Molecular function recognition by supervised projection pursuit machine learning.

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 an

SUBMITTER: Grear T 

PROVIDER: S-EPMC7895977 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature