0126 Milano, ItalySearch for more papers by this author Book Author(s):Prof., Dr. Roberto Todeschini, Prof., Dr. Roberto Todeschini Dept. of Environm. Sciences, University Milano-Bicocca, Piazza della Scienza 1, 0126 Milano, ItalySearch for more papers by this authorDr. Viviana Consonni, Dr. Viviana Consonni Dept. of Environm. Sciences, University Milano-Bicocca, Piazza della Scienza 1, 0126 Milano, ItalySearch for more papers by this author First published: 15 July 2009 https://doi.org/10.1002/9783527628766.biblioCitations: 1Book Series:Methods and Principles in Medicinal Chemistry Series Editor(s): Prof. Dr. Raimund Mannhold, Prof. Dr. Raimund Mannhold Molecular Drug Research Group, Heinrich-Heine-Universität, Universitätsstrasse 1, 40225 Düsseldorf, GermanySearch for more papers by this authorProf., Dr. Hugo Kubinyi, Prof., Dr. Hugo Kubinyi Donnersbergstrasse 9, 67256 Weisenheim am Sand, GermanySearch for more papers by this authorProf. Dr. Gerd Folkers, Prof. Dr. Gerd Folkers Collegium Helveticum, STW/ETH Zurich, 8092 Zurich, SwitzerlandSearch for more papers by this author AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Bibliography A-Razzak, M. and Glen, R.C. (1992) Application of rule-induction in the derivation of quantitative structure–activity relationships. J. Comput. Aid. Mol. Des., 6, 349– 383. CrossrefCASPubMedWeb of Science®Google Scholar Abe, I., Tatsumoto, H. and Hirashima, T. (1986) Prediction of activated carbon adsorption by adsorbability index (AI). Suishitsu Odaku Kenkyu, 9, 153– 161. Google Scholar Åberg, K.M. and Jacobsson, S.P. (2001) Pre-processing of three-way data by pulse-coupled neural networks – an imaging approach. Chemom. Intell. Lab. Syst., 57, 25– 36. CrossrefWeb of Science®Google Scholar Aboushaaban, R.R., Alkhamees, H.A., Abouauda, H.S. and Simonelli, A.P. (1996) Atom level electrotopological state indexes in QSAR designing and testing antithyroid agents. Pharm. Res., 13, 129– 136. CrossrefCASPubMedWeb of Science®Google Scholar Abraham, D.J. and Kellogg, G.E. (1993) Hydrophobic fields, in 3D QSAR in Drug Design. Theory, Methods and Applications (ed. H. Kubinyi), ESCOM, Leiden, The Netherlands, pp. 506– 520. Google Scholar Abraham, D.J. and Leo, A. (1987) Amino acid scale: hydrophobicity (delta G1/2 cal). Prot. Struct. Funct. Gen., 2, 130– 152. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Abraham, M.H. (1993a) Application of solvation equations to chemical and biochemical processes. Pure & Appl. Chem., 65, 2503– 2512. CrossrefCASWeb of Science®Google Scholar Abraham, M.H. (1993b) Hydrogen bonding. Part 31. Construction of a scale of solute effective or summation hydrogen bond basicity. J. Phys. Org. Chem., 6, 660– 684. Wiley Online LibraryCASWeb of Science®Google Scholar Abraham, M.H. (1993c) Physicochemical and biological processes. Chem. Soc. Rev., 22, 73– 83. CrossrefCASWeb of Science®Google Scholar Abraham, M.H. (1993d) Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical processes. Chem. Soc. Rev., 22, 73– 83. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Andonian-Haftvan, J., Cometto Muniz, J.E. and Cain, W.S. (1996) An analysis of nasal irritation thresholds using a new solvation equation. Fund. Appl. Toxicol., 31, 71– 76. CrossrefCASPubMedWeb of Science®Google Scholar Abraham, M.H., Andonian-Haftvan, J., Whiting, G.S., Leo, A. and Taft, R.S. (1994) Hydrogen bonding. Part 34. The factors that influence the solubility of gases and vapours in water at 298 K, and a new method for its determination. J. Chem. Soc. Perkin Trans. 2, 1777– 1791. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Chadha, H.S., Dixon, J.P. and Leo, A.J. (1994a) Hydrogen bonding. 39. The partition of solutes between water and various alcohols. J. Phys. Org. Chem., 7, 712– 716. Wiley Online LibraryCASWeb of Science®Google Scholar Abraham, M.H., Chadha, H.S., Dixon, J.P., Rafols, C. and Treiner, C. (1995a) Hydrogen bonding. Part 40. Factors that influence the distribution of solutes between water and sodium dodecylsulfate micelles. J. Chem. Soc. Perkin Trans. 2, 887– 894. CrossrefWeb of Science®Google Scholar Abraham, M.H., Chadha, H.S. and Mitchell, R.C. (1994b) Hydrogen bonding. 33. Factors that influence the distribution of solutes between blood and brain. J. Pharm. Sci., 83, 1257– 1268. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Abraham, M.H., Chadha, H.S. and Mitchell, R.C. (1995b) Hydrogen bonding. Part 36. Determination of blood brain distribution using octanol–water partition coefficients. Drug Design & Discovery, 13, 123– 131. CASPubMedGoogle Scholar Abraham, M.H., Duce, P.P., Prior, D.V., Barratt, D.G., Morris, J.J. and Taylor, P.J. (1989) Hydrogen bonding. Part 9. Solute proton donor and proton acceptor scales for use in drug design. J. Chem. Soc. Perkin Trans. 2, 1355– 1375. CrossrefWeb of Science®Google Scholar Abraham, M.H., Green, C.E. and Acree, W.E., Jr (2000) Correlation and prediction of the solubility of buckminsterfullerene in organic solvents; estimation of some physicochemical properties. J. Chem. Soc. Perkin Trans. 2, 281– 286. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Green, C.E., Acree, W.E., Jr, Hernandez, C.E. and Roy, L.E. (1998) Descriptors for solutes from the solubility of solids: trans-stilbene as an example. J. Chem. Soc. Perkin Trans. 2, 2677– 2681. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Grellier, P.L., Hamerton, I., McGill, R.A., Prior, D.V. and Whiting, G.S. (1988) Solvation of gaseous non-electrolytes. Faraday Discuss. Chem. Soc., 85, 107– 115. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Grellier, P.L. and McGill, R.A. (1987) Determination of olive oil–gas and hexadecane–gas partition coefficients, and calculation of the corresponding olive oil–water and hexadecane–water partition coefficients. J. Chem. Soc. Perkin Trans. 2, 797– 803. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Grellier, P.L., Prior, D.V., Duce, P.P., Morris, J.J. and Taylor, P.J. (1989) Hydrogen bonding. Part 7. A scale of solute hydrogen-bond acidity based on log K values for complexation in tetrachloromethane. J. Chem. Soc. Perkin Trans. 2, 699– 711. CrossrefWeb of Science®Google Scholar Abraham, M.H., Grellier, P.L., Prior, D.V., Morris, J.J. and Taylor, P.J. (1990) Hydrogen bonding. Part 10. A scale of solute hydrogen-bond basicity using log K values for complexation in tetrachloromethane. J. Chem. Soc. Perkin Trans. 2, 521– 529. CrossrefWeb of Science®Google Scholar Abraham, M.H., Ibrahim, A. and Acree, W.E., Jr (2005) Air to blood distribution of volatile organic compounds: a linear free energy analysis. Chem. Res. Toxicol., 18, 904– 911. CrossrefCASPubMedWeb of Science®Google Scholar Abraham, M.H., Ibrahim, A. and Zissimos, A.M. (2004) Determination of sets of solute descriptors from chromatographic measurements. J. Chromat., 1037, 29– 47. CrossrefCASPubMedWeb of Science®Google Scholar Abraham, M.H., Ibrahim, A., Zissimos, A.M., Zhao, Y.H., Comer, J. and Reynolds, D.P. (2002) Application of hydrogen bonding calculations in property based drug design. Drug Discov. Today, 7, 1056– 1063. CrossrefCASPubMedWeb of Science®Google Scholar Abraham, M.H. and Le, J. (1999) The correlation and prediction of the solubility of compounds in water using an amended solvation energy relationship. J. Pharm. Sci., 88, 868– 880. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Abraham, M.H., Lieb, W.R. and Franks, N.P. (1991) Role of hydrogen bonding in general anesthesia. J. Pharm. Sci., 80, 719– 724. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Abraham, M.H., Martins, F. and Mitchell, R.C. (1997) Algorithms for skin permeability using hydrogen bond descriptors: the problem of steroids. J. Pharm. Pharmacol., 49, 858– 865. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Abraham, M.H. and McGowan, J.C. (1987) The use of characteristic volumes to measure cavity terms in reversed phase liquid chromatography. Chromatographia, 23, 243– 246. CrossrefCASWeb of Science®Google Scholar Abraham, M.H. and Platts, J.A. (2001) Hydrogen bond structural group constants. J. Org. Chem., 66, 3484– 3491. CrossrefCASPubMedWeb of Science®Google Scholar Abraham, M.H. and Rafols, C. (1995) Factors that influence tadpole narcosis. An LFER analysis. J. Chem. Soc. Perkin Trans. 2, 1843– 1851. CrossrefWeb of Science®Google Scholar Abraham, M.H. and Whiting, G.S. (1992) Hydrogen bonding. XXI. Solvation parameters for alkylaromatic hydrocarbons from gas–liquid chromatographic data. J. Chromat., 594, 229– 241. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Whiting, G.S., Alarie, Y., Morris, J.J., Taylor, P.J., Doherty, R.M., Taft, R.W. and Nielsen, G.D. (1990a) Hydrogen bonding. Part 12. A new QSAR for upper respiratory tract irritation by airborne chemicals in mice. Quant. Struct. -Act. Relat., 9, 6– 10. Wiley Online LibraryCASWeb of Science®Google Scholar Abraham, M.H., Whiting, G.S., Carr, P.W. and Ouyang, H. (1998) Hydrogen bonding. Part 45. The solubility of gases and vapours in methanol at 298 K: an LFER analysis. J. Chem. Soc. Perkin Trans. 2, 1385– 1390. CrossrefWeb of Science®Google Scholar Abraham, M.H., Whiting, G.S., Doherty, R.M. and Shuely, W.J. (1990b) Hydrogen bonding. Part 13. A new method for the characterisation of GLC stationary phases – the Laffort data set. J. Chem. Soc. Perkin Trans. 2, 1451– 1460. CrossrefWeb of Science®Google Scholar Abraham, M.H., Whiting, G.S., Doherty, R.M. and Shuely, W.J. (1991a) Hydrogen bonding. XVI. A new solute solvation parameter, πH 2 , from gas chromatographic data. J. Chromat., 587, 213– 228. CrossrefCASWeb of Science®Google Scholar Abraham, M.H., Whiting, G.S., Doherty, R.M. and Shuely, W.J. (1991b) Hydrogen bonding. XVII. The characterisation of 24 gas–liquid chromatographic stationary phases studied by Poole and co-workers, including molten salts, and evaluation of solute–stationary phase interactions. J. Chromat., 587, 229– 236. CrossrefCASWeb of Science®Google Scholar Abraham, R.J. and Smith, P.E. (1988) Charge calculations in molecular mechanics. IV. A general method for conjugated systems. J. Comput. Chem., 9, 288– 297. Wiley Online LibraryCASWeb of Science®Google Scholar Abrahamian, E., Fox, P.C., Nærum, L., Christensen, I.T., Thøgersen, H. and Clark, R.D. (2003) Efficient generation, storage, and manipulation of fully flexible pharmacophore multiplets and their use in 3D similarity searching. J. Chem. Inf. Comput. Sci., 43, 458– 468. CrossrefCASPubMedWeb of Science®Google Scholar Abramowitz, R. and Yalkowsky, S.H. (1990) Estimation of aqueous solubility and melting point of PCB congeners. Chemosphere, 21, 1221– 1229. CrossrefCASWeb of Science®Google Scholar Absalan, G., Hemmateenejad, B., Soleimani, M., Akhond, M. and Miri, R. (2004) Quantitative structure–micellization relationship study of gemini surfactants using genetic-PLS and genetic-MLR. QSAR Comb. Sci., 23, 416– 425. Wiley Online LibraryCASWeb of Science®Google Scholar Acevedo-Martínez, J., Escalona-Arranz, J.C., Villar-Rojas, A., Téllez-Palmero, F., Pérez-Rosés, R., González, L. and Carrasco-Velar, R. (2006) Quantitative study of the structure–retention index relationship in the imine family. J. Chromat., 1102, 238– 244. CrossrefCASPubMedWeb of Science®Google Scholar Adams, N. and Schubert, U.S. (2004) From data to knowledge: chemical data management, data mining, and modeling in polymer science. J. Comb. Chem., 6, 12– 23. CrossrefCASPubMedWeb of Science®Google Scholar Adamson, G.W., Lynch, M.F. and Town, W.G. (1971) Analysis of structural characteristics of chemical compounds in a large computer-based file. Part II. Atom-centred fragments. J. Chem. Soc., (C), 3702– 3706. Web of Science®Google Scholar ADAPT, Jurs, P.C., Pennsylvania State University, PA, http://research.chem.psu.edu/pcjgroup/adapt.html. Google Scholar Afantitis, A., Melagraki, G., Makridima, K., Alexandridis, A., Sarimveis, H. and Iglessi-Markopoulou, O. (2005) Prediction of high weight polymers glass transition temperature using RBF neural networks. J. Mol. Struct. (Theochem), 716, 193– 198. CrossrefCASWeb of Science®Google Scholar Afantitis, A., Melagraki, G., Sarimveis, H., Koutentis, P.A., Markopoulos, J. and Iglessi-Markopoulou, O. (2006) A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis. Mol. Div., 10, 405– 414. CrossrefCASPubMedWeb of Science®Google Scholar Affolter, C., Baumann, K., Clerc, J.T., Schriber, H. and Pretsch, E. (1997) Automatic interpretation of infrared spectra. Mikrochim. Acta, 14, 143– 147. CASGoogle Scholar Afzelius, L., Masimirembwa, C.M., Karlén, A., Andersson, T.B. and Zamora, I. (2002) Discriminant and quantitative PLS analysis of competitive CYP2C9 inhibitors versus non-inhibitors using alignment independent GRIND descriptors. J. Comput. Aid. Mol. Des., 16, 443– 458. CrossrefCASPubMedWeb of Science®Google Scholar Agarwal, A., Pearson, P.P., Taylor, E.W., Li, H.B., Dahlgren, T., Herslof, M., Yang, Y.H., Lambert, G., Nelson, D.L., Regan, J.W. and Martin, A.R. (1993) Three dimensional quantitative structure–activity relationships of 5-HT receptor binding data for tetrahydropyridinylindole derivatives. A comparison of the Hansch and CoMFA methods. J. Med. Chem., 36, 4006– 4014. CrossrefCASPubMedWeb of Science®Google Scholar Agarwal, K.K. (1998) An algorithm for computing the automorphism group of organic structures with stereochemistry and a measure of its efficiency. J. Chem. Inf. Comput. Sci., 38, 402– 404. CrossrefCASWeb of Science®Google Scholar Agatonovic-Kustrin, S., Beresford, R. and Yusof, A.P.M. (2001) Theoretically derived molecular descriptors important in human intestinal absorption. J. Pharm. Biomed. Anal., 25, 227– 237. CrossrefCASPubMedWeb of Science®Google Scholar Agrafiotis, D.K. (1997) On the use of information theory for assessing molecular diversity. J. Chem. Inf. Comput. Sci., 37, 576– 580. CrossrefCASWeb of Science®Google Scholar Agrafiotis, D.K., Bandyopadhyay, D., Wegner, J.K. and van Vlijmen, H. (2007) Recent advances in chemoinformatics. J. Chem. Inf. Model., 47, 1279– 1293. CrossrefCASPubMedWeb of Science®Google Scholar Agrafiotis, D.K., Cedeño, W. and Lobanov, V.S. (2002) On the use of neural network ensembles in QSAR and QSPR. J. Chem. Inf. Comput. Sci., 42, 903– 911. CrossrefCASPubMedWeb of Science®Google Scholar Agrafiotis, D.K. and Lobanov, V.S. (1999) An efficient implementation of distance-based diversity measures based on k–d trees. J. Chem. Inf. Comput. Sci., 39, 51– 58. CrossrefCASWeb of Science®Google Scholar Agrafiotis, D.K. and Rassokhin, D.N. (2002) A fractal approach for selecting an appropriate bin size for cell-based diversity estimation. J. Chem. Inf. Comput. Sci., 42, 117– 122. CrossrefCASPubMedWeb of Science®Google Scholar Agrafiotis, D.K. and Xu, H. (2003) A geodesic framework for analyzing molecular similarities. J. Chem. Inf. Comput. Sci., 43, 475– 484. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Bano, S. and Khadikar, P.V. (2003a) QSAR study on 5-lipoxygenase inhibitors using distance-based topological indices. Bioorg. Med. Chem., 11, 5519– 5527. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Bano, S. and Khadikar, P.V. (2003b) Topological approach to quantifying molecular lipophilicity of heterogeneous set of organic compounds. Bioorg. Med. Chem., 11, 4039– 4047. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Chaturvedi, S.C., Abraham, M.H. and Khadikar, P.V. (2003) QSAR study on tadpole narcosis. Bioorg. Med. Chem., 11, 4523– 4533. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Gupta, M., Singh, J. and Khadikar, P.V. (2005) A novel method of estimation of lipophilicity using distance-based topological indices: dominating role of equalized electronegativity. Bioorg. Med. Chem., 13, 2109– 2120. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Karmarkar, S. and Khadikar, P.V. (2002) QSAR study on competition binding of rodenticides (PATs) to H1 receptor in rat and guinea pig brain. Bioorg. Med. Chem., 10, 2913– 2918. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Karmarkar, S., Khadikar, P.V. and Shrivastava, S. (2003) Use of distance-based topological indices in modeling antihypertensive activity: case of 2-aryl-imino-imidazolines. Indian J. Chem., 42, 1426– 1435. Web of Science®Google Scholar Agrawal, V.K. and Khadikar, P.V. (2001) QSAR prediction of toxicity of nitrobenzenes. Bioorg. Med. Chem., 9, 3035– 3040. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K. and Khadikar, P.V. (2002) QSAR study on narcotic mechanism of action and toxicity: a molecular connectivity approach to Vibrio fischeri toxicity testing. Bioorg. Med. Chem., 10, 3517– 3522. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Sharma, R. and Khadikar, P.V. (2002) QSAR studies on carbonic anhydrase inhibitors: a case of ureido and thioureido derivatives of aromatic/heterocyclic sulfonamides. Bioorg. Med. Chem., 10, 2993– 2999. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Singh, J. and Khadikar, P.V. (2002) On the topological evidences for modeling lipophilicity. Bioorg. Med. Chem., 10, 3981– 3996. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Singh, J., Louis, B., Joshi, S., Joshi, A. and Khadikar, P.V. (2006) The topology of molecule and its lipophilicity. Curr. Comput. -Aided Drug Des., 2, 369– 403. CrossrefCASWeb of Science®Google Scholar Agrawal, V.K., Singh, K. and Khadikar, P.V. (2004) QSAR studies on adenosine kinase inhibitors. Med. Chem. Res., 13, 479– 496. CrossrefCASWeb of Science®Google Scholar Agrawal, V.K., Sohgaura, R. and Khadikar, P.V. (2002) QSAR studies on biological activity of piritrexim analogues against pc DHFR. Bioorg. Med. Chem., 10, 2919– 2926. CrossrefCASPubMedWeb of Science®Google Scholar Agrawal, V.K., Srivastava, S. and Khadikar, P.V. (2004) QSAR study on phosphoramidothioate (Ace) toxicities in housefly. Mol. Div., 8, 413– 419. CrossrefCASPubMedWeb of Science®Google Scholar Ahmad, P., Fyfe, C.A. and Mellors, A. (1975) Parachors in drug design. Biochem. Pharmacol., 24, 1103– 1110. CrossrefCASPubMedWeb of Science®Google Scholar Ai, N., DeLisle, R.K., Yu, S.J. and Welsh, W.J. (2003) Computational models for predicting the binding affinities of ligands for the wild-type androgen receptor and a mutated variant associated with human prostate cancer. Chem. Res. Toxicol., 16, 1652– 1660. CrossrefCASPubMedWeb of Science®Google Scholar Aihara, J. (1976) A generalized total π-energy index for a conjugated hydrocarbon. J. Org. Chem., 41, 2488– 2490. CrossrefCASWeb of Science®Google Scholar Aihara, J. (1977a) Aromatic sextets and aromaticity in benzenoid hydrocarbons. Bull. Chem. Soc. Jap., 50, 2010– 2012. CrossrefCASWeb of Science®Google Scholar Aihara, J. (1977b) Resonance energies of benzenoid hydrocarbons. J. Am. Chem. Soc., 99, 2048– 2053. CrossrefCASWeb of Science®Google Scholar Aihara, J. (1978) Resonance energies of nonbenzenoid hydrocarbons. Bull. Chem. Soc. Jap., 51, 3540– 3543. CrossrefCASWeb of Science®Google Scholar Aires-de-Sousa, J. (2003) Representation of molecular chirality, in Handbook of Chemoinformatics, Vol. 3 (ed. J. Gasteiger), Wiley-VCH Verlag GmbH, Weinheim, Germany, pp. 1062– 1078. Wiley Online LibraryGoogle Scholar Aires-de-Sousa, J. and Gasteiger, J. (2001) New description of molecular chirality and its application to the prediction of the preferred enantiomer in stereoselective reactions. J. Chem. Inf. Comput. Sci., 41, 369– 375. CrossrefCASPubMedWeb of Science®Google Scholar Aires-de-Sousa, J. and Gasteiger, J. (2002) Prediction of enantiomeric selectivity in chromatography. Application of conformation-dependent and conformation-independent descriptors of molecular chirality. J. Mol. Graph. Model., 20, 373– 388. CrossrefCASPubMedWeb of Science®Google Scholar Ajay, Walters, W.P. and Murcko, M.A. (1998) Can we learn to distinguish between “drug-like” and “nondrug-like” molecules? J. Med. Chem., 41, 3314– 3324. CrossrefCASPubMedWeb of Science®Google Scholar Akagi, T., Mitani, S., Komyoji, T. and Nagatani, K. (1995) Quantitative structure–activity relationships of fluazinam and related fungicidal N-phenylpyridinamines preventive activity against Botrytis cinerea . J. Pestic. Sci., 20, 279– 290. CrossrefCASWeb of Science®Google Scholar Akaike, H. (1974) A new look at the statistical model identification. IEEE Transaction of Automatic Control, AC-19, 716– 723. CrossrefCASWeb of Science®Google Scholar Al-Fahemi, J.H., Cooper, D.L. and Allan, N.L. (2005) The use of momentum–space descriptors for predicting octanol–water partition coefficients. J. Mol. Struct. (Theochem), 727, 57– 61. CrossrefCASWeb of Science®Google Scholar Albahri, T.A. and George, R.S. (2003) Artificial neural network investigation of the structural group contribution method for predicting pure components auto ignition temperature. Ind. Eng. Chem. Res., 42, 5708– 5714. CrossrefCASWeb of Science®Google Scholar Albert, R., Jeong, H. and Barabási, A.-L. (1999) Diameter of the World Wide Web. Nature, 401, 130. CrossrefCASWeb of Science®Google Scholar Albuquerque, M.G., Hopfinger, A.J., Barreiro, E.J. and de Alencastro, R.B. (1998) Four-dimensional quantitative structure–activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists. J. Chem. Inf. Comput. Sci., 38, 925– 938. CrossrefCASPubMedWeb of Science®Google Scholar Alifrangis, L.H., Christensen, I.T., Berglund, A., Sandberg, M., Hovgaard, L. and Frokjaer, S. (2000) Structure–property model for membrane partitioning of oligopeptides. J. Med. Chem., 43, 103– 113. CrossrefCASPubMedWeb of Science®Google Scholar Alikhanidi, S. and Takahashi, Y. (2006) New molecular fragmental descriptors and their application to the prediction of fish toxicity. MATCH Commun. Math. Comput. Chem., 55, 205– 232. Web of Science®Google Scholar Alkorta, I., Rozas, I. and Elguero, J. (1998) Bond length–electron density relationships: from covalent bonds to hydrogen bond interactions. Struct. Chem., 9, 243– 247. CrossrefCASWeb of Science®Google Scholar Allen, B.C.P., and W.G. (2001) calculations using molecular J. Chem. Inf. Comput. Sci., 41, CrossrefCASPubMedWeb of Science®Google Scholar Allen, B.C.P., and W.G. (2003) of structural similarity using J. Chem. Inf. Comput. Sci., 43, CrossrefCASPubMedWeb of Science®Google Scholar Allen, (1971) of prediction as a for selecting 13, CrossrefWeb of Science®Google Scholar Allen, (1974) The relationship between and data and a method for 16, CrossrefWeb of Science®Google Scholar Allen, and (1995) of using or descriptors. J. Chem. Inf. Comput. Sci., CrossrefWeb of Science®Google Scholar A. and and as proton in hydrogen correlation of with acceptor J. Am. Chem. Soc., 85, CrossrefCASWeb of Science®Google Scholar A. and in infrared studies of hydrogen bonding. J. Am. Chem. Soc., 85, CrossrefCASWeb of Science®Google Scholar A. and A scale of based on J. Chem., CrossrefCASWeb of Science®Google Scholar A. and values from data. J. Chem., CrossrefCASWeb of Science®Google Scholar A.M., A., M., P., P., A., and (2003) A analysis on inhibitors and by QSAR Comb. Sci., 22, Wiley Online LibraryCASWeb of Science®Google Scholar Analysis 16, Google Scholar (1990) Molecular a new method in quantitative structure–activity relationships Chemom. Intell. Lab. Syst., 8, CrossrefCASWeb of Science®Google Scholar (2000) J. 14, Wiley Online LibraryCASWeb of Science®Google Scholar A.M., J.J. and (1998) in regression Acta, 29– CrossrefCASWeb of Science®Google Scholar K. CrossrefCASWeb of Science®Google Scholar K. Phys. CrossrefWeb of Science®Google Scholar C., A., and (1997) Estimation of partitioning parameters of surfactants using descriptors of molecular and hydrogen bonding. J. Pharm. Sci., Wiley Online LibraryCASPubMedWeb of Science®Google Scholar C., P., N., and A. (1992) Quantitative relationship of of and Chem. Res. Toxicol., 375. CrossrefCASPubMedWeb of Science®Google Scholar C., S., A. and M. (1993) solvation energy relationships in liquid chromatography. in as stationary phase for lipophilicity Quant. Struct. -Act. Relat., Wiley Online LibraryCASWeb of Science®Google Scholar A., M. and A. (2001) The role of and parameters of and derivatives for their toxicity. J. Mol. Struct. (Theochem), CrossrefCASWeb of Science®Google Scholar S., S., D., S., M. and S. data analysis of descriptors. Chem. 37, CrossrefWeb of Science®Google Scholar R., Y., R.,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".