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Scalable Molecular Design Using Reve...

Grinaway, Patrick B.

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Scalable Molecular Design Using Reversible Jump MCMC and Stochastic Approximation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Scalable Molecular Design Using Reversible Jump MCMC and Stochastic Approximation./
作者:	Grinaway, Patrick B.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	117 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
Contained By:	Dissertations Abstracts International80-09B.
標題:	Computational chemistry. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13426428
ISBN:	9780438911147

Scalable Molecular Design Using Reversible Jump MCMC and Stochastic Approximation.
Grinaway, Patrick B.

Scalable Molecular Design Using Reversible Jump MCMC and Stochastic Approximation. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 117 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: B.

Thesis (Ph.D.)--Weill Medical College of Cornell University, 2019.

This item must not be added to any third party search indexes.

Despite the existence of useful models for atomic-scale interactions, designing novel molecules (such as drugs) using this information has been extremely difficult. The difficulty results from the nature of the model, which is both extremely high dimensional and multimodal, as well as the nature of the objective function, which is an expectation under this model. In prior work, these models would be leveraged by computing individual expectation values and using these to rank the various molecules. Here, we introduce a joint probability distribution of configurations and chemical states, allowing the simulation to visit different molecular identities as well as different configurations. This introduces the requirement for reversible jump MCMC, as a change in molecular identity results in a change in dimensionality of the configurations. We then combine this approach with the Self-Adjusted Mixture Sampling (SAMS) technique developed by Tan to achieve sampling of chemical identities according to an arbitrary prespecified distribution. We then sought to use the relative free energies of each state as the target distribution, effectively prioritizing favorable chemical states. However, this requires the estimation of free energies. To resolve this, we simultaneously run another MCMC chain that provides online estimates of the necessary free energies. We additionally generate a transdimensional version of nonequilibrium switching amenable to highly parallel hardware setups.

ISBN: 9780438911147Subjects--Topical Terms:

3350019
Computational chemistry.

Scalable Molecular Design Using Reversible Jump MCMC and Stochastic Approximation.
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