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E. Papa, J. P. Doucet, A. Sangion & A. Doucet-Panaye. (2016) Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches. SAR and QSAR in Environmental Research 27:7, pages 521-538.
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Articles from other publishers (31)
Binxu Qiu, Zixiong Shen, Dongliang Yang & Quan Wang. (2023) Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study. Frontiers in Oncology 13.
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Gabriel Corrêa Veríssimo, Jadson de Castro Gertrudes & Vinícius Gonçalves Maltarollo. 2023. Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development. Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development
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Philipe Oliveira Fernandes & Vinicius Gonçalves Maltarollo. (2022) Structure-Activity Relationship Studies of Staphylococcus aureus DNA Gyrase B Inhibitors as Antibacterial Agents Employing Random Forest Models. International Journal of Quantitative Structure-Property Relationships 7:1, pages 1-16.
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Tengyi Zhu & Cuicui Tao. (2022) Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. Journal of Hazardous Materials 423, pages 127037.
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Philipe Oliveira Fernandes, Diego Magno Martins, Aline de Souza Bozzi, João Paulo A. Martins, Adolfo Henrique de Moraes & Vinícius Gonçalves Maltarollo. (2021) Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking. Molecular Diversity 25:3, pages 1301-1314.
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Beilei Yuan, Pengfei Wang, Leqi Sang, Junhui Gong, Yong Pan & Yanhui Hu. (2021) QNAR modeling of cytotoxicity of mixing nano-TiO2 and heavy metals. Ecotoxicology and Environmental Safety 208, pages 111634.
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Kimberley M. Zorn, Daniel H. Foil, Thomas R. Lane, Daniel P. Russo, Wendy Hillwalker, David J. Feifarek, Frank Jones, William D. Klaren, Ashley M. Brinkman & Sean Ekins. (2020) Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction. Environmental Science & Technology 54:19, pages 12202-12213.
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Daniel P. Russo, Kimberley M. Zorn, Alex M. Clark, Hao Zhu & Sean Ekins. (2018) Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction. Molecular Pharmaceutics 15:10, pages 4361-4370.
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Natalia Sizochenko, Victor Kuz’min, Liudmila Ognichenko & Jerzy Leszczynski. (2015) Introduction of simplex-informational descriptors for QSPR analysis of fullerene derivatives. Journal of Mathematical Chemistry 54:3, pages 698-706.
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Oleg A. Raevsky, Daniel E. Polianczyk, Veniamin Yu. Grigorev, Olga E. Raevskaja & John C. Dearden. (2015) In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models. Molecular Informatics 34:6-7, pages 417-430.
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Oleg A. Raevsky, Veniamin Yu. Grigor’ev, Daniel E. Polianczyk, Olga E. Raevskaja & John C. Dearden. (2014) Calculation of Aqueous Solubility of Crystalline Un-Ionized Organic Chemicals and Drugs Based on Structural Similarity and Physicochemical Descriptors. Journal of Chemical Information and Modeling 54:2, pages 683-691.
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Chin Yee Liew & Chun Wei Yap. (2013) QSAR and Predictors of Eye and Skin Effects. Molecular Informatics 32:3, pages 281-290.
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Chin Yee Liew, Chuen Pan, Andre Tan, Ke Xin Magneline Ang & Chun Wei Yap. (2012) QSAR classification of metabolic activation of chemicals into covalently reactive species. Molecular Diversity 16:2, pages 389-400.
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Chin Yee Liew, Yen Ching Lim & Chun Wei Yap. (2011) Mixed learning algorithms and features ensemble in hepatotoxicity prediction. Journal of Computer-Aided Molecular Design 25:9, pages 855-871.
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Melvin J. Yu. (2010) Predicting Total Clearance in Humans from Chemical Structure. Journal of Chemical Information and Modeling 50:7, pages 1284-1295.
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Nels Thorsteinson, Fuqiang Ban, Osvaldo Santos-Filho, Seyed M.H. Tabaei, Solange Miguel-Queralt, Caroline Underhill, Artem Cherkasov & Geoffrey L. Hammond. (2009) In silico identification of anthropogenic chemicals as ligands of zebrafish sex hormone binding globulin. Toxicology and Applied Pharmacology 234:1, pages 47-57.
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Jiazhong Li, Beilei Lei, Huanxiang Liu, Shuyan Li, Xiaojun Yao, Mancang Liu & Paola Gramatica. (2008) QSAR study of malonyl‐CoA decarboxylase inhibitors using GA‐MLR and a new strategy of consensus modeling. Journal of Computational Chemistry 29:16, pages 2636-2647.
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Artem Cherkasov, Fuqiang Ban, Osvaldo Santos-Filho, Nels Thorsteinson, Magid Fallahi & Geoffrey L. Hammond. (2008) An Updated Steroid Benchmark Set and Its Application in the Discovery of Novel Nanomolar Ligands of Sex Hormone-Binding Globulin. Journal of Medicinal Chemistry 51:7, pages 2047-2056.
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Alessandra Roncaglioni & Emilio Benfenati. (2008) In silico-aided prediction of biological properties of chemicals: oestrogen receptor-mediated effects. Chem. Soc. Rev. 37:3, pages 441-450.
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Tadashi Kadowaki, Craig E. Wheelock, Tetsuya Adachi, Taku Kudo, Shinobu Okamoto, Nobuya Tanaka, Koichiro Tonomura, Gozoh Tsujimoto, Hiroshi Mamitsuka, Susumu Goto & Minoru Kanehisa. (2007) Identification of Endocrine Disruptor Biodegradation by Integration of Structure–activity Relationship with Pathway Analysis. Environmental Science & Technology 41:23, pages 7997-8003.
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M. Paul Gleeson, Andrew M. Davis, Kamaldeep K. Chohan, Stuart W. Paine, Scott Boyer, Claire L. Gavaghan, Catrin Hasselgren Arnby, Cecilia Kankkonen & Nan Albertson. (2007) Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models. Journal of Computer-Aided Molecular Design 21:10-11, pages 559-573.
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Samuli‐Petrus Korhonen, Kari Tuppurainen, Arja Asikainen, Reino Laatikainen & Mikael Peräkylä. (2007) SOMFA on Large Diverse Xenoestrogen Dataset: The Effect of Superposition Algorithms and External Regression Tools. QSAR & Combinatorial Science 26:7, pages 809-819.
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Paola Gramatica. (2007) Principles of QSAR models validation: internal and external. QSAR & Combinatorial Science 26:5, pages 694-701.
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Artem Cherkasov, Fuqiang Ban, Yvonne Li, Magid Fallahi & Geoffrey L. Hammond. (2006) Progressive Docking: A Hybrid QSAR/Docking Approach for Accelerating In Silico High Throughput Screening. Journal of Medicinal Chemistry 49:25, pages 7466-7478.
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C.W. Yap, Z.R. Li & Y.Z. Chen. (2006) Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods. Journal of Molecular Graphics and Modelling 24:5, pages 383-395.
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Arja Asikainen, Mikko Kolehmainen, Juhani Ruuskanen & Kari Tuppurainen. (2006) Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods. Chemosphere 62:4, pages 658-673.
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Samuli-Petrus Korhonen, Kari Tuppurainen, Reino Laatikainen & Mikael Peräkylä. (2005) Comparing the Performance of FLUFF-BALL to SEAL-CoMFA with a Large Diverse Estrogen Data Set: From Relevant Superpositions to Solid Predictions. Journal of Chemical Information and Modeling 45:6, pages 1874-1883.
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Artem Cherkasov, Zheng Shi, Yvonne Li, Steven J. M. Jones, Magid Fallahi & Geoffrey L. Hammond. (2005) ‘Inductive' Charges on Atoms in Proteins: Comparative Docking with the Extended Steroid Benchmark Set and Discovery of a Novel SHBG Ligand. Journal of Chemical Information and Modeling 45:6, pages 1842-1853.
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Kamaldeep K. ChohanStuart W. PaineJaina MistryPatrick BartonAndrew M. Davis. (2005) A Rapid Computational Filter for Cytochrome P450 1A2 Inhibition Potential of Compound Libraries. Journal of Medicinal Chemistry 48:16, pages 5154-5161.
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Arja H. Asikainen, Juhani Ruuskanen & Kari A. Tuppurainen. (2004) Consensus kNN QSAR: A Versatile Method for Predicting the Estrogenic Activity of Organic Compounds In Silico. A Comparative Study with Five Estrogen Receptors and a Large, Diverse Set of Ligands. Environmental Science & Technology 38:24, pages 6724-6729.
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