UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item TOWARDS FULLY AUTOMATED ENHANCED SAMPLING OF NUCLEATION WITH MACHINE-LEARNING METHODS(2024) Zou, Ziyue; Tiwary, Pratyush; Chemistry; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Molecular dynamics (MD) simulation has become a powerful tool to model complex molecular dynamics in physics, materials science, biology, and many other fields of study as it is advantageous in providing temporal and spatial resolutions. However, phenomena of common research interest are often considered rare events, such as nucleation, protein conformational changes, and ligand binding, which occur on timescales far beyond what brute-force all-atom MD simulations can achieve within practical computer time. This makes MD simulation difficult for studying the thermodynamics and kinetics of rare events. Therefore, it is a common practice to employ enhanced sampling techniques to accelerate the sampling of rare events. Many of these methods require performing dimensionality reduction from atomic coordinates to a low-dimensional representation that captures the key information needed to describe such transitions. To better understand the current challenges in studying crystal nucleation with computer simulations, the goal is to first apply developed dimensionality reduction methods to such systems. Here, I will present two studies on applying different machine learning (ML) methods to the study of crystal nucleation under different conditions, i.e., in vacuum and in solution. I investigated how such meaningful low-dimensional representations, termed reaction coordinates (RCs), were constructed as linear or non-linear combinations of features. Using these representations along with enhanced sampling methods, I achieved robust state-to-state back-and-forth transitions. In particular, I focused on the case of urea molecules, a small molecule composed of 8 atoms, which can be easily sampled and is commonly used in daily practice as fertilizer in agriculture and as a nitrogen source in organic synthesis. I then analyzed my samples and benchmarked them against other experimental and computational studies. Given the challenges in studying crystal nucleation using molecular dynamics simulations, I aim to introduce new methods to facilitate research in this field. In the second half of the dissertation, I focused on presenting novel methods to learn low-dimensional representations directly from atomic coordinates without the aid of a priori known features, utilizing advanced machine learning techniques. To test my methods, I applied them to several representative model systems, including Lennard Jones 7 clusters, alanine dipeptide, and alanine tetrapeptide. The first system is known for its well-documented dynamics in colloidal rearrangements relevant to materials science studies, while the latter two systems represent problems related to conformational changes in biophysical studies. Beyond model systems, I also applied my methods to more complex physical systems in the field of materials science, specifically iron atoms and glycine molecules. Notably, the enhanced sampling method integrated with my approaches successfully sampled robust state-to-state transitions between allotropes of iron and polymorphs of glycine.Item TOWARD ENSEMBLE-BASED DRUG DISCOVERY THOUGH ENHANCED SAMPLING(2023) Smith, Zachary; Tiwary, Pratyush; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Quantitatively assessing protein conformational dynamics and ligand dissociation are two problems of critical importance for computer-aided drug discovery. Both of these problems involve larger shifts in the protein conformation than are ordinarily considered in drug discovery efforts. Even though it is well known that proteins are best described as a dynamic ensemble of states, actually acquiring a representative ensemble, especially one with probabilities attached to states, has remained an elusive problem. Molecular dynamics can in theory capture the full ensemble with a long enough simulation but it would take millions of years to simulate the timescale needed to study drug binding or unbinding. Given this timescale problem, it is necessary to develop software solutions to accelerate the sampling of these important rare events. A number of enhanced sampling methods such as metadynamics have arisen to deal with this problem but the methods that are able to attain the fastest speedup also require a low-dimensional description of the system's dynamics. In this thesis, I will develop methods to describe protein dynamics with low-dimensional functions that can be used with enhanced sampling and apply these methods in an enhanced sampling pipeline. The methods developed will both perform variable selection finding a small set of descriptors for the protein dynamics and perform manifold learning to find a low-dimensional representation of the dynamics using this set of descriptions. This pipeline will be used to tackle both problems of conformational dynamics and ligand dissociation in a relatively automated manner. I will then describe how solving these problems in a high throughput manner could impact structure-based drug design efforts, and the work remaining to attain that goal.