Consensus model for identification of novel PI3K inhibitors in large chemical library
暂无分享,去创建一个
Chin Yee Liew | Xiao Hua Ma | Chun Wei Yap | C. Yap | C. Liew | X. H. Ma
[1] Yu Zong Chen,et al. Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines , 2005, J. Chem. Inf. Model..
[2] Paola Gramatica,et al. Principles of QSAR models validation: internal and external , 2007 .
[3] B. Matthews. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.
[4] Jürgen Bajorath,et al. Towards Unified Compound Screening Strategies: A Critical Evaluation of Error Sources in Experimental and Virtual High‐Throughput Screening , 2006 .
[5] M. Herlyn,et al. Structure-based design of an organoruthenium phosphatidyl-inositol-3-kinase inhibitor reveals a switch governing lipid kinase potency and selectivity. , 2008, ACS chemical biology.
[6] D. Wiederschain,et al. Class IA phosphoinositide 3-kinase isoforms and human tumorigenesis: implications for cancer drug discovery and development , 2008, Current opinion in oncology.
[7] Xianghui Liu,et al. SVM Model for Virtual Screening of Lck Inhibitors , 2009, J. Chem. Inf. Model..
[8] C. Rommel,et al. Furan-2-ylmethylene thiazolidinediones as novel, potent, and selective inhibitors of phosphoinositide 3-kinase gamma. , 2006, Journal of medicinal chemistry.
[9] Lewis C Cantley,et al. The phosphoinositide 3-kinase pathway. , 2002, Science.
[10] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[11] Bernd Giese,et al. Targeting phosphoinositide 3-kinase: moving towards therapy. , 2008, Biochimica et biophysica acta.
[12] Vipin Kumar,et al. Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.
[13] W. Denny,et al. Synthesis, biological evaluation and molecular modelling of sulfonohydrazides as selective PI3K p110α inhibitors , 2007 .
[14] Pierre Baldi,et al. Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..
[15] Brian K. Shoichet,et al. Virtual screening of chemical libraries , 2004, Nature.
[16] J. F. Wang,et al. Prediction of P-Glycoprotein Substrates by a Support Vector Machine Approach , 2004, J. Chem. Inf. Model..
[17] Markus H. J. Seifert,et al. Essential factors for successful virtual screening. , 2008, Mini reviews in medicinal chemistry.
[18] Amanda C. Schierz. Virtual screening of bioassay data , 2009, J. Cheminformatics.
[19] Boon Chuan Low,et al. Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds , 2008, J. Chem. Inf. Model..
[20] Marketa Zvelebil,et al. Phosphoinositide 3-kinase signalling--which way to target? , 2003, Trends in pharmacological sciences.
[21] W. Denny,et al. Phosphoinositide-3-kinase (PI3K) inhibitors: identification of new scaffolds using virtual screening. , 2009, Bioorganic & medicinal chemistry letters.
[22] Xin Chen,et al. Virtual screening to successfully identify novel janus kinase 3 inhibitors: a sequential focused screening approach. , 2008, Journal of medicinal chemistry.
[23] M. Waterfield,et al. Synthesis and biological evaluation of 4-morpholino-2-phenylquinazolines and related derivatives as novel PI3 kinase p110alpha inhibitors. , 2006, Bioorganic & medicinal chemistry.
[24] Xin Chen,et al. Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents , 2004, J. Chem. Inf. Model..
[25] Z R Li,et al. Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. , 2006, Mini reviews in medicinal chemistry.
[26] Paola Gramatica,et al. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .
[27] Raphaël Frédérick,et al. Phosphoinositide-3-kinases (PI3Ks): Combined Comparative Modeling and 3D-QSAR To Rationalize the Inhibition of p110α , 2008, J. Chem. Inf. Model..
[28] Bernard F. Buxton,et al. Support Vector Machines in Combinatorial Chemistry , 2001 .
[29] Z. R. Li,et al. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. , 2008, Journal of molecular graphics & modelling.
[30] Pablo San Segundo,et al. Exploiting CPU Bit Parallel Operations to Improve Efficiency in Search , 2007 .
[31] B. Shoichet,et al. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. , 2002, Journal of medicinal chemistry.
[32] Christopher I. Bayly,et al. Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..
[33] R. Czerminski,et al. Use of Support Vector Machine in Pattern Classification: Application to QSAR Studies , 2001 .
[34] P. Fischer,et al. Computational chemistry approaches to drug discovery in signal transduction , 2008, Biotechnology journal.
[35] Juan J Perez,et al. Managing molecular diversity. , 2005, Chemical Society reviews.
[36] Nina Nikolova-Jeliazkova,et al. QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review , 2005, Alternatives to laboratory animals : ATLA.
[37] Robbie Loewith,et al. A Pharmacological Map of the PI3-K Family Defines a Role for p110α in Insulin Signaling , 2006, Cell.