Molecular function recognition by supervised projection pursuit machine learning

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Jacobs, D. J., Grear, T., Avery, C., & Patterson, J. (2021). Molecular function recognition by supervised projection pursuit machine learning. Scientific Reports.
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Description

Faculty, Identifying mechanisms that control molecular function is a significant challenge in pharmaceutical science and molecular engineering. Here, we present a novel projection pursuit recurrent neural network to identify functional mechanisms in the context of iterative supervised machine learning for discovery-based design optimization. Molecular function recognition is achieved by pairing experiments that categorize systems with digital twin molecular dynamics simulations to generate working hypotheses. Feature extraction decomposes emergent properties of a system into a complete set of basis vectors. Feature selection requires signal-to-noise, statistical significance, and clustering quality to concurrently surpass acceptance levels. Formulated as a multivariate description of differences and similarities between systems, the data-driven working hypothesis is refined by analyzing new systems prioritized by a discovery-likelihood. Utility and generality are demonstrated on several benchmarks, including the elucidation of antibiotic resistance in TEM-52 beta-lactamase. The software is freely available, enabling turnkey analysis of massive data streams found in computational biology and material science.

Details
Title

Molecular function recognition by supervised projection pursuit machine learning

Author(s)

Jacobs, Donald J.
Grear, Tyler
Avery, Chris
Patterson, John

UNC Charlotte Affiliate Type

Faculty

Extent

1 online resource

Date Created

2021

Genre

articles

Resource Type

text

Abstract

Identifying mechanisms that control molecular function is a significant challenge in pharmaceutical science and molecular engineering. Here, we present a novel projection pursuit recurrent neural network to identify functional mechanisms in the context of iterative supervised machine learning for discovery-based design optimization. Molecular function recognition is achieved by pairing experiments that categorize systems with digital twin molecular dynamics simulations to generate working hypotheses. Feature extraction decomposes emergent properties of a system into a complete set of basis vectors. Feature selection requires signal-to-noise, statistical significance, and clustering quality to concurrently surpass acceptance levels. Formulated as a multivariate description of differences and similarities between systems, the data-driven working hypothesis is refined by analyzing new systems prioritized by a discovery-likelihood. Utility and generality are demonstrated on several benchmarks, including the elucidation of antibiotic resistance in TEM-52 beta-lactamase. The software is freely available, enabling turnkey analysis of massive data streams found in computational biology and material science.

Subjects--Topics

Molecular biology
Pharmaceutical chemistry

Is Part Of

Scientific reports

Language

English

Publisher's Version

doi:10.1038/s41598-021-83269-y

Permalink

http://hdl.handle.net/20.500.13093/work:1012

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