Rokas Elijošius

Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models

Rem3Di is introduced, a representation-learning framework that repurposes latent features from atomistic foundation models as transferable molecular descriptors for property prediction and virtual screening and provides a route from simulation-trained atomistic representations to transferable, chirality-aware molecular representations for chemical machine learning.