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Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
DFDD: A Cloud-Ready Tool for Distance-Guided Fully Dynamic Docking in Host-Guest Complexation. [PDF]
J Chem Inf ModelHengphasatporn K +3 more
europepmc +1 more source
Constant-pH Simulation of the Human β<sub>2</sub> Adrenergic Receptor Inactivation. [PDF]
J Chem Inf ModelBallabio F, Capelli R.
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Comparison between accuracies of intraoral scanning and photogrammetry techniques performed with various devices in full-arch implant impressions. [PDF]
BMC Oral HealthAlbayrak B +3 more
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Insecticidal efficiency of selected essential oils against stored pests Sitophilus orzyae and Callosobruchus maculatus by using GC-MS analysis. [PDF]
Sci RepRevathy VS, Jacob AM, Binu N.
europepmc +1 more source
Revisiting Vacuum Simulations Using Modern Classical Protein Force Fields. [PDF]
ACS OmegaLam VT, Tran DP.
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Pioneer of bacterial genetics: the legacy of Esther Miriam Lederberg. [PDF]
GeneticsWendling CC, Bailey ZM.
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Journal of Chemical Theory and Computation, 2013
A statistical mechanics description of complex molecular systems involves the determination of ensembles of conformations that represent their Boltzmann distributions. The observable properties of these systems can be then predicted by calculating averages over such ensembles. In principle, given accurate energy functions and efficient sampling methods,
C. Camilloni, A. Cavalli, M. Vendruscolo
openaire +2 more sources
A statistical mechanics description of complex molecular systems involves the determination of ensembles of conformations that represent their Boltzmann distributions. The observable properties of these systems can be then predicted by calculating averages over such ensembles. In principle, given accurate energy functions and efficient sampling methods,
C. Camilloni, A. Cavalli, M. Vendruscolo
openaire +2 more sources