Results 91 to 100 of about 25,271,406 (229)

Toward Integrative Predictive Toxicology: Advanced Methods for Drug Toxicity and Safety Prediction

WIREs Computational Molecular Science, Volume 16, Issue 1, January/February 2026.
This figure summarizes the different methods in integrative predictive toxicology reviewed in this work, ranging from exploration of molecular mechanisms to in silico pharmacokinetic modeling and construction of adverse outcome pathway (AOP) networks based on key events (KEs) and key event relationships (KERs). Further integration with multi‐omics data
Eleonora Gianquinto, Matteo Bersani, Lucrezia Armando, Lara Davani, Clara Cena, Angela De Simone, Francesca Spyrakis   +6 more
wiley   +1 more source

Combined machine learning approaches to predict the thermal conductivity of liquid mixtures

Science and Technology for Energy Transition
The application of Machine Learning (ML)-based techniques was explored to create a fully predictive framework for estimating the thermal conductivity of multi-component mixtures containing hydrocarbons and oxygenated compounds.
Le Trung T., Nieto-Draghi Carlos, Lachet Véronique, Sofos Filippos D., Karakasidis Theodoros E., Creton Benoit   +5 more
doaj   +1 more source

Exploring the chemical-functional space of cell-penetrating peptides [PDF]

, 2012
Cell penetrating peptides (CPPs) are an increasingly growing part of fundamental and applied peptide research. Using their capacity to cross cell barriers, they have already been successfully applied as carriers for problematic cargos like DNA, (si)RNA ...
De Spiegeleer, Bart, Stalmans, Sofie, Wynendaele, Evelien   +2 more
core  

QSPR Analysis of Some Alzheimer’s Compounds via Topological Indices and Regression Models

Journal of chemistry
Neurodegenerative diseases (NDDs) have received considerable interest from scientists for a long time due to their multifactorial nature. Alzheimer’s disease (AD) is of particular importance among pathologies, and despite approved drugs for its treatment,
M. S. Sardar, K. H. Hakami
semanticscholar   +1 more source

QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool

Journal of Cheminformatics
Building reliable and robust quantitative structure–property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated.
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen, Linde Schoenmaker, Michiel Jespers, Olivier J. M. Béquignon, Marina Gorostiola González, Remco L. van den Broek, Andrius Bernatavicius, J. G. Coen van Hasselt, Piet. H. van der Graaf, Gerard J. P. van Westen   +12 more
doaj   +1 more source

QSPR analysis of air-to-blood distribution of volatile organic compounds

Ecotoxicology and Environmental Safety, 2008
Quantitative structure property relationship (QSPR) models for the prediction of human blood:air partition coefficient (log K(blood)) of volatile organic compounds (VOCs) has been developed based on the linear heuristic method (HM) and non-linear radial basis function neural networks (RBFNNs).
F, Luan, H T, Liu, W P, Ma, B T, Fan
openaire   +2 more sources

Some Hypotheses on Commonality in Liquidity: New Evidence from the Chinese Stock Market [PDF]

In this paper, we examine four specific hypotheses relating to commonality in liquidity on the Chinese stock markets. These hypotheses are: (a) that market-wide liquidity determines liquidity of individual stocks; (b) that liquidity varies with firm size;
Paresh Kumar Narayan, Xinwei Zheng, Zhichao Zhang   +2 more
core  

Prediction of properties of boron $$\alpha$$ α -icosahedral nanosheet by bond-addictive $${\mathbb {M}}$$ M -polynomial

Scientific Reports
Nanosheets with boron elements have excellent characteristics which makes the boron polymorphs unique and super hard. A boron $$\alpha$$ α -icosahedral nanosheet in crystalline form has superconductivity and thermal electronic properties.
D. Antony Xavier, K. Julietraja, Ammar Alsinai, S. Akhila   +3 more
doaj   +1 more source

Molecular modelling and design of lubricant additives and their molecular dynamic simulations studies of Diamond-Like-Carbon (DLC) and steel surface coating

Egyptian Journal of Petroleum, 2019
Quantitative Structure–Properties Relationship (QSPR) analysis was carried out on 30 lubricant additives while molecular dynamics simulations study was also performed to determine the dynamic binding strength between the hydrogen-containing DLC (a-C: H ...
Usman Abdulfatai, Adamu Uzairu, Sani Uba, Gideon Adamu Shallangwa   +3 more
doaj   +1 more source

Análisis QSPR de índices de retención de aromas medidos en cromatografía de gases [PDF]

, 2014
Las relación cuantitativa estructura-retención (QSRR) es muy útil para la predicción de los índices de retención (RI). En el presente trabajo se modeló el RI medido en la columna capilar OV-101, usando 1208 compuestos aromáticos optimizados en ...
Duchowicz, Pablo Román, Pis Diez, Reinaldo, Rojas Villa, Cristian Xavier   +2 more
core   +2 more sources