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How to stop worrying and love multiple citation experimental data

Yaroslav Vladislavovich Timofeev 1, 2
Yaroslav Vladislavovich Timofeev
, 
Amir M. Mrasov 1, 2
Amir M. Mrasov
, 
Maria Vyacheslavovna Panova 1
Maria Vyacheslavovna Panova
, 
Fedor Nikolaevich Novikov 1
Fedor Nikolaevich Novikov
, 
Igor Svitanko 1
Igor Svitanko
10.71267/mencom.7710
Published 2025-02-18
Communication , Volume 35, Issue 2, 224-227
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Timofeev Y. V. et al. How to stop worrying and love multiple citation experimental data // Mendeleev Communications. 2025. Vol. 35. No. 2. pp. 224-227.
GOST all authors (up to 50) Copy
Timofeev Y. V., Mrasov A. M., Panova M. V., Novikov F. N., Svitanko I. How to stop worrying and love multiple citation experimental data // Mendeleev Communications. 2025. Vol. 35. No. 2. pp. 224-227.
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TY - JOUR
DO - 10.71267/mencom.7710
UR - https://mendcomm.colab.ws/publications/10.71267/mencom.7710
TI - How to stop worrying and love multiple citation experimental data
T2 - Mendeleev Communications
AU - Timofeev, Yaroslav Vladislavovich
AU - Mrasov, Amir M.
AU - Panova, Maria Vyacheslavovna
AU - Novikov, Fedor Nikolaevich
AU - Svitanko, Igor
PY - 2025
DA - 2025/02/18
PB - Mendeleev Communications
SP - 224-227
IS - 2
VL - 35
ER -
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@article{2025_Timofeev,
author = {Yaroslav Vladislavovich Timofeev and Amir M. Mrasov and Maria Vyacheslavovna Panova and Fedor Nikolaevich Novikov and Igor Svitanko},
title = {How to stop worrying and love multiple citation experimental data},
journal = {Mendeleev Communications},
year = {2025},
volume = {35},
publisher = {Mendeleev Communications},
month = {Feb},
url = {https://mendcomm.colab.ws/publications/10.71267/mencom.7710},
number = {2},
pages = {224--227},
doi = {10.71267/mencom.7710}
}
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Timofeev, Yaroslav Vladislavovich, et al. “How to stop worrying and love multiple citation experimental data.” Mendeleev Communications, vol. 35, no. 2, Feb. 2025, pp. 224-227. https://mendcomm.colab.ws/publications/10.71267/mencom.7710.

Keywords

biological activity.
ChEMBL database
machine learning
MEDLINE
NLP
OOD detection

Abstract

Numerous public databases now collect and disseminate biological activity data from literature and patents, forming the basis for chemogenomics and novel scoring functions. However, data quality is often compromised due to multiple citations of values across different studies with varying protocols. To address this issue, we used the XGBoost model in combination with a BERT-based NLP approach and a distance-based out-of-distribution (OOD) data detection method to enhance classification accuracy and exclude review articles.

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