首页|Research Statement: First-principle graph equivariant machine learning for drug discovery
Research Statement: First-principle graph equivariant machine learning for drug discovery
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from os f.io: “I design graph-based machine learning models with physics-inspired inductive bi ases to accelerate drug discovery. More specifically, I aspire to expedite the p rocess of: - Structure-based drug discovery with fast and stable E(3)-equivarian t graph models reproducing energy landscapes and ensemble properties of biomolec ular systems trained in an end-to-end differentiable manner; - Ligand-based drug discovery with graph-based neural networks, active learning frameworks, and fou ndation models, emphasizing data efficiency, uncertainty quantification, and rea sonable inductive biases.”
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