Trends in deep learning for property-driven drug design

HIGHLIGHTS

• who: Muhammad Saim Ansari from the ArxivZürich, Basel, Switzerland have published the paper: Trends in Deep Learning for Property-driven Drug Design, in the Preprint: Arxiv
• what: The authors attempt to fill this gap by highlighting recent progress in DL methodology focusing on two fields: Multimodal drug property prediction, which encompasses all models that A) map from a joint space of molecules and a secondary modality toward some interaction prediction task, and B) can be evaluated on all entities of that modality. The authors focus on three of the most common Quantitative structure-activity . . .
  

   

  

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