Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients

HANSCH, CORWIN, MALONEY, PEYTON P., FUJITA, TOSHIO, MUIR, ROBERT M.·1962·Nature

Cited by (1,000)

Items 101–200 of 1,000

  1. Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research
    Jacob Kerner, Alan B. Dogan, Horst A. von Recum · 2020 · Acta Biomaterialia
  2. Calculating log P(oct) with no missing fragments;The problem of estimating new interaction parameters
    Albert J. Leo, David A. Hoekman · 2000 · Perspectives in Drug Discovery and Design
  3. Determination of isotherms from chromatographic peak shapes
    Eric V. Dose, Stephen C. Jacobson, Georges A. Guiochon · 1991 · Analytical Chemistry
  4. Correlation and estimation of gas–chloroform and water–chloroform partition coefficients by a linear free energy relationship method
    Michael H. Abraham, James A. Platts, Anne Hersey, Albert J. Leo, Robert W. Taft · 1999 · Journal of Pharmaceutical Sciences
  5. Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications
    Chia‐Hsiu Chen, Kenichi Tanaka, Masaaki Kotera, Kimito Funatsu · 2020 · Journal of Cheminformatics
  6. Linear free energy relationship analysis of microemulsion electrokinetic chromatographic determination of lipophilicity
    Michael H. Abraham, Claude Treiner, Martı́ Rosés, Clara Ràfols, Yasushi Ishihama · 1996 · Journal of Chromatography A
  7. Similarity methods in chemoinformatics
    Peter Willett · 2009 · Annual Review of Information Science and Technology
  8. The Use of Artificial Neural Networks in QSAR
    David William Salt, Nihat Yıldız, David J. Livingstone, Chris J. Tinsley · 1992 · Pesticide Science
  9. Binary Quantitative Structure−Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands
    Hua Gao, Chris Williams, Paul Labute, Jürgen Bajorath · 1998 · Journal of Chemical Information and Computer Sciences
  10. The RP-HPLC measurement and QSPR analysis of logPo/w values of several Pt(II) complexes
    James A. Platts, Steven P. Oldfield, Maria Reif, Alessandra Palmucci, Elisabetta Gabano, Domenico Osella · 2006 · Journal of Inorganic Biochemistry
  11. Relating chemical activity to structure: An examination of ILP successes
    Ross D. King, Ashwin Srinivasan, Michael J.E. Sternberg · 1996 · New Generation Computing
  12. Multivariate statistical analysis methods in QSAR
    Somayeh Pirhadi, Fereshteh Shiri, Jahan Bakhsh Ghasemi · 2015 · RSC Advances
  13. Computational Approaches to Lipophilicity: Methods and Applications
    Pierre‐Alain Carrupt, Bernard Testa, Patrick Gaillard · 1997 · Reviews in computational chemistry
  14. Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects
    Weichen Bo, Lang Chen, Dongya Qin, Sheng Geng, Jiaqi Li, Mei Ying Hu, Bo Li, Guizhao Liang · 2021 · Trends in Food Science & Technology
  15. Machine Learning in Drug Discovery
    Günter Klambauer, Sepp Hochreiter, Matthias Rarey · 2019 · Journal of Chemical Information and Modeling
  16. Digital Pharmaceutical Sciences
    Safa A. Damiati · 2020 · AAPS PharmSciTech
  17. Comparative QSAR and the Radical Toxicity of Various Functional Groups
    Cynthia Dias Selassie, Rajni Garg, Sanjay Kapur, Alka Kurup, Rajeshwar P. Verma, Suresh Babu Mekapati, Corwin H. Hansch · 2002 · Chemical Reviews
  18. Herbicides of photosystem II
    Walter Oettmeier · 1992 · Elsevier eBooks
  19. Feature construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes
    Ashwin Srinivasan, Ross D. King · 1999 · Data Mining and Knowledge Discovery
  20. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
    Xiliang Yan, Tongtao Yue, David Alan Winkler, Yongguang Yin, Hao Zhu, Guibin Jiang, Bing Yan · 2023 · Chemical Reviews
  21. Explainable Machine Learning for Property Predictions in Compound Optimization
    Raquel Rodríguez-Pérez, Jürgen Bajorath · 2021 · Journal of Medicinal Chemistry
  22. Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach
    Francesca Grisoni, Davide Ballabio, Roberto Todeschini, Viviana Consonni · 2018 · Methods in molecular biology
  23. Deep learning for predicting toxicity of chemicals: a mini review
    Weihao Tang, Jingwen Chen, Zhongyu Wang, Hong‐Bin Xie, Huixiao Hong · 2018 · Journal of Environmental Science and Health Part C
  24. AI in drug development: a multidisciplinary perspective
    Víctor Gallego, Roi Naveiro, Carlos Roca, David Rı́os Insua, Nuria Eugenia Campillo · 2021 · Molecular Diversity
  25. Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines
    Jonathan D. Hirst, Ross D. King, Michael J.E. Sternberg · 1994 · Journal of Computer-Aided Molecular Design
  26. Octanol-Water Partition Constant
    Susana Amézqueta, Xavier Subirats, Elisabet Fuguet, Martı́ Rosés, Clara Ràfols · 2019 · Elsevier eBooks
  27. Chemometrics in QSAR
    Roberto Todeschini, Viviana Consonni, Paola Gramatica · 2009 · Elsevier eBooks
  28. An approach toward the problem of outliers in QSAR
    Rajeshwar P. Verma, Corwin H. Hansch · 2005 · Bioorganic & Medicinal Chemistry
  29. Application of Cascade Correlation Networks for Structures to Chemistry
    Anna Maria Bianucci, Alessio Micheli, Alessandro Sperduti, Antonina Starita · 2000 · Applied Intelligence
  30. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies
    Katarina M. Nikolic, Lazaros Mavridis, Teodora Djikić, Jelica Vučićević, Danica D. Agbaba, Kemal Yelekçi, John B. O. Mitchell · 2016 · Frontiers in Neuroscience
  31. A computer automated structure evaluation (CASE) approach to calculation of partition coefficient
    Gilles Klopman, Shaomeng Wang · 1991 · Journal of Computational Chemistry
  32. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information
    Wen Zhang, Yanlin Chen, Dingfang Li · 2017 · Molecules
  33. Universal Approach for Structural Interpretation of QSAR/QSPR Models
    Polishchuk, Pavel G., Kuz'min, Victor E., Artemenko, Anatoly G., Muratov, Eugene N. · 2013 · Molecular Informatics
  34. ToxRead: A tool to assist in read across and its use to assess mutagenicity of chemicals
    Giuseppina C. Gini, A.M. Franchi, Alberto Manganaro, Azadi Golbamaki, Emilio Benfenati · 2014 · SAR and QSAR in environmental research
  35. Cyclooxygenase (COX) Inhibitors: A Comparative QSAR Study
    Rajni Garg, Alka Kurup, Suresh Babu Mekapati, Corwin H. Hansch · 2003 · Chemical Reviews
  36. Machine learning-assisted exploration of a versatile polymer platform with charge transfer-dependent full-color emission
    Suiying Ye, Nastaran Meftahi, Igor Lyskov, Tian Tian, Richard Whitfield, Sudhir Kumar, Andrew Joseph Christofferson, David Alan Winkler, Chih‐Jen Shih, Salvy P. Russo et al. · 2023 · Chem
  37. Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database
    Mariusz Butkiewicz, Edward W. Lowe, Ralf Mueller, Jeffrey Mendenhall, Pedro L. Teixeira, C. David Weaver, Jens Meiler · 2013 · Molecules
  38. Promises and Pitfalls of Quantitative Structure−Activity Relationship Approaches for Predicting Metabolism and Toxicity
    Elton Zvinavashe, Albertinka Jacoba Murk, Ivonne M.C.M. Rietjens · 2008 · Chemical Research in Toxicology
  39. Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure−Activity Relationship Studies of Benzodiazepines
    Alessio Micheli, Alessandro Sperduti, Antonina Starita, Anna Maria Bianucci · 2000 · Journal of Chemical Information and Computer Sciences
  40. Fragmental Approach in QSPR
    N. S. Zefirov, Vladimir A. Palyulin · 2002 · Journal of Chemical Information and Computer Sciences
  41. Correlation of partition coefficients in n-heptane-aqueous systems with buccal absorption data for a series of amines and acids
    A H Beckett, A.C. Moffat · 1969 · Journal of Pharmacy and Pharmacology
  42. A strategy for ranking environmentally occurring chemicals
    Jörgen Jönsson, Lennart Eriksson, Michael Sjöstróm, Svante Wold, Maria Livia Tosato · 1989 · Chemometrics and Intelligent Laboratory Systems
  43. Experimental and Computational Nanotoxicology—Complementary Approaches for Nanomaterial Hazard Assessment
    Valérie Forest · 2022 · Nanomaterials
  44. Computer-aided nanotoxicology: risk assessment of metal oxide nanoparticles via nano-QSAR
    Jiakai Cao, Yong Liang Pan, Yanting Jiang, Ronghua Qi, Beilei Yuan, Zhenhua Jia, Juncheng Jiang, Qingsheng Wang · 2020 · Green Chemistry
  45. Lipophilicity of the Nitrophenols
    Michael H. Abraham, Chau My Du, James A. Platts · 2000 · The Journal of Organic Chemistry
  46. Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods
    Nikil Wale, George Karypis · 2009 · Journal of Chemical Information and Modeling
  47. Antibiotic discovery in the artificial intelligence era
    Telmah Lluka, Jonathan M. Stokes · 2022 · Annals of the New York Academy of Sciences
  48. The inevitable QSAR renaissance
    RICHARD D. III CRAMER · 2011 · Journal of Computer-Aided Molecular Design
  49. Comparative QSAR: Angiotensin II Antagonists
    Alka Kurup, Rajni Garg, David J. Carini, Corwin H. Hansch · 2001 · Chemical Reviews
  50. Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method
    Zhenxing Wu, Dejun Jiang, Chang‐Yu Hsieh, Guangyong Chen, Ben Liao, Dongsheng Cao, Tingjun Hou · 2021 · Briefings in Bioinformatics
  51. Warmr: a data mining tool for chemical data
    Ross D. King, Ashwin Srinivasan, Luc Dehaspe · 2001 · Journal of Computer-Aided Molecular Design
  52. Predicting the pKa of Small Molecules
    Matthias Rupp, Robert Körner, Igor V. Tetko · 2011 · Combinatorial Chemistry & High Throughput Screening
  53. Quantum mechanics implementation in drug-design workflows: does it really help?
    Olayide A Arodola, Mahmoud E. S. Soliman · 2017 · Drug Design Development and Therapy
  54. Discovery of an Orally Bioavailable Inhibitor of Defective in Cullin Neddylation 1 (DCN1)-Mediated Cullin Neddylation
    Jared T. Hammill, Deepak Bhasin, Daniel C. Scott, Jaeki Min, Yizhe Chen, Yan Lü, Lei Yang, Ho Shin Kim, Michele Connelly, Courtney Vowell Hammill et al. · 2018 · Journal of Medicinal Chemistry
  55. History of Quantitative Structure‐Activity Relationships
    Cynthia Dias Selassie · 2003 · Wiley eBooks
  56. Nano-QSAR modeling for ecosafe design of heterogeneous TiO 2 -based nano-photocatalysts
    Alicja Mikołajczyk, Agnieszka Gajewicz, Ewa Mulkiewicz, Bakhtiyor Rasulev, Martyna Marchelek, Magdalena Diak, Seishiro Hirano, Adriana Zaleska‐Medynska, Tomasz Puzyn · 2018 · Environmental Science Nano
  57. An Approach towards the Quantitative Structure–Activity Relationships of Caffeic Acid and its Derivatives
    Rajeshwar P. Verma, Corwin H. Hansch · 2004 · ChemBioChem
  58. The Characterisation of (Quantitative) Structure-Activity Relationships: Preliminary Guidance
    AP Worth, Arianna Bassan, A.F. Gallegos, Netzeva Ti, Grace Y. Patlewicz, Manuela Pavan, Ivanka Tsakovska, Marjan Vračko · 2005
  59. Partitioning of Selected Estrogenic Compounds between Synthetic Membrane Vesicles and Water: Effects of Lipid Components
    Hiroshi Yamamoto, Howard M. Liljestrand · 2004 · Environmental Science & Technology
  60. An Overview of Antiviral Peptides and Rational Biodesign Considerations
    Ying-Chiang Jeffrey Lee, Jaden D. Shirkey, Jongbeom Park, Karishma Bisht, Alexis J. Cowan · 2022 · BioDesign Research
  61. Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR
    Anita Rácz, Dávid Bajusz, Károly Héberger · 2019 · Molecular Informatics
  62. Viscosity models for ionic liquids and their mixtures
    Anya F. Bouarab, Jean‐Philippe Harvey, Christian Robelin · 2020 · Physical Chemistry Chemical Physics
  63. Equivariant Graph Neural Networks for Toxicity Prediction
    Cremer, Julian, Medrano Sandonas, Leonardo, Tkatchenko, Alexandre, Clevert, Djork-Arné, De Fabritiis, Gianni · 2023 · Chemical Research in Toxicology
  64. Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential
    Ovidiu Ivanciuc · 2008 · Current Topics in Medicinal Chemistry
  65. Investigating biological activity spectrum for novel quinoline analogues 2: Hydroxyquinolinecarboxamides with photosynthesis-inhibiting activity
    Robert Musioł, Dominik Tabak, Halina Niedbala, Barbara Podeszwa, Josef Jampílek, Katarína Kráľová, Jiřı́ Dohnal, Jacek Finster, Agnieszka Mencel, Jarosław Polański · 2008 · Bioorganic & Medicinal Chemistry
  66. BRADSHAW: a system for automated molecular design
    Darren V. S. Green, Stephen D. Pickett, Chris N. Luscombe, Stefan Senger, David Marcus, Jamel Eddine Meslamani, David Brett, Adam Powell, Jonathan Masson · 2019 · Journal of Computer-Aided Molecular Design
  67. FISH SUBCHRONIC TOXICITY PREDICTION MODEL FOR INDUSTRIAL ORGANIC CHEMICALS THAT PRODUCE NARCOSIS
    Daniel J. Call, Larry T. Brooke, Michael L. Knuth, S. H. Poirier, Marilynn D. Hoglund · 1985 · Environmental Toxicology and Chemistry
  68. Computer‐Assisted Molecular Design (CAMD)—An Overview
    Horst Frühbeis, Robert Klein, Holger Wallmeier · 1987 · Angewandte Chemie International Edition in English
  69. Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes
    Niazi, Sarfaraz K. · 2025 · Pharmaceuticals
  70. Quantitative retention-biological activity relationship study by micellar liquid chromatography
    Emelita D. Breyer, Joost K. Strasters, Morteza G. Khaledi · 1991 · Analytical Chemistry
  71. Relating chemical activity to structure: An examination of ILP successes
    Ross D. King, Michael J.E. Sternberg, Ashwin Srinivasan · 1995 · New Generation Computing
  72. A quantitative approach to polar organic reactivity
    Herbert Mayr, Armin R. Ofial · 2015 · SAR and QSAR in environmental research
  73. Chemoinformatics: a history
    Peter Willett · 2011 · Wiley Interdisciplinary Reviews Computational Molecular Science
  74. Computer-modeling-based QSARs for analyzing experimental data on biotransformation and toxicity
    Ans E. M. F. Soffers, Marelle G. Boersma, Wouter H. J. Vaes, Jacques J. M. Vervoort, Bożena Tyrakowska, Joop L. M. Hermens, Ivonne M.C.M. Rietjens · 2001 · Toxicology in Vitro
  75. Structure-based prediction of ligand–protein interactions on a genome-wide scale
    Howook Hwang, Fabian Dey, Donald S. Petrey, Barry H. Honig · 2017 · Proceedings of the National Academy of Sciences
  76. The development of a quantitative structure property relationship (QSPR) for the prediction of dielectric constants using neural networks
    Robert C. Schweitzer, Jeffrey B. Morris · 1999 · Analytica Chimica Acta
  77. A comparison between 2D and 3D descriptors in QSAR modeling based on bio‐active conformations
    Malkeet Singh Bahia, Omer Kaspi, Meir Touitou, Idan Binayev, Seema Dhail, J. Dominik Spiegel, Netaly Khazanov, Abraham Yosipof, Hanoch Senderowitz · 2023 · Molecular Informatics
  78. Lipophilicity of some GABAergic phenols and related compounds determined by HPLC and partition coefficients in different systems
    Gabriela N. Reiner, Diana Labuckas, Daniel A. Garcı́a · 2009 · Journal of Pharmaceutical and Biomedical Analysis
  79. Use of13C NMR Chemical Shift as QSAR/QSPR Descriptor
    Rajeshwar P. Verma, Corwin H. Hansch · 2011 · Chemical Reviews
  80. Modelling methods and cross-validation variants in QSAR: a multi-level analysis$
    Anita Rácz, Dávid Bajusz, Károly Héberger · 2018 · SAR and QSAR in environmental research
  81. QSAR and QSPR Studies of a Highly Structured Physicochemical Domain
    Orazio Nicolotti, Angelo Carotti · 2005 · Journal of Chemical Information and Modeling
  82. The advancement of multidimensional QSAR for novel drug discovery - where are we headed?
    Tao Wang, Xinsong Yuan, Mianbin Wu, Jianping Lin, Li-Rong Yang · 2017 · Expert Opinion on Drug Discovery
  83. Simple Quantum Chemical Parameters as an Alternative to the Hammett Sigma Constants in QSAR Studies
    Peter Ertl · 1997 · Quantitative Structure-Activity Relationships
  84. High-Throughput Liquid Chromatography/Mass Spectrometry Method for the Determination of the Chromatographic Hydrophobicity Index
    Giulio Camurri, Alessio Zaramella · 2001 · Analytical Chemistry
  85. Nonlinear Methods in the Analysis of Protein Sequences: A Case Study in Rubredoxins
    Alessandro Giuliani, Romualdo Benigni, Paolo Sirabella, Joseph P. Zbilut, Alfredo Colosimo · 2000 · Biophysical Journal
  86. Data enhanced Hammett-equation: reaction barriers in chemical space
    Marco Bragato, Guido Falk von Rudorff, O. Anatole von Lilienfeld · 2020 · Chemical Science
  87. Modeling calcium channel antagonistic activity of dihydropyridine derivatives using QTMS indices analyzed by GA-PLS and PC-GA-PLS
    Afshan Mohajeri, Bahram Hemmateenejad, Ahmad Reza Mehdipour, Ramin Miri · 2007 · Journal of Molecular Graphics and Modelling
  88. Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines
    Jonathan D. Hirst, Ross D. King, Michael J.E. Sternberg · 1994 · Journal of Computer-Aided Molecular Design
  89. Robust scoring functions for protein-ligand interactions with quantum chemical charge models.
    Wang, Jui-Chih, Lin, Jung-Hsin, Chen, Chung-Ming, Perryman, Alex L., Olson, Arthur J. · 2011 · Journal of Chemical Information and Modeling
  90. Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis
    Hongyu Chen, Shigeru Yamaguchi, Yuya Morita, Hiroyasu Nakao, Xiangning Zhai, Y. Shimizu, Harunobu Mitsunuma, Motomu Kanai · 2021 · Cell Reports Physical Science
  91. Effect of the electronic and physicochemical parameters on the carcinogenesis activity of some sulfa drugs using QSAR analysis based on genetic-MLR and genetic-PLS
    Omar Deeb, Bahram Hemmateenejad, Amal Jaber, Ramón Garduño‐Juárez, Ramin Miri · 2007 · Chemosphere
  92. The effect of nitroaromatics’ composition on their toxicity in vivo: Novel, efficient non-additive 1D QSAR analysis
    V. Е. Kuz’min, Eugene N. Muratov, Anatoly G. Artemenko, Leonid Gorb, Mohammad Qasim, Jerzy Leszczyński · 2008 · Chemosphere
  93. The Interactions of Bilirubin with Model and Biological Membranes
    Maurice Leonard, Noa Noy, David Zakim · 1989 · Journal of Biological Chemistry
  94. Applications of conceptual density functional theory in reference to quantitative structure–activity / property relationship
    Pooja Sharma, Prabhat Ranjan, Tanmoy Chakraborty · 2024 · Molecular Physics
  95. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies
    Mingyue Zheng, Jihui Zhao, Chen Cui, Zunyun Fu, Xutong Li, Xiaohong Liu, Xiaoyu Ding, Xiaoqin Tan, Fei Li, Xiaomin Luo et al. · 2018 · Medicinal Research Reviews
  96. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models
    Vinícius M. Alves, Alexander Golbraikh, Stephen J. Capuzzi, Kammy Liu, Wai In Lam, Daniel Korn, Diane Pozefsky, Carolina Horta Andrade, Eugene N. Muratov, Alexander Tropsha · 2018 · Journal of Chemical Information and Modeling
  97. New Approach to Pharmacophore Mapping and QSAR Analysis Using Inductive Logic Programming. Application to Thermolysin Inhibitors and Glycogen Phosphorylase b Inhibitors
    Nathalie Marchand-Geneste, Kimberly A. Watson, Bjørn Kåre Alsberg, Ross D. King · 2001 · Journal of Medicinal Chemistry
  98. Quantitative systems toxicology
    Peter Bloomingdale, Conrad Housand, Joshua F. Apgar, Bjorn L. Millard, Donald E. Mager, John M. Burke, Dhaval K. Shah · 2017 · Current Opinion in Toxicology
  99. Synthesis, inhibitory activities, and QSAR study of xanthone derivatives as α-glucosidase inhibitors
    Yan Liu, Zhuofeng Ke, Jian‐Fang Cui, Wen‐Hua Chen, Lin Ma, Bo Wang · 2008 · Bioorganic & Medicinal Chemistry
  100. Comparative QSAR studies on PAMPA/modified PAMPA for high throughput profiling of drug absorption potential with respect to Caco-2 cells and human intestinal absorption
    Rajeshwar P. Verma, Corwin H. Hansch, Cynthia Dias Selassie · 2007 · Journal of Computer-Aided Molecular Design