Applications of machine learning in molecular simulations : Transcending barriers
Year: 2016
Type: dissertation
Abstract: Molecular dynamics has established itself over the last years as a strong tool for structure-based molecular investigation in biology. Stefan Doerr's thesis concerns the application of machine learning methods to molecular dynamics data. The goal is twofold. To improve the sampling capabilities of molecular dynamics to allow for slower and more interesting biological processes to be studied and to improve the reproducibility, accessibility and power of molecular dynamics through the development of the required software tools. A great improvement in sampling speed is achieved in this thesis through the application and development of adaptive sampling methods. These allow the computer to build a model based on knowledge gathered from previous simulations and sample the configurational space in a more intelligent manner. On the other hand, through the development of the HTMD software which provides a unified framework for molecular discovery, the reproducibility of molecular experiments is increased, the access to MD research is simplified for new scientists and experiments can be easily performed in a high-throughput manner.
Author: Stefan Doerr
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2783462684