Sohan Addagudi

This paper introduces CellVS-Net, a cell-level virtual screening model that outperforms existing methods on two benchmarks, demonstrating its viability as an alternative to molecular-level drug screening.

This study evaluates foundation models for predicting cellular responses to perturbations, finding that some pretrained embeddings improve predictions while others do not.

AIDO.ModelGenerator enables rapid, reproducible multimodal foundation model development by integrating datasets, pre-trained models, and experiment recipes to improve biological predictions.

This paper benchmarks foundation model representations for predicting cellular perturbation responses, identifying effective representation learning strategies.

The paper introduces AIDO.Cell, a series of dense Transformer models pre-trained on human single-cell RNA-seq data, achieving state-of-the-art results in clustering, classification, and perturbation modeling.