Results 51 to 60 of about 11,478 (188)

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source

An Atom Counting QSPR Protocol [PDF]

QSAR & Combinatorial Science, 2008
AbstractA simple descriptor, viz the number of carbon (NC)/non‐hydrogenic (NNH) atoms present in a molecule, is proposed for the development of useful Quantitative Structure–Property–Relationship (QSPR) models. This work is prompted by that of Randic and Basak (J. Chem. Inf. Comput. Sci.
Santanab Giri, Dehesh R. Roy, Sofie Van Damme, Patrick Bultinck, Venkatesan Subramanian, Pratim K. Chattaraj   +5 more
openaire   +1 more source

Quantitative Analysis Reveals Hitchhiking Drives Polysorbate Hydrolase Persistence Via Host Cell Protein–Antibody Interactions

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Polysorbate‐degrading host cell proteins (HCPs) represent a critical challenge in the manufacturing of monoclonal antibody therapeutics due to their potential to persist during downstream processing. While their enzymatic activity has been characterized, the role of direct HCP‐mAb interactions, particularly those involving polysorbate ...
Melanie Maier, Lukas Griesinger, Matthias Franzreb, Simon Kluters   +3 more
wiley   +1 more source

Multimodal Chromatography in the Downstream Processing of mAb‐Based Products: Mechanisms, Strategies, and Applications

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Multimodal chromatography has emerged as a powerful tool for the purification of monoclonal antibodies (mAbs) and their derivatives—including antibody fragments (Fabs), Fc‐fusions, bispecific (BsAb), and antibody–drug conjugates (ADCs)—offering enhanced selectivity through the integration of ionic, hydrophobic, hydrogen‐bonding, and π–π ...
Amin Javidanbardan, Marco P․ C․ Marques, Ana M. Azevedo, Daniel G. Bracewell, Stephen Goldrick   +4 more
wiley   +1 more source

Computational and Machine‐Learning Studies of Ethylene Oligomerization

Carbon and Hydrogen, EarlyView.
This review focuses on recent advances in computational and machine‐learning studies of ethylene oligomerization, highlighting mainstream catalyst systems based on Co, Ta, Ti, Zr, and Hf, with particular emphasis on Fe‐ and Cr‐based catalysts and their controlling factors governing reactivity and LAO distribution.
Zhixin Qin, Zhihui Song, Heying Feng, Li Jin   +3 more
wiley   +1 more source

Using generalized quantitative structure–property relationship (QSPR) models to predict host cell protein retention in ion‐exchange chromatography

Journal of Chemical Technology &Biotechnology, EarlyView.
Abstract BACKGROUND Selecting an optimal chromatography resin during biopharmaceutical downstream process development is a great challenge. This is especially the case for recombinant subunit vaccines, where product properties vary greatly and recovery often involves cell lysis, which yields a complex mixture of different host cell materials. Host cell
Tim Neijenhuis, Olivier Le Bussy, Geoffroy Geldhof, Marieke E Klijn, Marcel Ottens   +4 more
wiley   +1 more source

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

Macromolecular Rapid Communications, EarlyView.
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

Predicting Drop‐Weight Impact Sensitivity From Molecular Graphs Using Physics‐Informed Artificial Intelligence

Propellants, Explosives, Pyrotechnics, EarlyView.
This work presents an approach for predicting the drop‐weight impact sensitivity of pure molecular explosives directly from 2D molecular graphs using physics‐informed artificial intelligence (AI) models. A dataset comprising experimentally measured sensitivities for 625 unique high‐explosive molecules is augmented with physics‐informed synthetic ...
Grant Hutchings, Jack V. Davis, Virginia W. Manner, Marc J. Cawkwell, Frank W. Marrs   +4 more
wiley   +1 more source