This PhD project is on the algorithmic aspects of synthesis planning and mass spectrometry; two separate chemical problems concerning the understanding of molecules and how these behave.
Part I:
In synthesis planning, the goal is to synthesize a target molecule from available starting materials, possibly optimizing costs such as price or environmental impact of the process. Current algorithmic approaches to synthesis planning are usually based on selecting a bond set and finding a single good plan among those induced by it.
We demonstrate that synthesis planning can be phrased as a combinatorial optimization problem on hypergraphs, not necessarily using a pre-defined bond set. For this, individual synthesis plans are modeled as directed hyperpaths embedded in a hypergraph of reactions (HoR) representing the chemistry of interest. As a consequence, application of a know polynomial time algorithm to find the K shortest hyperpaths yields the K best synthesis plans for a given target molecule. To this end, classical quality measures are discussed.
Having K good plans to choose from has several benefits: It makes the synthesis planning process much more robust when in later stages adding further chemical details, it allows one to combine several notions of cost, and it provides a way to deal with imprecise yield estimates.
An empirical study of our method illustrates the limitations of what a chemist can expect is feasible to compute, as well as the practical value of our method for cases where yield estimates are imprecise or unknown. To illustrate the realism of the approach, synthesis plans from our abstraction level are compared with detailed chemical synthesis plans from the literature. For this, a synthesis plan for Wieland-Miescher ketone and a synthesis plan for lysergic acid are used.
In addition, equivalence of our structural definition of a hyperpath and two definitions from the hypergraph literature is shown.
Part II:
Mass spectrometry is an analytic technique for characterizing molecules and molecular mixtures, by gaining knowledge of their structure and composition from the way they fragment. In a mass spectrometer, molecules or molecular mixtures are ionized and fragmented, and the abundances of the different fragment masses are
measured, resulting in so-called mass spectra.
We suggest a new road map improving the current state-of-the art in computational methods for mass spectrometry. The main focus is on increasing the chemical realism of the modeling of the fragmentation process. Two core ingredients for this are i) describing the individual fragmentation reactions via graph transformation rules and ii) expressing the dynamics of the system via reaction rates and quasi-equilibrium theory. Graph transformation rules are used both for specifying the possible core fragmentation reactions, and for characterizing the reaction sites when learning values for the rates. We believe that this model describes chemical mechanisms more accurately than previous ones, and that this can lead to both better spectrum prediction and more explanatory power. Our modeling of system dynamics also allows better separation of instrument dependent and instrument independent parameters of the model.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:32819 |
| Date | 28 January 2019 |
| Creators | Kianian, Rojin |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/updatedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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