Journal of chemical information and modeling
Nov 6, 2018
Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered...
Food research international (Ottawa, Ont.)
Nov 1, 2018
In this study, potato starch (PS) was processed by high hydrostatic pressure (HHP) at 200, 350 and 500 MPa for 30 min at 25 °C. Effects of HHP-treated PS on the processing performance of PS-gluten and PS-hydroxypropylmethylcellulose (HPMC) dough-like...
Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assi...
The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that ca...
In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these na...
Journal of chemical information and modeling
Sep 26, 2018
Knowledge of the thermodynamic properties of molecules is essential for chemical process design and the development of new materials. Experimental measurements are often expensive and not environmentally friendly. In the past, studies using molecular...
The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all ...
The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this predict...
Bioorganic & medicinal chemistry letters
Aug 27, 2018
Quantitative structure-activity relationship (QSAR) analysis uses structural, quantum chemical, and physicochemical features calculated from molecular geometry as explanatory variables predicting physiological activity. Recently, deep learning based ...
Journal of chemical theory and computation
Aug 17, 2018
The ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learn...