Results 151 to 160 of about 826,121 (282)

Utilizing Grand Canonical Monte Carlo Methods in Drug Discovery. [PDF]

ACS Med Chem Lett, 2020
Bodnarchuk MS, Packer MJ, Haywood A.
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Monte Carlo methods for medical imaging research. [PDF]

Biomed Eng Lett
Lee H.
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Models of Care in Hospital Medicine: An Analysis of Advance Practitioner Utilization Using Monte Carlo Methods. [PDF]

Perm J
Sharma R, Akram N, Madden M.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Kinetic Monte Carlo methods for three-dimensional diffusive capture problems in exterior domains. [PDF]

R Soc Open Sci
Bernoff AJ, Lindsay AE.
europepmc   +1 more source

Design of a focused collimator for proton therapy spot scanning using Monte Carlo methods. [PDF]

Med Phys, 2020
Geoghegan TJ, Nelson NP, Flynn RT, Hill PM, Rana S, Hyer DE.   +5 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Multilevel Monte Carlo Methods for Stochastic Convection-Diffusion Eigenvalue Problems. [PDF]

J Sci Comput
Cui T, De Sterck H, Gilbert AD, Polishchuk S, Scheichl R.   +4 more
europepmc   +1 more source