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

A Reëxamination of the Hammett Equation.

Jaffe, H. H. (1953) · Chemical Reviews · doi:10.1021/cr60165a003 · Cited by 2,132

Electroanalytical Chemistry

Monnier, D. (1959) · Analytica Chimica Acta · doi:10.1016/0003-2670(59)80186-0 · Cited by 252

Plant Growth Regulation

Jameson, D. A. (1962) · Journal of Range Management · doi:10.2307/3894877 · Cited by 217

The Chemistry and Mode of Action of Plant Growth Substances

WIGHTMAN, F. (1956) · Soil Science · doi:10.1097/00010694-195609000-00012 · Cited by 193

Auxin-Induced Growth Inhibition a Natural Consequence of Two-Point Attachment

Foster, R. J., McRae, D. H., & Bonner, J. (1952) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.38.12.1014 · Cited by 90

The Acidic Dissociation Constants of Phenoxyacetic Acid and its Derivatives

Hayes, N. V., & Branch, G. E. K. (1943) · Journal of the American Chemical Society · doi:10.1021/ja01248a031 · Cited by 56

FURTHER EVIDENCE FOR A CHEMICAL REACTION BETWEEN PLANT GROWTH-REGULATORS AND A PLANT SUBSTRATE

Hansch, C. H., Muir, R. M., & Metzenberg, R. L. (1951) · PLANT PHYSIOLOGY · doi:10.1104/pp.26.4.812 · Cited by 47

Electronic Structure and Auxin Activity of Benzoic Acid Derivatives

Fukui, K., Nagata, C., & Yonezawa, T. (1958) · Journal of the American Chemical Society · doi:10.1021/ja01542a058 · Cited by 43

DIORTHO SUBSTITUTED PHENOXYACETIC ACIDS AS ANTIAUXINS

McRae, D. H., & Bonner, J. (1952) · PLANT PHYSIOLOGY · doi:10.1104/pp.27.4.834 · Cited by 42

Chemical Constitution as Related to Growth Regulator Action

Muir, R. M., & Hänsch, C. (1955) · Annual Review of Plant Physiology · doi:10.1146/annurev.pp.06.060155.001105 · Cited by 39

A dichotomy between lipophilicity and in vivo and in vitro genotoxicity

Rosenkranz, H. S., & Klopman, G. (1993) · Mutation Research/Environmental Mutagenesis and Related Subjects · doi:10.1016/0165-1161(93)90152-p · Cited by 3

Iopronic acid interaction with hepatic microsomal cytochrome P-450: relationship between chemical structure and binding

Facino, R. M., Raffaeli, E., & Pitrè, D. (1979) · Pharmacological Research Communications · doi:10.1016/s0031-6989(79)80064-8 · Cited by 3

Quantitative Structure — Activity Relationship Studies Around Cytotoxic Drugs

Rekker, R. F. (1983) · Springer eBooks · doi:10.1007/978-94-009-6798-4_3 · Cited by 3

Drug Design with Artificial Neural Networks

(2011) · Springer eBooks · doi:10.1007/springerreference_60306 · Cited by 3

THE INFLUENCE OF THE LIPOPHILIC AND ELECTRONIC CHARACTER OF SUBSTITUTED PHENOLS ON THE EFFECTORS OF ASCARIS SUUM MITOCHONDRIA

Tollenaere, J. P. (1972) · Elsevier eBooks · doi:10.1016/b978-0-12-711050-9.50037-3 · Cited by 3

Die Eiweißbindung einiger Psychopharmaka mit tricyclischem Ringsystem in Abhängigkeit von ihrer chemischen Konstitution

Glasser, H., & Krieglstein, J. (1969) · Springer eBooks · doi:10.1007/978-3-662-39718-3_331 · Cited by 3

Machine learning–based QSAR for safety evaluation of environmental chemicals

Gao, Y., Zhang, J., Cui, S., Wu, Y., Huang, M., & Zhuang, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00038-2 · Cited by 2

Photodynamic anticancer and antibacterial properties of a series of Sn(IV) tetraarylporphyrins

Soy, R. C., Babu, B., Mack, J. P., & Nyokong, T. (2024) · Journal of Porphyrins and Phthalocyanines · doi:10.1142/s1088424624500275 · Cited by 2

QSAR and Machine Learning Predictors

Fernandes, P. O., & Maltarollo, V. G. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_6 · Cited by 2

AI in drug design: evolution or revolution?

Green, D. V. S. (2025) · Emerging Topics in Life Sciences · doi:10.1042/etls20240005 · Cited by 3

Correlating topological indices with physicochemical properties in 15 polycyclic aromatic hydrocarbons

Huang, Z., Zhao, J., Jin, B.-B., Cao, J., Yang, Y., & Li, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-29525-x · Cited by 2

Artificial Intelligence in Ligand-Based Drug Design

Delre, P., Cerchia, C., Falbo, E., & Lavecchia, A. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_5 · Cited by 2

The (r)evolution of chemical space and molecular modeling: a time-resolved perspective

Moral‐Morales, A. D., Feitosa, F. L., Andrade, C. H., & Medina‐Franco, J. L. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00850-1 · Cited by 2

Explainable Artificial Intelligence in Drug Discovery

Geevarghese, A. V. (2024) · Computational intelligence methods and applications · doi:10.1007/978-981-97-3705-5_6 · Cited by 2

Modelling lethality and teratogenicity of zebrafish ( Danio rerio ) due to β-lactam antibiotics employing the QSTR approach

Nath, A., Ojha, P. K., & Roy, K. (2024) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2024.2378797 · Cited by 2

Application of GFA-MLR and G/PLS Techniques in QSAR/QSPR Studies with Application in Medicinal Chemistry and Predictive Toxicology

Roy, P. P., Ray, S., & Roy, K. (2015) · Springer eBooks · doi:10.1007/978-3-319-20883-1_20 · Cited by 2

Enhancing Carbon Acid pKa Prediction by Augmentation of Sparse Experimental Datasets with Accurate AIBL (QM) Derived Values

Plante, J. P., Caine, B. A., & Popelier, P. L. A. (2021) · Molecules · doi:10.3390/molecules26041048 · Cited by 2

Modeling of Log Kow of a Series of PAHs using Computational Chemistry

Mebarki, F., Meneceur, S., & Bouafia, A. (2022) · Asian Journal of Research in Chemistry · doi:10.52711/0974-4150.2022.00078 · Cited by 2

A new set of KNIME nodes implementing the QPhAR algorithm

Kohlbacher, S. M., Ibis, G., Permann, C., Bryant, S. D., Langer, T., & Seidel, T. (2023) · Molecular Informatics · doi:10.1002/minf.202200245 · Cited by 2

APRENDIZADO DE MÁQUINA APLICADO A QSAR

de Menezes, R. P. B., Scotti, L., & Scotti, M. T. (2024) · Química Nova · doi:10.21577/0100-4042.20240024 · Cited by 2

Impact of 2-hydroxypropyl-β-cyclodextrin inclusion complex formation on dopamine receptor-ligand interaction – A case study

Zell, L., Hofer, T. S., Schubert, M., Popoff, A., Holl, A. K., Marschhofer, M., Huber‐Cantonati, P., Temml, V., & Schuster, D. (2024) · Biochemical Pharmacology · doi:10.1016/j.bcp.2024.116340 · Cited by 2

The photodynamic antimicrobial chemotherapy activities of azaBODIPY dyes and their gold nanoparticle conjugates

Magwaza, T. M., Molupe, N., Openda, Y. I., Mack, J. P., & Nyokong, T. (2025) · Photodiagnosis and Photodynamic Therapy · doi:10.1016/j.pdpdt.2025.104781 · Cited by 2

Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling

Lee, K. H., Won, S. J., Oyinloye, P., & Shi, L. (2025) · Advances in neurobiology · doi:10.1007/978-3-031-96364-3_3 · Cited by 2

A relationship between diffusional transport in lipid membranes and a lipophilic-eutectic parameter

Schultz, T. W., & Ando, H. Y. (1986) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(86)80251-x · Cited by 2

Quantitative Structure-Activity Relationship Study of Amino Acid Derivatives as Histone Deacetylase Inhibitors using the Genetic Algorithm – Multiple Linear Regression

Beheshti, A., Pourbasheer, E., Nekoei, M., & Banaei, A. (2012) · Analytical Chemistry Letters · doi:10.1080/22297928.2012.10648250 · Cited by 2

Acute toxicity of binary mixture benzene-ethanol and partition coefficient K(ow) of benzene and ethanol.

Rucki, M., & Tichý, M. (2004) · PubMed · Cited by 2

The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors

Zhao, G. (2011) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/w4vm-f755 · Cited by 2

Computational Structure–Activity Relationship Studies of Epigenetic Target Inhibitors

Méndez‐Lucio, O. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802808-7.00013-7 · Cited by 2

Prediction of Solubility

Chen, Y.-S., Qi, X., & Liu, R. (2018) · doi:10.1201/9781315120492-3 · Cited by 2

Recent advances in deep learning enabled approaches for identification of molecules of therapeutics relevance

Kashyap, K., & Siddiqi, M. I. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-18638-7.00027-x · Cited by 2

SAR Based Computational Models as Decision Making Tools in Bioremediation

Price, N. R., & Chaudhry, Q. (2010) · Plant ecophysiology · doi:10.1007/978-90-481-9852-8_11 · Cited by 2

Recent Applications of Quantitative Structure-Activity Relationships in Drug Design

Deeb, O. (2012) · InTech eBooks · doi:10.5772/38624 · Cited by 2

Composite multi-parameter ranking of real and virtual compounds for design of MC4R agonists: Renaissance of the Free-Wilson methodology

Nilsson, I., & Polla, M. O. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9605-7 · Cited by 2

Hydration of Adamantane Skeleton: Water Assembling around Amantadine and Halo-substituted Adamantanes

Doi, H., & Aida, M. (2013) · Chemistry Letters · doi:10.1246/cl.2013.292 · Cited by 2

面向毒害有机物生态风险评价的(Q)SAR技术: 进展与展望

王, 亚., 于, 海., 陈, 景., 乔, 显., & 李, 雪. (2008) · Scientia Sinica Chimica · doi:10.1360/zb2008-38-6-461 · Cited by 2

How Far Chemistry and Toxicology are Computational Sciences?

Gini, G. C. (2013) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-319-00272-9_2 · Cited by 2

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

Hamzeh‐Mivehroud, M., Sokouti, B., & Dastmalchi, S. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0549-5.ch002 · Cited by 2

MedChemInformatics: An Introduction to Machine Learning for Drug Discovery

Roberts, M. G., & Lawrence, R. (2020) · Royal Society of Chemistry eBooks · doi:10.1039/9781839160233-00037 · Cited by 2

On Extended Topochemical Atom (ETA) Indices for QSPR Studies

Roy, K., & Das, R. N. (2014) · IGI Global eBooks · doi:10.4018/978-1-4666-5125-8.ch037 · Cited by 2

A QSAR model for predicting the acute toxicity of pesticides to Gammarids

Devillers, J. (2003) · Data handling in science and technology · doi:10.1016/s0922-3487(03)23011-2 · Cited by 2

Chemoinformatic and Chemogenomic Approach to ADMET

Martiny, V. Y., Pajeva, I. K., Wiese, M., Davis, A. M., & Miteva, M. A. (2014) · Wiley eBooks · doi:10.1002/9781118783344.ch7 · Cited by 2

Inductive Queries for a Drug Designing Robot Scientist

King, R. D., Schierz, A. C., Clare, A. J., Rowland, J. J., Sparkes, A., Nijssen, S., & Ramon, J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-7738-0_18 · Cited by 2

Enhancement of Ordinal CoMFA by Ridge Logistic Partial Least Squares

Ohgaru, T., Shimizu, R., Okamoto, K., Kawashita, N., Kawase, M., Shirakuni, Y., Nishikiori, R., & Takagi, T. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700444z · Cited by 2

Zur Verteilung von Arylcarbonsäurehydraziden im System n‐Octanol/Wasser

Kramer, C. (1978) · Zeitschrift für Chemie · doi:10.1002/zfch.19780180919 · Cited by 2

Quantitative Struktur-Aktivitäts-Beziehungen für die Wachstumshemmung von autotrophen Chlorella vulgaris-Suspensionen durch substituierte Aniline and Anilinhydrochloride

Kramer, C. (1989) · Biochemie und Physiologie der Pflanzen · doi:10.1016/s0015-3796(89)80047-2 · Cited by 2

A Simple Model‐Based Function for Description of Non‐Linear Bioactivity‐Hydrophobicity Profiles

Baláž, Š., Wiese, M., & Seydel, J. K. (1992) · Quantitative Structure-Activity Relationships · doi:10.1002/qsar.19920110108 · Cited by 2

CHEMICAL ATTENUATION OF BACTERIAL VIRULENCE: SMALL MOLECULE INHIBITORS OF TYPE III SECRETION

Kauppi, A. M. (2006) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 2

AIR - WATER PARTITIONING OF VOLATILE ORGANIC COMPOUNDS AND GREENHOUSE GASES IN THE PRESENCE OF SALTS

Falabella, J. B. (2007) · SMARTech Repository (Georgia Institute of Technology) · Cited by 2

An Introduction to Chemometrics and Cheminformatics

Nantasenamat, C. (2021) · Wiley eBooks · doi:10.1002/9781119681397.ch4 · Cited by 2

Machine Learning Approaches in Computational Toxicology Studies

Ambure, P. S., Barigye, S. J., & Gozalbes, R. (2021) · Wiley eBooks · doi:10.1002/9781119681397.ch7 · Cited by 2

Drug Design, Molecular Descriptors in

Balaban, A. T. (2014) · Springer eBooks · doi:10.1007/978-3-642-27737-5_136-2 · Cited by 2

Site‐Identification by Ligand Competitive Saturation as a Paradigm of Co‐solvent MD Methods

Orr, A. A., & MacKerell, A. D. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch5 · Cited by 2

Improved machine learning models for predicting selective compounds

Ning, X., Walters, M. A., & Karypis, G. (2011) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2147805.2147817 · Cited by 1

In Silico and Biophysical Techniques in Anticancer Drug Discovery Research

Ghazi, A., & Yap, B. K. (2024) · Interdisciplinary cancer research · doi:10.1007/16833_2024_235 · Cited by 1

The use of computational biology in protein engineering and drug discovery

Putignano, G., Marino, N., Bischof, E. Y., Zhavoronkov, A. A., & Vanhaelen, Q. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13681-8.00001-1 · Cited by 1

Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations

Keto, A. B., & Krenske, E. H. (2025) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.5c01229 · Cited by 1

Novel Psychoactive Substances: Slaying the Dragon With Artificial Intelligence

Wishart, D. S., Prashanthi, K., & López‐Hernández, Y. (2025) · Therapeutic Drug Monitoring · doi:10.1097/ftd.0000000000001429 · Cited by 1

Empirical and physics-based calculations of physical-chemical properties

Ratkova, E. L., Abramov, Y. A., Baskin, I. I., Livingstone, D. J., Xia, J., & Tetko, I. V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00058-3 · Cited by 1

Statistical QSAR Modeling for Future Drug Design and Discovery

Sheikhpour, R., & Gharaghani, S. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_9 · Cited by 1

Transformation of QSAR by AI and ML

Gomatam, A., Khan, A. A., Coutinho, K., Maheshwari, S., Adani, V., Martis, E. A. F., & Coutinho, E. C. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_25 · Cited by 1

Deep Learning for In Silico ADMET Prediction

Lavecchia, A. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_8 · Cited by 1

Enhancing Graph Neural Networks for Multi-target Activity Prediction Through Multi-task Learning and Knowledge Distillation

Cerveira, A., Kremer, F. S., Gomes, G., & Corrêa, U. B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15987-8_31 · Cited by 1

RGGE-DTD: A Unified Model for Simultaneous Prediction of Drug-Target Interactions and Drug-Disease Associations in Drug Repositioning

Song, J., Yang, Z., Song, Z., Heng, Y., Ge, L., Zhang, J., & Chen, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132319 · Cited by 1

Design and Characterization of Topological Small RNAs

Hassall, J., MacDonald, P. R., Cordero, T., Rostain, W., & Jaramillo, A. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2730-2_13 · Cited by 1

Taxane Analogues against Lung Cancer: A Quantitative Structure–Activity Relationship Study

Verma, R. P., & Hansch, C. H. (2009) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2009.00816.x · Cited by 1

Philip S. Magee: a life in QSAR

Charton, M. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9153-8 · Cited by 1

QSAR/QSPR Model Research of Complicated Samples

Li, Y., & Ying, X. (2013) · Advanced materials research · doi:10.4028/www.scientific.net/amr.740.306 · Cited by 1

Cluster Analysis and QSAR Study of Some Anti‐hepatitis B Virus Agents Comprising 4‐Aryl‐6‐chloro‐quinolin‐2‐ones and 5‐Aryl‐7‐chloro‐1,4‐benzodiazepines

Li, Z., Chen, K., Xie, H., Wang, Y., & Dong, F. (2009) · Chinese Journal of Chemistry · doi:10.1002/cjoc.201090007 · Cited by 1

Molecular Modeling: Considerations for the Design of Pharmaceuticals and Biopharmaceuticals

Mosier, P. D., & Kellogg, G. E. (2008) · Humana Press eBooks · doi:10.1007/978-1-59745-532-9_13 · Cited by 1

The Birth of QSAR—In Memory of Professor Corwin Hansch

Fujita, T. (2012) · Journal of Pesticide Science · doi:10.1584/jpestics.w12-09 · Cited by 1

Embedding Toxicology into the Chemistry Curriculum

Anastas, N. D. (2015) · Royal Society of Chemistry eBooks · doi:10.1039/9781782621942-00137 · Cited by 1

QSAR model development for studying carbonic anhydrase inhibitors as anticonvulsant agents

Ajeet, A., Mishra, A. K., & Kumar, A. (2016) · Medicinal Chemistry Research · doi:10.1007/s00044-016-1617-8 · Cited by 1

Introductory Chapter: Some Quantitative Structure Activity Relationship Descriptor

Kandemi̇rlı, F. (2017) · InTech eBooks · doi:10.5772/intechopen.69642 · Cited by 1

In-silico approach in risk assessment of nutraceutical properties

Nushusna, S., H., Y., & Hamzah, M. S. (2020) · Food Research · doi:10.26656/fr.2017.4(s1).s35 · Cited by 1

From chemical documentation to chemoinformatics: 50 years of chemical information science

Willett, P. (2018) · Facet eBooks · doi:10.29085/9781856049986.010 · Cited by 1

Structure-related relationship: Plant-derived antidiabetic compounds

Gupta, M. K., Gouda, G., Sultana, S., Punekar, S. M., Vadde, R., & Ravikiran, T. (2023) · Studies in natural products chemistry · doi:10.1016/b978-0-323-91294-5.00008-7 · Cited by 2

Clustering of atoms relative to vector space in the Z-matrix coordinate system and ‘graphical fingerprint’ analysis of 3D pharmacophore structure

Kızılcan, D. Ş., Güzel, Y., & Türkmenoğlu, B. (2024) · Molecular Diversity · doi:10.1007/s11030-023-10798-1 · Cited by 1

Enzyme inhibitors

Bouabdallah, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16013-4.00013-0 · Cited by 1

Photodynamic antibacterial chemotherapy activities of P(V) and Ga(III) triarylcorroles and their silver nanoparticle conjugates

Soy, R. C., Macigane, P. R., Mack, J. P., & Nyokong, T. (2024) · Journal of Porphyrins and Phthalocyanines · doi:10.1142/s1088424624500408 · Cited by 1

A new computational cross-structure-activity relationship (C-SAR) approach applies to a selective HDAC6 inhibitor dataset for accelerated structure development

Kassab, S. E. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110169 · Cited by 1

Quantitative Structure–Cytotoxicity Relationship of Bioactive Heterocycles by the Semi-empirical Molecular Orbital Method with the Concept of Absolute Hardness

Ishihara, M., Sakagami, H., Kawase, M., & Motohashi, N. (2008) · Topics in heterocyclic chemistry · doi:10.1007/7081_2007_100 · Cited by 1

Three-dimensional vector of pharmacophore atomic interaction and its applications to stereoisomeric description

Chen, G.-H., Xia, Z., & Lü, Y. (2010) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2010.04.007 · Cited by 1

Poszukiwanie modelu wody opisujacego hydratacje biologicznych makromolekul

Poznański, J. T. (2006) · Cited by 1

Chemometric modeling to predict aquatic toxicity of benzene derivatives in Pimephales Promelas

Ziani, N., Amirat, K., & Messadi, D. (2016) · Management of Environmental Quality An International Journal · doi:10.1108/meq-05-2015-0082 · Cited by 1

Application of image moments in MIA‐QSAR

Xu, M. H., Wang, X., Li, B. Q., Zhai, H. L., & Lu, S. H. (2017) · Journal of Chemometrics · doi:10.1002/cem.2958 · Cited by 1

Implementation of multilinear regression in inhibition of thyroid hormone receptor-coactivator

Devi, A. S., Sarwinda, D., & Hertono, G. F. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/974/1/012005 · Cited by 1

QSAR Modeling Using Quantum Chemical Descriptors of Benzimidazole Analogues With Antiparasitic Properties

Jardínez-Vera, A. C., & Medina‐Franco, J. L. (2018) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.2018070105 · Cited by 1

Practical Aspects of Machine Learning for the Design-Synthesis-Purify-Assay Workflow

Sirockin, F., & Stiefl, N. (2018) · CHIMIA International Journal for Chemistry · doi:10.2533/chimia.2018.648 · Cited by 1

Virtual Screening Methods Based on Bayesian Statistics

Vogt, M., & Bajorath, J. (2010) · IGI Global eBooks · doi:10.4018/978-1-61520-911-8.ch010 · Cited by 1

Application of Computational Methods for Creating and Selecting Polymeric Compositions with Given Properties

Derbisher, E. V., & Дербишер, В. Е. (2019) · Mathematical Physics and Computer Simulation · doi:10.15688/mpcm.jvolsu.2019.1.4 · Cited by 1

Machine Learning Algorithms for QSPR/QSAR Predictive Model Development Involving High-Dimensional Data

Datta, S. (2019) · Cited by 1

Biological Activity, Physical Properties, and Toxicity

Pal, R., & Chattaraj, P. K. (2021) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.2021070102 · Cited by 1

Analysis of floor stability in underground coal mines

Santos, C. A. (1988) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 1

Prediction of Solubility

Chen, Y.-S., Qi, X., & Liu, R. (2008) · CRC Press eBooks · doi:10.1201/9781420009552.ch3 · Cited by 1

Overview and Recent Advances in QSAR Studies

Gangwal, R. P., Damre, M. V., & Sangamwar, A. T. (2016) · Apple Academic Press eBooks · doi:10.1201/b19853-2 · Cited by 1

A Non-Bornian Approach to the Standard Gibbs Energy of Ion Transfer at the Oil | Water Interface

Osakai, T. (2022) · Review of Polarography · doi:10.5189/revpolarography.68.3 · Cited by 1

Toxicological Profile of Nutraceutical Supplements

Ashique, S., Kumar, S., Rihan, M., & Garg, A. K. (2024) · Springer eBooks · doi:10.1007/978-981-99-5575-6_32 · Cited by 1

Combinatorial Chemistry Library Design

Klein, R., & Lindell, S. D. (2013) · Wiley eBooks · doi:10.1002/9781118742921.ch2.2 · Cited by 1

Mechanism of Drug Action: Basic Concepts

Bodor, N. S., & Buchwald, P. (2012) · Wiley eBooks · doi:10.1002/9781118407738.ch2 · Cited by 1

"In silico" prediction of drug transport across physiological barriers

Suenderhauf, C. (2011) · edoc (University of Basel) · doi:10.5451/unibas-005676976 · Cited by 1

Growth-regulating Activity of Certain 2:6-Substituted Phenoxyacetic Acids

Osborne, D. J., BLACKMAN, G. E., Powell, R. G., SUDZUKI, F., & Novoa, S. (1954) · Nature · doi:10.1038/174742a0 · Cited by 11

Structure-activity correlations for phenoxyacetic acids and indoleacetic acids used for plant growth regulation

Block, A. M., & Clements, R. G. (2009) · International Journal of Quantum Chemistry · doi:10.1002/qua.560090719 · Cited by 3

Auxin structure/activity relationships: Benzoic acids and phenols

Farrimond, J. A., Elliott, M. C., & Clack, D. W. (1980) · Phytochemistry · doi:10.1016/0031-9422(80)83183-9 · Cited by 11

Studies on plant growth‐regulating substances. XXX. The plant growth‐regulating activity of substituted phenols

Harper, D. B., & Wain, R. L. (1969) · Annals of Applied Biology · doi:10.1111/j.1744-7348.1969.tb02889.x · Cited by 15

Structure-Activity Relationship in the Auxin Activity of Mono-Substituted Phenylacetic Acids

Muir, R. M., Fujita, T., & Hansch, C. H. (1967) · PLANT PHYSIOLOGY · doi:10.1104/pp.42.11.1519 · Cited by 39