Active Learning Glide

Active Learning Glide combines Glide's high-performance ligand-receptor docking with Schrödinger’s state-of-the-art deep learning to efficiently screen ultra-large compound libraries.

 

Active Learning Glide is built on the premise that given docking scores for a sufficient number of ligands, an Machine Learning (ML) model can “learn” to recognize features in the ligand structures that can be predictive of the docking score. This trained model can be used to predict the docking scores of ligands outside the training set without needing to dock them first. This method exploits the computational efficiency of ML evaluation (ML evaluation in the Schrodinger Suite is roughly 1000x faster than Glide docking) to allow screening of ultra-large libraries (+ 1BLN ligands) in a reasonable amount of time.

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