Generative AI for graph-based drug design: Recent advances and the way forward
Vikas Garg
Abstract
Discovering new promising molecule candidates that could translate into effective drugs is a key scientific pursuit. However, factors such as the vastness and discreteness of the molecular search space pose a formidable technical challenge in this quest. AI-driven generative models can effectively learn from data, and offer hope to streamline drug design. In this article, we review state of the art in generative models that operate on molecular graphs. We also shed light on some limitations of the existing methodology and sketch directions to harness the potential of AI for drug design tasks going forward.
Topics & Concepts
SketchGenerative grammarComputer scienceKey (lock)GraphArtificial intelligenceChemical spaceData scienceGenerative modelDrug discoveryMachine learningTheoretical computer scienceBioinformaticsAlgorithmBiologyComputer securityComputational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Structure and Dynamics