All Outputs (4)

GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling (2021)
Journal Article
Louison, K. A., Louison, K. A., Dryden, I. L., & Laughton, C. A. (2021). GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling. Journal of Chemical Theory and Computation, 17(12), 7930-7937. https://doi.org/10.1021/acs.jctc.1c00735

We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coor... Read More about GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling.

Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip (2021)
Journal Article
Burroughs, L., Amer, M., Vassey, M., Koch, B., Figueredo, G., Mukonoweshuro, B., …Alexander, M. R. (2021). Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip. Biomaterials, 271, Article 120740. https://doi.org/10.1016/j.biomaterials.2021.120740

© 2021 The Authors Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages an... Read More about Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip.

Principal nested shape space analysis of molecular dynamics data (2019)
Journal Article
Dryden, I. L., Kim, K., Laughton, C. A., & Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13(4), 2213-2234. https://doi.org/10.1214/19-AOAS1277

Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of interest to summarize the shape evolution of the molecules in a succinct, low-dimensional representation. However, Euclidean techniques such as principa... Read More about Principal nested shape space analysis of molecular dynamics data.

Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability (2018)
Journal Article
Fridgeirsdottir, G., Harris, R., Dryden, I. L., Fischer, P. M., & Roberts, C. J. (2018). Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability. Molecular Pharmaceutics, 15(5), https://doi.org/10.1021/acs.molpharmaceut.8b00021

Solid dispersions can be a successful way to enhance the bioavailability of poorly soluble drugs. Here 60 solid dispersion formulations were produced using ten chemically diverse, neutral, poorly soluble drugs, three commonly used polymers, and two m... Read More about Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability.