Morphological subprofile analysis for bioactivity annotation of small molecules

Area of Science:

• Computational chemistry
• Chemical biology
• Drug discovery

Background:

• Fast prediction of compound mode of action (MoA) is crucial for bioactivity annotation and early off-target identification in drug discovery.
• Morphological profiling, using assays like Cell Painting, provides rapid, unbiased compound activity assessment.
• Challenges exist due to incomplete bioactivity data and unknown activities of reference compounds.

Purpose of the Study:

• To introduce a novel subprofile analysis method for mapping MoA of both known and novel compounds.
• To enable rapid bioactivity annotation and target identification in large compound libraries.

Main Methods:

• Defined specific MoA clusters based on morphological features.
• Extracted 'subprofiles' comprising a subset of morphological features for each cluster.
• Developed subprofile analysis for compound MoA assignment.

Main Results:

• The subprofile analysis successfully assigned compounds to twelve distinct MoA clusters.
• This approach facilitates rapid bioactivity annotation of unexplored compounds.

Conclusions:

• Subprofile analysis is an effective method for predicting compound MoA and accelerating bioactivity annotation.
• The approach has the potential for future expansion to additional target clusters.