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. 2019 Mar 28;11:25. doi: 10.1186/s13321-019-0347-6

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 2 — Genetic algorithm-informed design suggestions. Output of the optimization genetic algorithm used to identify the best library designs, showing the pareto front (and the zoom of the 70–100% variability region of the pareto front) with all the working solutions (blue) and the best solutions (red) from the final population, obtained using the same input as in Fig. 1a. Three representative solutions (BS 1, BS 2 and BS 3 corresponding to 100%, 94% and 92% respectively) have been selected as suggestions for three possible designs to obtain “simplified” and maximally diverse OBOC libraries