Packora: Systematic Design for Generative Molecular Crystal Structure Prediction

Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing can strongly affect material properties. We present Packora, a generative model for molecular CSP that jointly predicts atomic coordinates and the lattice from molecular graphs. To accommodate diverse crystal compositions and available structural information, Packora offers greater conditioning flexibility than existing approaches, accepting any subset of molecular conformers, stereochemical labels, and space-group information while supporting multi-component and organometallic crystals. Beyond this conditioning flexibility, Packora integrates effective design choices for atomistic generative modeling: cacheable pairwise refinement, effective training objectives and numerical solvers, conditioning dropout, and balanced scaling of pairwise and single representations. We also report systematic studies of architecture, training, conditioning, inference, and scaling that we conducted to make the design choices. Finally, we introduce a new evaluation suite for generative molecular CSP that separates generation and ranking, following the CCDC CSP blind test protocol. Packora outperforms baselines in both settings, achieving the highest coverage across all six generation benchmarks, higher experimental-form recovery, lower experimental-form ranks, and faster convergence in ranking.