Skip to yearly menu bar Skip to main content


Poster
in
Workshop: New Frontiers of AI for Drug Discovery and Development

Removing Biases from Molecular Representations via Information Maximization

Chenyu Wang · Sharut Gupta · Caroline Uhler · Tommi Jaakkola

Keywords: [ Batch Effect ] [ Molecular Representation ] [ Drug Discovery ] [ information maximization ] [ contrastive learning ]


Abstract:

High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug. Since large-scale screens have to be divided into multiple experiments, a key difficulty is dealing with batch effects, which can introduce systematic errors and non-biological associations in the data. We propose InfoCORE, an Information maximization approach for COnfounder REmoval, to effectively deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. It adaptively reweighs samples to equalize their implied batch distribution. Extensive experiments on drug screening data reveal InfoCORE's superior performance in a multitude of tasks including molecular property prediction and molecule-phenotype retrieval. Additionally, we show results for how InfoCORE offers a versatile framework and resolves general distribution shifts and issues of data fairness by minimizing correlation with spurious features or removing sensitive attributes.

Chat is not available.