Input reduction reduces the length of the input context before feeding to downstream LLMs.

How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world?

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to generate initial structures and diffuse over torsion angles.

Currently, two mainstream GNN-based methods have been developed for constructing force fields: group theory-based methods and direction-based methods.

Disentangled representation learning (DRL) aims to identify and decompose underlying factors behind observations, thus facilitating data perception and generation.