In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems.
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Akash Khandelwal | Ramamurthi Narayanan | Akash Khandelwal | S. Gunturi | R. Narayanan | Sitarama B Gunturi
[1] U Norinder,et al. Experimental and computational screening models for the prediction of intestinal drug absorption. , 2001, Journal of medicinal chemistry.
[2] G Beck,et al. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. , 2001, Journal of pharmaceutical sciences.
[3] Jürgen Bajorath,et al. Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.
[4] G. Sudlow,et al. The characterization of two specific drug binding sites on human serum albumin. , 1975, Molecular pharmacology.
[5] Ramamurthi Narayanan,et al. In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method. , 2005, Bioorganic & medicinal chemistry.
[6] David J. Begley,et al. Potential of Immobilized Artificial Membranes for Predicting Drug Penetration Across the Blood−Brain Barrier , 1998, Pharmaceutical Research.
[7] D. E. Clark,et al. Progress in computational methods for the prediction of ADMET properties. , 2002, Current opinion in drug discovery & development.
[8] Milan Randic,et al. On Interpretation of Well-Known Topological Indices , 2001, J. Chem. Inf. Comput. Sci..
[9] Roman Kaliszan,et al. Quantitative structure-enationselective retention relationships for the chromatography of 1,4-benzodiazepines on a human serum albumin based HPLC chiral stationary phase: An approach to the computational prediction of retention and enantioselectivity , 1992 .
[10] Tingjun Hou,et al. Recent development and application of virtual screening in drug discovery: an overview. , 2004, Current pharmaceutical design.
[11] Shu-Shen Liu,et al. VSMP: A Novel Variable Selection and Modeling Method Based on the Prediction , 2003, J. Chem. Inf. Comput. Sci..
[12] M Dorigo,et al. Ant colonies for the travelling salesman problem. , 1997, Bio Systems.
[13] H. Vorum. Reversible ligand binding to human serum albumin. Theoretical and clinical aspects. , 1999, Danish medical bulletin.
[14] Michael H Abraham,et al. The factors that influence permeation across the blood-brain barrier. , 2004, European journal of medicinal chemistry.
[15] J. Topliss,et al. Unified model for the corneal permeability of related and diverse compounds with respect to their physicochemical properties. , 1996, Journal of pharmaceutical sciences.
[16] Roald Hoffmann,et al. QUALITATIVE THINKING IN THE AGE OF MODERN COMPUTATIONAL CHEMISTRY : OR WHAT LIONEL SALEM KNOWS , 1998 .
[17] V. Andrisano,et al. Enantioselective separation of chiral arylcarboxylic acids on an immobilized human serum albumin chiral stationary phase. , 1997, Chirality.
[18] Gabriele Cruciani,et al. Predicting drug metabolism: a site of metabolism prediction tool applied to the cytochrome P450 2C9. , 2003, Journal of medicinal chemistry.
[19] Andreas Zell,et al. Feature Selection for Descriptor Based Classification Models. 2. Human Intestinal Absorption (HIA) , 2004, J. Chem. Inf. Model..
[20] Tudor I. Oprea,et al. Virtual Screening in Lead Discovery: A Viewpoint† , 2002, Molecules : A Journal of Synthetic Chemistry and Natural Product Chemistry.
[21] Lemont B. Kier,et al. Modeling Blood-Brain Barrier Partitioning Using the Electrotopological State , 2002, J. Chem. Inf. Comput. Sci..
[22] Arun K Mandagere,et al. Graphical model for estimating oral bioavailability of drugs in humans and other species from their Caco-2 permeability and in vitro liver enzyme metabolic stability rates. , 2002, Journal of medicinal chemistry.
[23] Gabriele Cruciani,et al. Predicting human serum albumin affinity of interleukin-8 (CXCL8) inhibitors by 3D-QSPR approach. , 2005, Journal of medicinal chemistry.
[24] H. van de Waterbeemd,et al. ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.
[25] Vladimir Poroikov,et al. A New Statistical Approach to Predicting Aromatic Hydroxylation Sites. Comparison with Model-Based Approaches , 2004, J. Chem. Inf. Model..
[26] Franco Lombardo,et al. In silico ADME prediction: data, models, facts and myths. , 2003, Mini reviews in medicinal chemistry.
[27] C. Beddell,et al. Binding measurements of indolocarbazole derivatives to immobilised human serum albumin by high-performance liquid chromatography. , 1996, Journal of chromatography. B, Biomedical applications.
[28] H. Kubinyi. Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution , 1994 .
[29] Toshio Kinoshita,et al. Prediction of human serum albumin–drug binding affinity without albumin , 2002 .
[30] Hao Zhu,et al. ESP: A Method To Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases , 2004, J. Chem. Inf. Model..
[31] T. Kinoshita,et al. Quantitative analysis of human serum albumin-drug interactions using reversed-phase and ion-exchange liquid chromatography , 1999 .
[32] D. Agrafiotis,et al. Variable selection for QSAR by artificial ant colony systems , 2002, SAR and QSAR in environmental research.
[33] B Testa,et al. Predicting blood-brain barrier permeation from three-dimensional molecular structure. , 2000, Journal of medicinal chemistry.
[34] Philip L. Smith,et al. In vitro models to predict blood-brain barrier permeability , 1997 .
[35] R. Kaliszan,et al. Quantitative structure-retention relationships in affinity high-performance liquid chromatography. , 2002, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.
[36] Roberto Todeschini,et al. Handbook of Molecular Descriptors , 2002 .
[37] Subhash C. Basak,et al. Prediction of Cellular Toxicity of Halocarbons from Computed Chemodescriptors: A Hierarchical QSAR Approach , 2003, J. Chem. Inf. Comput. Sci..
[38] C. Decker,et al. Prediction of pharmacokinetic properties using experimental approaches during early drug discovery. , 2001, Current opinion in chemical biology.
[39] T. Hanai,et al. Influence of glycosylation on the drug binding of human serum albumin. , 1998, Biomedical chromatography : BMC.
[40] Anton J. Hopfinger,et al. Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..
[41] Dimitris K. Agrafiotis,et al. A Novel Method for Building Regression Tree Models for QSAR Based on Artificial Ant Colony Systems , 2001, J. Chem. Inf. Comput. Sci..
[42] S. Urien,et al. Drug Binding in Plasma , 1994, Clinical pharmacokinetics.
[43] John G. Topliss,et al. QSAR Model for Drug Human Oral Bioavailability1 , 2000 .
[44] Joseph V. Turner,et al. Bioavailability Prediction Based on Molecular Structure for a Diverse Series of Drugs , 2004, Pharmaceutical Research.
[45] D C Carter,et al. Structure of human serum albumin. , 1990, Science.
[46] Christopher Hulme,et al. Knowledge-based approaches in the design and selection of compound libraries for drug discovery. , 2002, Current opinion in drug discovery & development.
[47] M. Karelson. Molecular descriptors in QSAR/QSPR , 2000 .
[48] Ruisheng Zhang,et al. QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine , 2004, J. Chem. Inf. Model..
[49] Han van de Waterbeemd,et al. High-throughput and in silico techniques in drug metabolism and pharmacokinetics. , 2002 .
[50] Stephen R. Johnson,et al. Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.
[51] D C Carter,et al. Three-dimensional structure of human serum albumin. , 1989, Science.
[52] Hxugo Kubiny. Variable Selection in QSAR Studies. I. An Evolutionary Algorithm , 1994 .
[53] A J Hopfinger,et al. Characterization of skin penetration processes of organic molecules using molecular similarity and QSAR analysis. , 2004, Molecular pharmaceutics.
[54] Mark A. Murcko,et al. Virtual screening : an overview , 1998 .
[55] T. Kennedy. Managing the drug discovery/development interface , 1997 .
[56] A. Cavalli,et al. Stereoselective binding of 2,3-substituted 3-hydroxypropionic acids on an immobilised human serum albumin chiral stationary phase: stereochemical characterisation and quantitative structure-retention relationship study. , 2000, Journal of chromatography. A.
[57] J. Lavandera,et al. Cheminformatic models to predict binding affinities to human serum albumin. , 2001, Journal of medicinal chemistry.
[58] Sandra Fox,et al. High Throughput Screening for Drug Discovery: Continually Transitioning into New Technology , 1999, Journal of biomolecular screening.
[59] G. Sudlow,et al. Further characterization of specific drug binding sites on human serum albumin. , 1976, Molecular pharmacology.
[60] Bruno Boulanger,et al. Towards a new age of virtual ADME/TOX and multidimensional drug discovery , 2002, J. Comput. Aided Mol. Des..
[61] Lemont B. Kier,et al. Modeling Drug Albumin Binding Affinity with E-State Topological Structure Representation , 2003, J. Chem. Inf. Comput. Sci..
[62] Tingjun Hou,et al. ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors , 2003, J. Chem. Inf. Comput. Sci..
[63] Luca Maria Gambardella,et al. Ant Algorithms for Discrete Optimization , 1999, Artificial Life.
[64] K M Merz,et al. Molecular dynamics study of the IIA binding site in human serum albumin: influence of the protonation state of Lys195 and Lys199. , 2001, Journal of medicinal chemistry.
[65] Michael C. Hutter,et al. Prediction of blood–brain barrier permeation using quantum chemically derived information , 2003, J. Comput. Aided Mol. Des..
[66] S. O'Brien,et al. Greater than the sum of its parts: combining models for useful ADMET prediction. , 2005, Journal of medicinal chemistry.