The generated candidates are novel at both the molecular and chemical scaffold levels when compared to the training data.

When the collected dataset is good, i.e., mostly

Backdoor attack is a common threat to deep neural networks.

While finite-dimensional irreducible representations (IRs) are attractive building blocks for equivariance due to their computationally exploitable structure, theyoften don'texist fornon-compact groups, precluding generalizations to most non-linear symmetries, let alone those ill-modeled by groups.