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. 2021 Feb 19;11:4247. doi: 10.1038/s41598-021-83269-y

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Discriminant analysis on synthetic molecules. (a) The synthetic molecule FSL indicates Free-Square-Linear geometric signatures are present within domains A, B, and C respectively. Free indicates no restraining forces are applied in that region. (b) The synthetic molecule ELT indicates Extended-Linear-Triangular geometrical signatures within domains A, B, and C respectively. The extended structure emulates a pose for binding to a receptor. (c) The vector space is partitioned into (discriminant, undetermined, indifferent) subspaces, spanned by (d-modes, u-modes, i-modes) shown in bar-graphs, colored as (red, yellow, blue). The decision triad criteria are shown as stacked bar graphs with selection (bottom) corresponding to the signal-to-noise scoring function that rank-orders the modes within each subspace separately, consensus (middle) and quality (top). Consensus and quality values are plotted (above, below) the 0-reference line when selection is (above, below) the bifurcation reference. Gray background shows the decision triad acceptance thresholds. As an output from SPLOC-RNN, the stacked bar graph format highlights the characteristic strengths and weaknesses of each mode.