Results 101 to 110 of about 11,635 (258)

Harary-Albertson index of graphs [PDF]

Contributions to Mathematics, 2021
Zhen Lin
doaj   +1 more source

Polymer informatics: Integrating data‐driven strategies, advanced machine learning, and automated synthesis for next‐generation polymer design

InfoScience, Volume 3, Issue 2, June 2026.
Designing new polymers for applications such as sustainable plastics, biomaterials, and 3D printing has traditionally been slow and expensive, relying heavily on trial‐and‐error experiments. This review shows how polymer informatics—the integration of large polymer databases, machine‐learning models, and automated robotic synthesis—enables fast ...
Md. Saiful Islam, Tamanna Hoque, Junied Islam Shuvo, Sheta Roy Tori, Sadit Bihongo Malitha, Md. Zahangir Alam, A. M. Sarwaruddin Chowdhury   +6 more
wiley   +1 more source

QSPR modeling using Catalan solvent and solute parameters [PDF]

, 2010
The field of quantitative structure-property relationship (QSPR) can greatly benefit from molecular descriptors that particularly represent the intermolecular interactions. Catalan has developed a set of solvatochromic scales for solvents, which could be
Taravat Ghafourian, Shayanfar, Ali, Ghafourian, Taravat, Mohammad A. A. Fakhree, Ali Shayanfar, Ali Shayanfar (1878088), Mohammad A A Fakhree (16204013), Abolghasem Jouyban, Jouyban, Abolghasem, Fakhree, Mohammad A. A., Abolghasem Jouyban (578282), Taravat Ghafourian (4466557)   +11 more
core   +3 more sources

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

Macromolecular Rapid Communications, Volume 47, Issue 12, 22 June 2026.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig, Yannik Köster, Timo Koswig, Punith Raviswamy, Subhash V. S. Ganti, Stefan Zechel, Christopher Kuenneth, Ulrich S. Schubert   +7 more
wiley   +1 more source

QSPR modeling of flash points: An update

Journal of Molecular Graphics and Modelling, 2007
Quantitative structure-property relationship (QSPR) models for the flash points of 758 organic compounds are developed using geometrical, topological, quantum mechanical and electronic descriptors calculated by CODESSA PRO software. Multilinear regression models link the structures to their reported flash point values.
Center for Heterocyclic Compounds, Department of Chemistry, University of Florida, Gainesville, FL 32611, USA ( host institution ), Katritzky, Alan R., Stoyanova-Slavova, Iva B., Dobchev, Dimitar A., Karelson, Mati   +4 more
openaire   +3 more sources

Sarveiskalvon permeabiliteetin ennustaminen QSPR-mallin avulla [PDF]

, 2011
QSPR (Quantitive structure property relationship) describes relationship between descriptors and biological activity. Therefore, QSPR models are useful tools in drug discovery. The literature review summarizes existing corneal, intestinal and blood brain
Tissari, Anita
core  

Topological Descriptors of Colorectal Cancer Drugs and Characterizing Physical Properties Via QSPR Analysis

International Journal of Analytical Chemistry
Topological descriptors and QSPR analysis are statistical techniques that are highly beneficial for analyzing various physical and chemical characteristics of molecular graphs without necessitating expensive and time-consuming laboratory experiments. The
Sumiya Nasir
doaj   +1 more source

QSPR Model for Regulatory Purpose: from Development to Integration into the QSAR Toolbox

Chemical Engineering Transactions, 2016
Quantitative Structure-Property Relationships (QSPR) are predictive methods of macroscopic properties of substances based on their only molecular structures.
Guillaume Fayet, Patricia Rotureau
doaj   +1 more source

Understanding Water Activity in Deep Eutectic Solvents: Experimental Screening, Thermodynamic Modeling, and QSAR Predictions

ChemistrySelect, Volume 11, Issue 21, 4 June 2026.
Water activity (aw) was measured in eight DESs (10–90 wt% water). Strong DES–water interactions persist to ∼50 wt%, after which structure collapses toward aqueous‐like behavior. Redlich–Kister, Wilson, and COSMO‐RS models showed limited performance, whereas QSAR modeling using COSMOtherm‐derived σ‐profile descriptors significantly improved aw ...
Marko Rogošić, Matija Cepanec, Anja Damjanović, Marijan Logarušić, Ana Jurinjak Tušek, Ivana Varenina, Marina Cvjetko Bubalo   +6 more
wiley   +1 more source

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Journal of Cheminformatics, 2020
Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity
Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera, K. Funatsu   +3 more
semanticscholar   +1 more source