Mostafa Karimi

This paper develops interpretable deep learning models for structure-free compound-protein affinity prediction and contact mapping, showing improved interpretability and performance.

This paper develops a deep generative model, gcWGAN, to design novel protein folds using sequence-structure data, achieving higher fold coverage and sequence diversity than a competing method.

This paper introduces a deep generative model for designing drug combinations using graph-based principles, showing it can generate low-toxicity combinations that match known drug modules.

This paper introduces a weakly supervised regression model to predict pathogenicity of missense variants and infer molecular mechanisms from imprecise annotations.

DeepAffinity predicts compound-protein affinity using a semi-supervised deep learning model combining recurrent and convolutional networks, achieving high accuracy and interpretability.