RXNFLOW is a generative flow network that efficiently designs diverse, potent, and synthesizable drug candidates by operating over massive chemical reaction spaces. It achieved superior docking scores and synthesizability across 15 protein targets, maintaining high diversity and enabling zero-shot sampling and objective adaptation without retraining.
View blogA new framework for learning and computation on Symmetric Positive Definite (SPD) matrix manifolds is introduced, featuring a vector-valued distance function and an extension of gyrocalculus. This approach enables specialized neural layers and generally outperforms Euclidean and hyperbolic models on tasks like knowledge graph completion and item recommendation.
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