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Machine learning and statistical analysis for biomass torrefaction: A review.

Bioresource technology
Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kine...

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods.

Journal of chemical information and modeling
We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for red...

Easy and Rapid Approach to Obtaining the Binding Affinity of Biomolecular Interactions Based on the Deep Learning Boost.

Analytical chemistry
Recently, the deep learning (DL) dimension of artificial intelligence has received much attention from biochemical researchers and thus has gradually become the key approach adopted in the area of biosensing applications. Studies have shown that the ...

Artificial neural network (ANN) and response surface methodology (RSM) algorithm-based improvement, kinetics and isotherm studies of electrocoagulation of oily wastewater.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electr...

Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images.

Journal of computer-aided molecular design
Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed...

RETRACTED: Chemistry-Informed Neural Networks modelling of lignocellulosic biomass pyrolysis.

Bioresource technology
This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the authors and the Editor-in-Chief. The article has reused text fr...

Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution.

Analytical chemistry
Artificial intelligence (AI) is used to quantitatively analyze the voltammetry of the reduction of acetic acid in aqueous solution generating thermodynamic and kinetic data. Specifically, the variation of the steady-state current for the reduction of...

Prediction of the performance of pre-packed purification columns through machine learning.

Journal of separation science
Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts throu...

Protein p Prediction by Tree-Based Machine Learning.

Journal of chemical theory and computation
Protonation states of ionizable protein residues modulate many essential biological processes. For correct modeling and understanding of these processes, it is crucial to accurately determine their p values. Here, we present four tree-based machine l...