Public articles linked to the same research event.
arXiv GRACE is a 3D collision cross section (CCS) predictor that adapts a pretrained molecular geometry encoder through geometric residual adduct conditioning via early fusion; on over 9,000 experimental molecule-adduct CCS records it achieves the best mean percentage difference among the evaluated learned models on random, scaffold, and adduct-sensitive splits (1.67%, 2.11%, and 2.36% respectively), shows consistently lower error across four independent external test sets, and attains the lowest mean percent difference against four previously reported physics-based workflows on a held-out set.
GRACE is a 3D collision cross section (CCS) predictor that adapts a pretrained molecular geometry encoder through geometric residual adduct conditioning via early fusion; on over 9,000 experimental molecule-adduct CCS records it achieves the best mean percentage difference among the evaluated learned models on random, scaffold, and adduct-sensitive splits (1.67%, 2.11%, and 2.36% respectively), shows consistently lower error across four independent external test sets, and attains the lowest mean percent difference against four previously reported physics-based workflows on a held-out set.
GRACE is a 3D collision cross section (CCS) predictor that adapts a pretrained molecular geometry encoder through geometric residual adduct conditioning via early fusion; on over 9,000 experimental molecule-adduct CCS records it achieves the best mean percentage difference among the evaluated learned models on random, scaffold, and adduct-sensitive splits (1.67%, 2.11%, and 2.36% respectively), shows consistently lower error across four independent external test sets, and attains the lowest mean percent difference against four previously reported physics-based workflows on a held-out set.
GRACE is a 3D collision cross section (CCS) predictor that adapts a pretrained molecular geometry encoder through geometric residual adduct conditioning via early fusion; on over 9,000 experimental molecule-adduct CCS records it achieves the best mean percentage difference among the evaluated learned models on random, scaffold, and adduct-sensitive splits (1.67%, 2.11%, and 2.36% respectively), shows consistently lower error across four independent external test sets, and attains the lowest mean percent difference against four previously reported physics-based workflows on a held-out set.