POLYAD: Predicting and Fitting Mixed Vibrational States to a Multi-Resonant Hamiltonian

Polyad is a computer program that constructs sets of strongly interacting vibrational states from resonant interactions. It utilizes the multi-resonant Hamiltonian model which accounts for resonances directly and handles weaker interactions using second order perturbation theory. The model is defined at runtime and includes harmonic, anharmonic, and resonance constants. Polyad connects theory to experiment by predicting energy levels within a user-defined energy range from spectroscopic constants, fitting spectroscopic constants to a set of spectroscopic data, and assessing agreement between a computed model and a spectrum. Results are provided for vibrational levels of water with up to 15,000 cm-1 of energy

Digging Deeper into the Methods of Computational Chemistry

This dissertation applies a skeptical but hopeful analytical paradigm and the tools of linear algebra, numerical methods, and machine learning to a diversity of problems in computational chemistry. When the foundation underlying a project is undermined, the primary purpose of the project becomes digging into the nature and structure of the problem. A common theme emerges in which assumptions in an area are challenged and a deeper understanding of the problem structure leads to new insights. In chapter 2, this approach is exploited to approximate derivative coupling vectors, which together with the difference gradient span the branching planes of conical intersections between electronic states. While gradients are commonly available in many electronic structure methods, the derivative coupling vectors are not always implemented and ready for use in characterizing conical intersections. An approach is introduced which computes the derivative coupling vector with high accuracy (direction and magnitude) using energy and gradient information. The new method is based on the combination of a linear-coupling two-state Hamiltonian and a finite-difference Davidson approach for computing the branching plane. Benchmark cases are provided showing these vectors can be efficiently computed near conical intersections. In chapter 3, this approach yields a countercultural explanation for what machine learning algorithms have learned in modeling a chemical reactivity dataset. Data-driven models of chemical reactions, a departure from conventional chemical approaches, have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not provided the kind of chemical insights that historically advanced the field of chemistry. The chapter examines the knowledgebase of machine learning models—what does the machine learn?—by deconstructing black box machine learning models of a diverse chemical reaction dataset. Through experimentation with chemical representations and modeling techniques, the analysis provides insights into the nature of how statistical accuracy can arise, even when the model lacks informative physical principles. By peeling back the layers of these complicated models we arrive at a minimal, chemically intuitive model (and no machine learning involved). This model is based on systematic reaction type classification and Evans-Polanyi relationships within reaction types which are easily visualized and interpreted. Through exploring this simple model, we gain deeper understanding of the dataset and uncover a means for expert interactions to improve the model’s reliability. In chapter 4, human - algorithm interaction is explored as a paradigm for generating representative ensembles of conformers for organic compounds, a challenging problem in computational chemistry with implications on the ability to understand and predict reactivity. The approach utilizes the molecular editor IQmol as an interface between chemists and reinforcement learning algorithms with the cheminformatics package RDKit as a backbone. Conformer ensembles are evaluated by uniqueness and the approximation they yield of the partition function. Prototype results are presented for a standard reinforcement learning algorithm tested on linear alkanes and chemist manipulation of a fragment of the biomolecule lignin. Future aims and directions for this young project are discussed. The concluding chapter reflects on the broader lessons learned from conducting the dissertation. It discusses open questions and potential paradigms for pursuing them.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155137/1/joshkamm_1.pd

CytoSEED: a Cytoscape plugin for viewing, manipulating and analyzing metabolic models created by the Model SEED

Summary: CytoSEED is a Cytoscape plugin for viewing, manipulating and analyzing metabolic models created using the Model SEED. The CytoSEED plugin enables users of the Model SEED to create informative visualizations of the reaction networks generated for their organisms of interest. These visualizations are useful for understanding organism-specific biochemistry and for highlighting the results of flux variability analysis experiments

CytoSEED: Software for Viewing and Analyzing Bacterial Metabolic Models

The goal of our research is to develop software that will help biologists to better understand and visualize the metabolic pathways of bacteria. A metabolic pathway is a group of compounds and reactions involved in a specific biological process. The Model SEED project currently provides a static viewer for bacterial metabolic pathways. However, this viewer does not support editing pathways - in particular adding, removing, and moving nodes that represent reaction`s and compounds to different pathways. The software we have developed addresses this shortcoming by replicating the capabilities of the current viewer and allowing modifications to the metabolic pathways. These modifications include the ability to move reactions and compounds between existing and newly created pathways. We have also implemented the ability to view multiple bacterial models simultaneously so biologists can easily compare pathways to comprehend differences between bacteria. A goal of this project has also been to take the information that is available within the Model SEED and display it in a more accessible way. This is done through node coloration and connections as well as displaying information inside panels. Our software enables biologists to view data from gene expression experiments by superimposing them on pathways using node coloration. Biologists can use information provided by our software to design laboratory experiments that test bacterial metabolism under specific conditions and view the results. These capabilities enable biologists to gain more knowledge about bacteria relevant to human health, energy production, and environmental impact

Conformer-RL: A deep reinforcement learning library for conformer generation

Conformer-RL is an open-source Python package for applying deep reinforcement learning (RL) to the task of generating a diverse set of low-energy conformations for a single molecule. The library features a simple interface to train a deep RL conformer generation model on any covalently bonded molecule or polymer, including most drug-like molecules. Under the hood, it implements state-of-the-art RL algorithms and graph neural network architectures tuned specifically for molecular structures. Conformer-RL is also a platform for researching new algorithms and neural network architectures for conformer generation, as the library contains modular class interfaces for RL environments and agents, allowing users to easily swap components with their own implementations. Additionally, it comes with tools to visualize and save generated conformers for further analysis. Conformer-RL is well-tested and thoroughly documented with tutorials for each of the functionalities mentioned above, and is available on PyPi and Github: https://github.com/ZimmermanGroup/conformer-rl.Conformer-RL is an open-source Python package for generating conformers of molecules and polymers using deep reinforcement learning. The package includes pretrained models for generating conformers of several classes of covalently bonded molecules as well as a robust library for training and evaluating tailored models for custom molecules and tasks.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/174916/1/jcc26984.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/174916/2/jcc26984_am.pd

Conformational Sampling over Transition-Metal-Catalyzed Reaction Pathways: Toward Revealing Atroposelectivity

The Py-Conformational-Sampling (PyCoSa) technique is introduced as a systematic computational means to sample the configurational space of transition-metal-catalyzed stereoselective reactions. When applied to atroposelective Suzuki–Miyaura coupling to create axially chiral biaryl products, the results show a range of mechanistic possibilities that include multiple low-energy channels through which C–C bonds can be formed

Conformational Sampling over Transition-Metal-Catalyzed Reaction Pathways: Toward Revealing Atroposelectivity

The Py-Conformational-Sampling (PyCoSa) technique is introduced as a systematic computational means to sample the configurational space of transition-metal-catalyzed stereoselective reactions. When applied to atroposelective Suzuki–Miyaura coupling to create axially chiral biaryl products, the results show a range of mechanistic possibilities that include multiple low-energy channels through which C–C bonds can be formed

Isolation and Genome Sequencing of Two Novel Mycobacteriophages, Optimus and Sassafras

Twenty new mycobacteriophages, capable of infecting Mycobacterium smegmatis, were isolated from soil samples collected on or nearby Hope College in Holland, Michigan. Collectively, the group displayed a variety of plaque morphologies indicating an assortment of different phages. Both lytic and temperate phages appear represented in this collection. Purified phage stocks were used to prepare genomic DNA samples for restriction digest analysis. Of 20 samples analyzed, a total of 13 phages produced just 4 types of restriction digest patterns indicating some degree of relatedness among some of our new phage isolates. Interestingly, one group of 4 phages (Optimus, Lynx, Aurora and TheCube14) that yielded a similar restriction digest pattern, were all isolated from mulch-covered soil at a depth of 4-8 cm. Two phages (Optimus and Sassafras) were chosen for complete genome sequencing and comparative genomic analyses. Both phages produced plaques of between 1-2 mm in diameter at 24 hours that enlarged to about 4 mm in diameter after 48 hours of incubation at 37°C. Whereas continued incubation of phage Optimus resulted in cessation of plaque growth by 72 hours, plaques produced by Sassafras continued to enlarge beyond 8 days, reaching a diameter of greater than 10 mm. Phage Optimus produced plaques that displayed a clear center surrounded by turbid rings. Phage Sassafras produced clear plaques with defined edges at 24 hours, but all subsequent growth was progressively more turbid in nature, resulting in plaques with a turbid ring around a center clear zone. Comparison of the restriction digest patterns for Optimus and Sassafras with more than 60 existing mycobacteriophage genomes indicates that Optimus may be a new representative of cluster H, while Sassafras shows some similarity to the F cluster of mycobacteriophages. Results of our analyses of both genomes are reported