Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework.

Effective prioritization of chemical compounds that show promising bioactivities from compound screenings represents a first critical step toward identifying successful drug candidates. Current development on computational approaches for compound prioritization is largely focused on devising advanced ranking algorithms that better learn the ordering among compounds. However, such methodologies are fundamentally limited by the scarcity of available data, particularly when the screenings are conducted at a relatively small scale over known promising compounds. Instead, in this work, we explore the structures of bioassay space and leverage such structures to improve ranking performance of an existing strong ranking algorithm. This is done by identifying assistance bioassays and assistance compounds intelligently and leveraging such assistance within the existing ranking algorithm. By leveraging the assistance bioassays and assistance compounds, the data scarcity can be properly compromised. Along this line, we develop a suite of assistance bioassay selection methods and assistance compound selection methods. Our experiments demonstrate an overall 8.34% improvement on the ranking performance over the state of the art.