Compound Screening and Prioritization in Drug Discovery Using Machine Learning

Authors: Mariyam, A. Asiya, Varshni, R. R., Samyuktha, S. P., Kumar, V. Anush, Narmadha, B.

Source: 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) Electrical, Electronics and Computer Science (SCEECS), 2024 IEEE International Students' Conference on. :1-11 Feb, 2024

Relation: 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

The Insulation Strength Structure-Property Relationship Model of Gas Containing Double or Triple Bonds

Authors: Zhao, Q., Tong, D., Tu, Y., Zheng, Z., Qiu, X., Wang, C.

Source: IEEE Transactions on Dielectrics and Electrical Insulation IEEE Trans. Dielect. Electr. Insul. Dielectrics and Electrical Insulation, IEEE Transactions on. 30(1):49-55 Feb, 2023

The q-RASPR approach for predicting the property and fate of persistent organic pollutants

Authors: Chia Ming Chang, Arkaprava Banerjee, Vinay Kumar, Kunal Roy, Emilio Benfenati

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Subject Terms: Quantitative structure-property relationship (QSPR), Read-across (RA), Quantitative Read-Across structure-property relationship (q-RASPR), Persistent Organic pollutants (POPs), Polychlorinated Biphenyls (PCBs), Polybrominated Diphenyl Ethers (PBDEs), Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/3cfe5223e9784045be27eea5fa9f842a

Boiling point enhanced vapor pressure prediction by Antoine-based quantitative structure–property relationship

Authors: Feng, Xiaojie ^a, Xiong, Jialiang ^a, Wang, Qiang ^a, Jia, Qingzhu ^b, Yan, Fangyou ^{a, ⁎}

Source: In Chemical Engineering Science 1 March 2025 306

Comprehensive accurate prediction of critical jet fuel properties with multiple machine learning models

Authors: Shao, Yitong ^{a, b, 1}, Yu, Mengxian ^{a, b, 1}, Zhao, Mengchao ^{a, 1}, Xue, Kang ^{a, b, c, ⁎}, Zhang, Xiangwen ^{a, b, c}, Zou, Ji-Jun ^{a, b, c}, Pan, Lun ^{a, b, c, ⁎}

Source: In Chemical Engineering Science 1 February 2025 304

A full in-silico approach for quantifying the effect of intramolecular hydrogen bonds on physicochemical properties

Authors: Zhou, Yufang ^a, Ruan, Qiyang ^a, Li, Yun ^{a, b}, Fan, Yulan ^{a, ⁎}, Chen, Deliang ^{a, ⁎}

Source: In Journal of Molecular Liquids 1 May 2025 425

Predicting the amino group pKa of amino acids using machine learning-QSPR methods

Authors: Rafieezade, Mohammad-Reza, Fazeli, Ali ^⁎

Source: In Journal of Molecular Liquids 15 August 2024 408

A GNN-Based QSPR Model for Surfactant Properties

Authors: Seokgyun Ham, Xin Wang, Hongwei Zhang, Brian Lattimer, Rui Qiao

Source: Colloids and Interfaces, Vol 8, Iss 6, p 63 (2024)

Subject Terms: molecular property prediction, surfactant, graph neural networks (GNN), quantitative structure–property relationship (QSPR), Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2504-5377/8/6/63; https://doaj.org/toc/2504-5377

Access URL: https://doaj.org/article/ab9bc38263764cbb888085cdc8a76abc

Low-viscosity oligoether esters (OEEs) as high-efficiency lubricating oils: Insight on their structure–lubricity relationship

Authors: Hanwen Wang, Ying Wang, Ping Wen, Lin Ma, Mingjin Fan, Rui Dong, Chunhua Zhang

Source: Friction, Vol 12, Iss 6, Pp 1133-1153 (2023)

Subject Terms: oligoether esters (OEEs), low-viscosity ester, friction, wear, quantitative structure–property relationship (QSPR), Mechanical engineering and machinery, TJ1-1570

File Description: electronic resource

Relation: https://doaj.org/toc/2223-7690; https://doaj.org/toc/2223-7704

Access URL: https://doaj.org/article/4b998374d45141e988c6ba59bdc49cbf

Predictive modeling and regression analysis of diverse sulfonamide compounds employed in cancer therapy

Authors: Muhammad Danish, Tehreem Liaquat, Farwa Ashraf, Shahid Zaman

Source: Frontiers in Chemistry, Vol 12 (2024)

Subject Terms: compounds with anti-cancer/anti-tumor sulfonamides, topological indices(TIs), quantitative structure-property relationship QSPR analysis, and regression models, graph theory, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fchem.2024.1413850/full; https://doaj.org/toc/2296-2646

Access URL: https://doaj.org/article/935e649050744b5e9021c0e2f55b9952

A Review of Machine Learning and QSAR/QSPR Predictions for Complexes of Organic Molecules with Cyclodextrins

Authors: Dariusz Boczar, Katarzyna Michalska

Source: Molecules, Vol 29, Iss 13, p 3159 (2024)

Subject Terms: quantitative structure–activity relationship (QSAR), quantitative structure–property relationship (QSPR), machine learning, cyclodextrin, inclusion complex, Gibbs free energy (ΔG), Organic chemistry, QD241-441

File Description: electronic resource

Relation: https://www.mdpi.com/1420-3049/29/13/3159; https://doaj.org/toc/1420-3049

Access URL: https://doaj.org/article/e7919e4da293467f9d0e91e305d84cf3

Structure–Activity Relationship (SAR) Modeling of Mosquito Repellents: Deciphering the Importance of the 1-Octanol/Water Partition Coefficient on the Prediction Results

Authors: James Devillers, Hugo Devillers

Source: Applied Sciences, Vol 14, Iss 13, p 5366 (2024)

Subject Terms: Aedes aegypti, physicochemical descriptor, Quantitative Structure–Property Relationship (QSPR), artificial neural network, classification model, vector control, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/13/5366; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/bf9e9bb63cde4cc7abd55820b12765dc

Selective adsorption of antibiotics on manganese oxide-loaded biochar and mechanism based on quantitative structure–property relationship model

Authors: Liao, Xinyi ^{a, 1}, Chen, Chen ^{a, 1}, Liang, Zhijie ^{a, b, ⁎}, Zhao, Zhiwei ^{a, b}, Cui, Fuyi ^{a, b}

Source: In Bioresource Technology January 2023 367

Molecular graph-based deep learning method for predicting multiple physical properties of alternative fuel components

Authors: Liu, Jinpeng ^a, Gong, Siyuan ^a, Li, Hongwei ^a, Liu, Guozhu ^{a, b, c, ⁎}

Source: In Fuel 1 April 2022 313

QSPR study for the prediction of UV maximum absorption wavelength of coumarins by heuristic method and radial basis function neural network

Authors: Han, Ping, Liu, Huitao, Wen, Yingying, Mu, Guangfen, Gao, Yuan, Luan, Feng

Source: 2010 Sixth International Conference on Natural Computation Natural Computation (ICNC), 2010 Sixth International Conference on. 4:1778-1782 Aug, 2010

Relation: 2010 Sixth International Conference on Natural Computation (ICNC)

Quantitative structure-property relationship for melting and freezing points of deep eutectic solvents

Authors: Khajeh, Aboozar ^{a, ⁎}, Shakourian-Fard, Mehdi ^a, Parvaneh, Khalil ^b

Source: In Journal of Molecular Liquids 1 January 2021 321

An updated database of human maximum skin fluxes and epidermal permeability coefficients for drugs, xenobiotics, and other solutes applied as aqueous solutions

Authors: Hanumanth Srikanth Cheruvu, Xin Liu, Jeffrey E Grice, Michael S. Roberts

Source: Data in Brief, Vol 42, Iss , Pp 108242- (2022)

Subject Terms: Percutaneous absorption, maximum flux, epidermal permeability, in vitro human skin penetration test (IVPT), quantitative structure-property relationship (QSPR), xenobiotics, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352340922004449; https://doaj.org/toc/2352-3409

Access URL: https://doaj.org/article/004442ebbd0b423f93f9f322af52cc68

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

Authors: Gabriela Falcón-Cano, Christophe Molina, Miguel Ángel Cabrera-Pérez

Source: Pharmaceutics, Vol 14, Iss 10, p 1998 (2022)

Subject Terms: Caco-2, regression, quantitative structure–property relationship (QSPR), KNIME, permeability, Pharmacy and materia medica, RS1-441

File Description: electronic resource

Relation: https://www.mdpi.com/1999-4923/14/10/1998; https://doaj.org/toc/1999-4923

Access URL: https://doaj.org/article/b711ad1e068c481699cf7bd607d96c22

Prediction of microdialysis relative recovery of flavone derivatives based on molecular descriptors

Authors: Zhan, Shuyu, Huang, Jianping, Shao, Qing, Fan, Xiaohui, Guo, Wenjing

Source: Journal of the Brazilian Chemical Society. November 2012 23(11)

Subject Terms: quantitative structure-property relationship (QSPR), microdialysis, relative recovery, multiple linear regression, support vector machine, flavone derivatives

File Description: text/html

Access URL: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532012001100012

Predicting partition coefficients of migrants in food simulant/polymer systems using adaptive neuro-fuzzy inference system

Authors: Shahbazikhah, Parviz, Asadollahi-Baboli, Mohammad, Khaksar, Ramin, Alamdari, Reza Fareghi, Zare-Shahabadi, Vali

Source: Journal of the Brazilian Chemical Society. August 2011 22(8)

Subject Terms: quantitative structure property relationship (QSPR), adaptive neuro-fuzzy inference system (ANFIS), partition coefficients, additive migration, food safety

File Description: text/html

Access URL: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532011000800007