Structure-based drug discovery involves exploring chemical space in search of novel compounds that are likely to bind to and modulate the activity of a biomolecular drug target. There is an urgent need for efficient strategies for exploring chemical space, conditioned by the target’s unique biophysical properties. In this project, researchers will use a structure-aware approach that combines generative artificial intelligence models and molecular docking to rapidly explore chemical space and generate target-specific virtual libraries. Such target-specific virtual libraries will likely contain compounds that medicinal chemists can use as starting points for developing novel drug candidates.
2020
Leveraging Generative Artificial Intelligence for the De Novo Design of Biomolecular Probes
Structure-based drug discovery involves exploring chemical space in search of novel compounds that are likely to bind to and modulate the activity of a biomolecular drug target. There is an urgent need for efficient strategies for exploring chemical space, conditioned by the target’s unique biophysical properties. In this project, researchers will use a structure-aware approach that combines generative artificial intelligence models and molecular docking to rapidly explore chemical space and generate target-specific virtual libraries. Such target-specific virtual libraries will likely contain compounds that medicinal chemists can use as starting points for developing novel drug candidates.
The project’s “sample-and-dock” approach to generate target-specific screening library for drug targets.