Exploring Aromatase Inhibitors to Breast Cancer with Machine Learning Approach Based Quantitative Structure-Activity Relationships Model Technique

Authors: Pawar, Sahebrao B., Deshmukh, N.K.

Source: 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA) Computing, Communication, Control And Automation (ICCUBEA), 2023 7th International Conference On. :1-8 Aug, 2023

Relation: 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA)

Fluorescence sensor array based on covalent organic frameworks and QSAR study for identification of organic pesticides

Authors: An, Fangxia ^a, Li, Fang ^{a, b}, Deng, Shengyuan ^c, Wan, Ying ^d, Han, Haikang ^a, Luo, Jun ^a, Zhu, Longyi ^{a, ⁎}, Qian, Hua ^{a, ⁎}

Source: In Microchemical Journal March 2025 210

Workflow for predictive risk assessments of UVCBs: cheminformatics library design, QSAR, and read-across approaches applied to complex mixtures of metal naphthenates

Authors: A. J. Prussia, C. Welsh, T. S. Somers, P. Ruiz

Source: Frontiers in Toxicology, Vol 6 (2024)

Subject Terms: unknown or variable composition complex reaction products and biological materials (UVCBs), metal naphthenates, quantitative structure–activity relationships, quantitative structure–property relationships, read-across, virtual chemical library, Toxicology. Poisons, RA1190-1270

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/ftox.2024.1452838/full; https://doaj.org/toc/2673-3080

Access URL: https://doaj.org/article/c2e5a8fbb19f4212b518fa95b900f024

Exploring the reproductive exposure risks of phthalates and organophosphates in atmospheric particulate matter based on quantitative structure-activity relationships and network toxicology models

Authors: Gao, Ke ^{a, b, ⁎}, Hua, Kai ^a, Wang, Shiyuan ^a, Chen, Xi ^{b, c}, Zhu, Tong ^{b, ⁎⁎}

Source: In Journal of Hazardous Materials 5 May 2025 488

Combining multilevel calculation protocol and QSAR model to predict the activity of phenolic antioxidants suitable for high-energy-density fuels at different temperatures

Authors: Yuan, Zhiyuan ^a, Guo, Yongsheng ^{a, b, ⁎⁎}, Fang, Wenjun ^{a, b, ⁎}

Source: In Energy 1 April 2025 320

Predicting mito-target interactions for per-and poly-fluoroalkyl compounds: Mapping mitochondrial toxicity on zebrafish voltage-dependent anion channel 2

Authors: González-Durruthy, Michael, Halder, Amit K., Moura, Ana S., Cordeiro, M. Natalia D.S. ^⁎

Source: In Aquatic Toxicology April 2025 281

First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health

Authors: Kumar, Ankur ^a, Ojha, Probir Kumar ^{a, ⁎}, Roy, Kunal ^{b, ⁎}

Source: In Science of the Total Environment 1 December 2024 954

OntoQSAR: an Ontology for Interpreting Chemical and Biological Data in Quantitative Structure-Activity Relationship Studies

Authors: Angelo, Rafaela M., Io, Andreia K., Almeida, Matheus P., Silveira, Rafael G., Oliveira, Patricia R., Alcazar, Jose J. P., Honorio, Kathia M., Bettanin, Fernanda

Source: 2020 IEEE 14th International Conference on Semantic Computing (ICSC) Semantic Computing (ICSC, 2020 IEEE 14th International Conference on. :203-206 Feb, 2020

Relation: 2020 IEEE 14th International Conference on Semantic Computing (ICSC)

Machine learning algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery

Authors: Stitou, Mourad, Toufik, Hamid, Bouachrine, Mohammed, Bih, Hssain, Lamchouri, Fatima

Source: 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS) Intelligent Systems and Advanced Computing Sciences (ISACS), 2019 International Conference on. :1-8 Dec, 2019

Relation: 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)

Benchmarking multi-task learning in predictive models for drug discovery

Authors: McCabe, Philippa Grace, Ortega-Martorell, Sandra, Olier, Ivan

Source: 2019 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2019 International Joint Conference on. :1-7 Jul, 2019

Relation: 2019 International Joint Conference on Neural Networks (IJCNN)

AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism

Authors: Huang, Zhi ^a, Yu, Jiang ^{a, b, c, ⁎}, He, Wei ^d, Yu, Jie ^{a, b}, Deng, Siwei ^a, Yang, Chun ^e, Zhu, Weiwei ^a, Shao, Xiao ^f

Source: In Journal of Hazardous Materials 5 March 2024 465

Merging cultures and disciplines to create a drug discovery ecosystem at Virginia commonwealth university: Medicinal chemistry, structural biology, molecular and behavioral pharmacology and computational chemistry

Authors: Glen E. Kellogg, Yana Cen, Malgorzata Dukat, Keith C. Ellis, Youzhong Guo, Jiong Li, Aaron E. May, Martin K. Safo, Shijun Zhang, Yan Zhang, Umesh R. Desai

Source: SLAS Discovery, Vol 28, Iss 6, Pp 255-269 (2023)

Subject Terms: Drug discovery ecosystem, Structure-based drug discovery, Quantitative structure-activity relationships, Computational glycomics, Drug Discrimination, Allosteric effectors of hemoglobin, Medicine (General), R5-920, Biotechnology, TP248.13-248.65

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2472555223000175; https://doaj.org/toc/2472-5552

Access URL: https://doaj.org/article/96b02476711041fea6a676aac62c3785

Developing Robust Human Liver Microsomal Stability Prediction Models: Leveraging Inter-Species Correlation with Rat Data

Authors: Pranav Shah, Vishal B. Siramshetty, Ewy Mathé, Xin Xu

Source: Pharmaceutics, Vol 16, Iss 10, p 1257 (2024)

Subject Terms: metabolic stability, human liver microsomes, rat liver microsomes, quantitative structure activity relationships, in silico ADME, Pharmacy and materia medica, RS1-441

File Description: electronic resource

Relation: https://www.mdpi.com/1999-4923/16/10/1257; https://doaj.org/toc/1999-4923

Access URL: https://doaj.org/article/584a938732b645b5adf4af3147c4f2f2

Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent

Authors: Isman Kurniawan, Nabilla Kamil, Annisa Aditsania, Erwin Budi Setiawan

Source: JOIN: Jurnal Online Informatika, Vol 8, Iss 1, Pp 81-90 (2023)

Subject Terms: ant colony optimization, artificial neural network, cancer, indenopyrazole, quantitative structure-activity relationships, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://join.if.uinsgd.ac.id/index.php/join/article/view/1055; https://doaj.org/toc/2528-1682; https://doaj.org/toc/2527-9165

Access URL: https://doaj.org/article/94fcfbc751ce4b70ab1c3a6c39947fba

Internal validation parameters of linear regression equations in QSAR problem

Authors: Inna Khristenko, Volodymyr Ivanov

Source: Вісник Харківського національного університету: Серія xімія, Iss 40, Pp 12-21 (2023)

Subject Terms: quantitative structure-activity relationships (qsar), regression models, internal validation, topological descriptors, Chemistry, QD1-999

File Description: electronic resource

Relation: https://periodicals.karazin.ua/chemistry/article/view/23245; https://doaj.org/toc/2220-637X; https://doaj.org/toc/2220-6396

Access URL: https://doaj.org/article/559796a2220a4aff850597c0e9845505

A fingerprints based molecular property prediction method using the BERT model

Authors: Naifeng Wen, Guanqun Liu, Jie Zhang, Rubo Zhang, Yating Fu, Xu Han

Source: Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-13 (2022)

Subject Terms: Molecular property prediction, Molecular representation, Pre-training language model, Deep neural network, Quantitative structure-activity relationships, Information technology, T58.5-58.64, Chemistry, QD1-999

File Description: electronic resource

Relation: https://doaj.org/toc/1758-2946

Access URL: https://doaj.org/article/00d73938472b4809924a25932179a43f

Deriving the predicted no effect concentrations of 35 pesticides by the QSAR-SSD method

Authors: Huang, Peng ^{a, c}, Liu, Shu-Shen ^{a, b, c, ∗}, Wang, Ze-Jun ^{a, b}, Ding, Ting-Ting ^{a, c}, Xu, Ya-Qian ^{a, b}

Source: In Chemosphere July 2022 298

Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants

Authors: Ying Zhang, Liangxu Xie, Dawei Zhang, Xiaojun Xu, Lei Xu

Source: Molecules, Vol 28, Iss 22, p 7457 (2023)

Subject Terms: persistent organic pollutants, air half-life, quantitative structure–activity relationships, molecular descriptors, machine learning, Organic chemistry, QD241-441

File Description: electronic resource

Relation: https://www.mdpi.com/1420-3049/28/22/7457; https://doaj.org/toc/1420-3049

Access URL: https://doaj.org/article/2d5b40649fe04e45b1fe56ceb94a68ab

Symbolic regression for the interpretation of quantitative structure-property relationships

Authors: Katsushi Takaki, Tomoyuki Miyao

Source: Artificial Intelligence in the Life Sciences, Vol 2, Iss , Pp 100046- (2022)

Subject Terms: Model interpretability, Quantitative structure-activity relationships, Quantitative structure-property relationships, Symbolic regression, Genetic programming, Science (General), Q1-390

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2667318522000162; https://doaj.org/toc/2667-3185

Access URL: https://doaj.org/article/48027fd855b44d00bd5d9ff2260b54fd

Predicting Reduction Rates of Energetic Nitroaromatic Compounds Using Calculated One-Electron Reduction Potentials

Contributors: Tratnyek, Paul

Source: Environmental Science & Technology, 49(6):3778-3786

File Description: Medium: ED

Access URL: http://www.osti.gov/scitech/servlets/purl/1184952