Results 101 to 110 of about 7,878 (217)

Extending Graph (Discrete) Derivative Descriptors to N-Tuple Atom-Relations [PDF]

, 2015
In the present manuscript, an extension of the previously defined Graph Derivative Indices (GDIs) is discussed. To achieve this objective, the concept of a hypermatrix, conceived from the calculation of the frequencies of triple and quadruple atom ...
Artiles Martínez, Luis M., Barigye, Stephen J., Guerra de León, José O., Marrero-Ponce, Yovani, Martínez Santiago, Oscar, Martínez-López, Yoan, Millán Cabrera, Reisel, Pérez Giménez, Facundo, Torrens, Francisco   +8 more
core   +1 more source

Vaporization enthalpy prediction of ionic liquids based on back-propagation artificial neural network

Green Chemical Engineering
Vaporization enthalpy (ΔvapH) is a fundamental thermodynamic property of ionic liquids (ILs). Accurate prediction of vaporization enthalpy relies on appropriate mathematical models grounded in precise experimental measurements. The quantitative structure-
Changzheng Ji, Zhaochong Shi, Yichao Zheng, Weike Wang, Jialin Shi, Changjun Peng, Honglai Liu   +6 more
doaj   +1 more source

Predicting purification process fit of monoclonal antibodies using machine learning

mAbs
In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation.
Andrew Maier, Minjeong Cha, Sean Burgess, Amy Wang, Carlos Cuellar, Soo Kim, Neeraja Sundar Rajan, Josephine Neyyan, Rituparna Sengupta, Kelly O’Connor, Nicole Ott, Ambrose Williams   +11 more
doaj   +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 model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network. [PDF]

RSC Adv, 2020
Wang Y, Chen X.
europepmc   +1 more source

Application of machine learning models for property prediction to targeted protein degraders

Nature Communications
Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules.
Giulia Peteani, Minh Tam Davide Huynh, Grégori Gerebtzoff, Raquel Rodríguez-Pérez   +3 more
doaj   +1 more source

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  

Quantum Chemical QSPR Study of The Parameters Influences on Viscosity (?) for Some Aniline Derivatives

مجلة علوم ذي قار, 2019
Quantitative Structure-Property Relationship (QSPR) analysis to viscosity (?) of 9 aniline derivatives have been conducted. The study was done by using molecular modelling of aniline compounds. The calculation was performed by the DFT method at B3PW91/6-
Sadiq Ismael, Sadiq Ismael
doaj   +2 more sources

Designing the next generation of polymers with machine learning and physics-based models

Machine Learning: Science and Technology
The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches.
Alex K Chew, Mohammad Atif Faiz Afzal, Anand Chandrasekaran, Jan Henk Kamps, Vaidya Ramakrishnan   +4 more
doaj   +1 more source

Modelling Flavour Release through Quantitative Structure Property Relationships (QSPR)

CHIMIA, 2001
The use of QSPR to explain the partition behaviour of flavour compounds in different matrices and to predict dynamic flavour release from certain food systems is described.
Andrew J. Taylor, Robert S.T. Linforth
doaj