Combining IC₅₀ or K_i Values from Different Sources Is a Source of Significant Noise.

ABSTRACT: As part of the ongoing quest to find or construct large data sets for use in validating new machine learning (ML) approaches for bioactivity prediction, it has become distressingly common for researchers to combine literature IC₅₀ data generated using different assays into a single data set. It is well-known that there are many situations where this is a scientifically risky thing to do, even when the assays are against exactly the same target, but the risks of assays being incompatible are even higher when pulling data from large collections of literature data like ChEMBL. Here, we estimate the amount of noise present in combined data sets using cases where measurements for the same compound are reported in multiple assays against the same target. This approach shows that IC

SUBMITTER: Landrum GA 

PROVIDER: S-EPMC10934815 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature