Presentation Title

Finding Kinematiphores — Can Molecular Fingerprinting Reveal Active Compounds in Molecular Machines?

Start Date

November 2016

End Date

November 2016

Location

HUB 302-#93

Type of Presentation

Poster

Abstract

Molecular machines are currently at the forefront of attention in the fields of chemistry, materials science, and nanotechnology. The 2016 Nobel Prize for Chemistry was awarded by the Royal Swedish Academy to recognize recent advances in the ability to synthesize these machines. Currently these molecular machines are atomic-scale analogs of mechanical devices (linkages, etc.), however there are many more mechanical components (gears, cams, hinges, universal joints and so forth) for which we have no molecular analogs. Our research aims to fill this gap through the design or identification of kinematiphores: basic structural components of molecules that give rise to specialized kinematic behavior. Unlike mechanical machines which are deterministic, mechanics at the molecular scale is inherently stochastic, and thus we do not assume that direct mechanical analogs are the only or best design solutions at the molecular scale. Being mindful of this, we seek molecular fragments with a given mechanical functionality without biasing our search by presuming how that functionality should be achieved. In seeking these kinematiphores, we perform molecular dynamics simulations of tethered molecules under different modes of deformation, gathering performance data on a pool of ~60,000 candidate molecules. The structure of each molecule is “fingerprinted” and cluster-analysis is used to identify relevant groupings. Machine learning principles of neural networking will then be utilized to demarcate desired performance characteristics in the molecules through the association of fingerprint to simulation data to identify mechanical active moieties that are common to several molecules. These results will be used to provide the basis for modular design of molecular machines through the creation of specialized kinematiphores.

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Finding Kinematiphores — Can Molecular Fingerprinting Reveal Active Compounds in Molecular Machines?

HUB 302-#93

Molecular machines are currently at the forefront of attention in the fields of chemistry, materials science, and nanotechnology. The 2016 Nobel Prize for Chemistry was awarded by the Royal Swedish Academy to recognize recent advances in the ability to synthesize these machines. Currently these molecular machines are atomic-scale analogs of mechanical devices (linkages, etc.), however there are many more mechanical components (gears, cams, hinges, universal joints and so forth) for which we have no molecular analogs. Our research aims to fill this gap through the design or identification of kinematiphores: basic structural components of molecules that give rise to specialized kinematic behavior. Unlike mechanical machines which are deterministic, mechanics at the molecular scale is inherently stochastic, and thus we do not assume that direct mechanical analogs are the only or best design solutions at the molecular scale. Being mindful of this, we seek molecular fragments with a given mechanical functionality without biasing our search by presuming how that functionality should be achieved. In seeking these kinematiphores, we perform molecular dynamics simulations of tethered molecules under different modes of deformation, gathering performance data on a pool of ~60,000 candidate molecules. The structure of each molecule is “fingerprinted” and cluster-analysis is used to identify relevant groupings. Machine learning principles of neural networking will then be utilized to demarcate desired performance characteristics in the molecules through the association of fingerprint to simulation data to identify mechanical active moieties that are common to several molecules. These results will be used to provide the basis for modular design of molecular machines through the creation of specialized kinematiphores.