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. 2021 Apr 3;26(7):2065. doi: 10.3390/molecules26072065

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© 2021 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Effect of adjusting the temperature parameter in the contrastive learning loss on the distribution of molecules in the latent space as visualized via the t-SNE algorithm. For clarity, only a random subset of 2000 natural products is shown. (A) Learning based purely on the cross-entropy objective function. (B–E) The temperature scalar (as in [112]) was varied between 0.02 and 0.5 as indicated. (Reducing t below led to numerical instabilities.) All drugs, fluorophores, and Recon2 metabolites are plotted, along with a randomly chosen 2000 natural products (as in [113]).

Effect of adjusting the temperature parameter in the contrastive learning loss on the distribution of molecules in the latent space as visualized via the t-SNE algorithm. For clarity, only a random subset of 2000 natural products is shown. (A) Learning based purely on the cross-entropy objective function. (B–E) The temperature scalar (as in [112]) was varied between 0.02 and 0.5 as indicated. (Reducing t below led to numerical instabilities.) All drugs, fluorophores, and Recon2 metabolites are plotted, along with a randomly chosen 2000 natural products (as in [113]).