Spotlight at Machine Learning for Drug Discovery at ICLR 2022

Our paper titled Predicting single-cell perturbation responses for unseen drugs is accepted as spotlight talk at MLDD 2022. This paper augments my previous work CPA with a molecular representation to predict unseen drugs. We additionally devised a transfer learning approach to leverage Bulk RNA-seq data to compensate for scarce data problems at the single-cell level.