
Included are the Geometry-Complete Diffusion Model (GCDM) checkpoint files and Geometric Latent Diffusion Model (GeoLDM) checkpoint files referenced in our accompanying GCDM manuscript for 3D molecule generation and optimization (https://arxiv.org/abs/2302.04313). Additionally, included with the GCDM checkpoint files are EGNN molecular property prediction checkpoint files trained with three random seeds per property (18 in total). Lastly, GCDM (and EGNN baseline) checkpoint files for structure-based drug design experiments are included (8 in total).
Generative Modeling, Equivariance, Computational Biology, Geometric Deep Learning, Graph Neural Networks
Generative Modeling, Equivariance, Computational Biology, Geometric Deep Learning, Graph Neural Networks
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
