Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2022 Jun 4;14:30. doi: 10.1186/s13321-022-00608-5

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© The Author(s) 2022

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/. 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 in a credit line to the data.

PMC Copyright notice

Fig. 6 — A prediction performance comparison of various classifiers using different tokenization. MTT(ECFP) represents the classifier using MTT with ECFP1 and ECFP2 tokenization. MTT(ECFP + BPE) represents the classifier using MTT with ECFP1, ECFP2, and BPE tokenization. Exper-imental results show that the adoption of multiple tokenization can improve prediction performance