Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical–Genetic Interactions
Hamid Safizadeh, Scott W. Simpkins, Justin Nelson, Sheena Claire Li, Jeff S. Piotrowski, Mami Yoshimura, Yoko Yashiroda, Hiroyuki Hirano, Hiroyuki Osada, Minoru Yoshida, Charles M. Boone, Chad L. Myers · Journal of Chemical Information and Modeling · 2021
as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.