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Subject : qsar

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Academic Journal

The efficiency of ligand–receptor interaction information alone as new descriptors in QSAR modeling via random forest artificial neural network.

Authors : Mozafari, Zeinab¹ (AUTHOR); Arab Chamjangali, Mansour¹ (AUTHOR) ; Beglari, Mozhgan¹ (AUTHOR)

Source: Chemical Biology & Drug Design. Aug2020, Vol. 96 Issue 2, p812-824. 13p.

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Academic Journal

Predicting subtype selectivity of dopamine receptor ligands with three-dimensional biologically relevant spectrum.

Authors : Kuang, Zheng ‐ Kun^1,2; Feng, Shi ‐ Yu²; Hu, Ben^1,2

Source: Chemical Biology & Drug Design. Dec2016, Vol. 88 Issue 6, p859-872. 14p.

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Academic Journal

Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

Authors : Matsuzaka, Yasunari¹ (AUTHOR) ; Uesawa, Yoshihiro¹ (AUTHOR)

Source: International Journal of Molecular Sciences. Oct2019, Vol. 20 Issue 19, p4855. 1p.

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Academic Journal

ADis-QSAR: a machine learning model based on biological activity differences of compounds.

Authors : Park, Gyoung Jin¹ (AUTHOR); Kang, Nam Sook¹ (AUTHOR)

Source: Journal of Computer-Aided Molecular Design. Sep2023, Vol. 37 Issue 9, p435-451. 17p.

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Academic Journal

Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach.

Authors : Banerjee, Arkaprava¹ (AUTHOR); Kar, Supratik² (AUTHOR); Pore, Souvik¹ (AUTHOR)

Source: Nanotoxicology. Feb2023, Vol. 17 Issue 1, p78-93. 16p.

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Academic Journal

2D QSAR STUDIES OF NOVEL TRIAZOLINONE DERIVATIVES AS ANGIOTENSIN II ANTAGONISTS USING TOPOLOGICAL DESCRIPTORS.

Authors : Sharma, Mukesh C.¹; Kohli, D. V.²

Source: Indian Drugs. May2022, Vol. 59 Issue 5, p17-23. 7p.

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Academic Journal

Comparative study of machine learning approaches for classification and prediction of selective caspase-3 antagonist for Zika virus drugs.

Authors : Ray, Upasana¹ (AUTHOR) ; Chouhan, Usha¹ (AUTHOR); Verma, Neha¹ (AUTHOR)

Source: Neural Computing & Applications. Aug2020, Vol. 32 Issue 15, p11311-11328. 18p.

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Academic Journal

Predictive QSAR models development and validation for human ether-a-go-go related gene (hERG) blockers using newer tools.

Authors : Moorthy, N. S. Hari Narayana¹ (AUTHOR) ; Ramos, Maria J.¹ (AUTHOR); Fernandes, Pedro A.¹ (AUTHOR)

Source: Journal of Enzyme Inhibition & Medicinal Chemistry. Jun2014, Vol. 29 Issue 3, p317-324. 8p.

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Academic Journal

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.

Authors : Czub, Natalia¹ (AUTHOR) ; Pacławski, Adam¹ (AUTHOR) ; Szlęk, Jakub¹ (AUTHOR)

Source: Pharmaceutics. Oct2021, Vol. 13 Issue 10, p1711-1711. 1p.

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Academic Journal

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

Authors : Ancuceanu, Robert¹ (AUTHOR) ; Hovanet, Marilena Viorica¹ (AUTHOR) ; Anghel, Adriana Iuliana¹ (AUTHOR)

Source: International Journal of Molecular Sciences. Mar2020, Vol. 21 Issue 6, p2114. 1p.

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نتائج البحث

فلترة النتائج

The efficiency of ligand–receptor interaction information alone as new descriptors in QSAR modeling via random forest artificial neural network.

Predicting subtype selectivity of dopamine receptor ligands with three-dimensional biologically relevant spectrum.

Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

ADis-QSAR: a machine learning model based on biological activity differences of compounds.

Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach.

2D QSAR STUDIES OF NOVEL TRIAZOLINONE DERIVATIVES AS ANGIOTENSIN II ANTAGONISTS USING TOPOLOGICAL DESCRIPTORS.

Comparative study of machine learning approaches for classification and prediction of selective caspase-3 antagonist for Zika virus drugs.

Predictive QSAR models development and validation for human ether-a-go-go related gene (hERG) blockers using newer tools.

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

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