Chemistry Meets Biology: Cross-Disciplinary Evaluation of Drug Discovery Databases

This study evaluates the utility of key biochemical databases in drug discovery through a crowd-reviewed analysis, focusing on their application in medicinal chemistry and drug discovery. The findings reveal substantial deviations in data accessibility and reliability across these databases, with specific challenges in integrating chemical and biological information. The results highlight the urgent need for better-structured and user-friendly databases to support the ever-evolving interdisciplinary demands of drug discovery while emphasizing improvements in database design to enhance predictive modeling and efficient data processing. The authors conducted a structured survey among students and researchers to assess data coverage, search functionality, and information quality and provided recommendations and suggestions for increasing their quality.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-04-08
DOI
https://doi.org/10.1021/acs.jcim.5c00021
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.01

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article

Chemistry Meets Biology: Cross-Disciplinary Evaluation of Drug Discovery Databases

Alexander Pashenko, Igor V. Tkachenko, Sergey V. Ryabukhin, Konstantin S. Gavrilenko et al.
2 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
3.01
article

Chemistry Meets Biology: Cross-Disciplinary Evaluation of Drug Discovery Databases

Alexander Pashenko, Igor V. Tkachenko, Sergey V. Ryabukhin, Konstantin S. Gavrilenko, Dmitriy M. Volochnyuk, Nataliya A. Shtil, Duncan B. Judd, Andrii V. Kyrsanov
article en
2 citations

Abstract

This study evaluates the utility of key biochemical databases in drug discovery through a crowd-reviewed analysis, focusing on their application in medicinal chemistry and drug discovery. The findings reveal substantial deviations in data accessibility and reliability across these databases, with specific challenges in integrating chemical and biological information. The results highlight the urgent need for better-structured and user-friendly databases to support the ever-evolving interdisciplinary demands of drug discovery while emphasizing improvements in database design to enhance predictive modeling and efficient data processing. The authors conducted a structured survey among students and researchers to assess data coverage, search functionality, and information quality and provided recommendations and suggestions for increasing their quality.

Journal of Chemical Information and ModelingVol. 65(8)
Taras Shevchenko National University of Kyiv (UA), Stevenage Bioscience Catalyst (GB), Enamine (Ukraine) (UA)
Ministry of Education and Science of Ukraine
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
3.01
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Chemistry Meets Biology: Cross-Disciplinary Evaluation of Drug Discovery Databases — Alexander Pashenko, Igor V. Tkachenko, et al. · Journal of Chemical Information and Modeling (2025) | TGRS Research Map | TGRS