Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

ABSTRACT: A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical-genetic interaction data from the

SUBMITTER: Safizadeh H 

PROVIDER: S-EPMC8479812 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature