An important aspect of drug discovery involves determining how well a drug binds to its target (protein) molecule. Typically, this step involves aligning a 3D structure of a drug and its target protein at various configurations in a process called «docking.» Preferred binding sites are then discovered by running docking simulations repeatedly with multiple drug candidates for a particular target molecule. Typically, owing to the vast number of likely candidates for potential drug-target interactions, deep learning models are used to carry out such simulations. However, a problem with using such models is the difficulty in interpreting their predictions. While deep learning certainly makes for a speedy drug discovery, it is little more than a black box. Additionally, docking simulations cannot be used to develop drugs for novel targets that have no known 3D complex with an interacting drug.
Researchers identify new medicines using interpretable deep learning predictions
An important aspect of drug discovery involves determining how well a drug binds to its target (protein) molecule. Typically, this step involves aligning a 3D structure of a drug and its target protein at various configurations in a process called "docking." Preferred binding sites are then discovered by running docking simulations repeatedly with multiple drug candidates for a particular target molecule. Typically, owing to the vast number of likely candidates for potential drug-target interactions, deep learning models are used to carry out such simulations. However, a problem with using such models is the difficulty in interpreting their predictions. While deep learning certainly makes for a speedy drug discovery, it is little more than a black box. Additionally, docking simulations cannot be used to develop drugs for novel targets that have no known 3D complex with an interacting drug.