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Found 5 papers, 5 papers with code
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Deep generative models have recently achieved superior performance in 3D molecule generation.
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}.
Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs
To the best of our knowledge, it is the first linear-time GNN model that can count 6-cycles with theoretical guarantees.
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Deep generative models have achieved tremendous success in designing novel drug molecules in recent years.
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
The main computational challenges include: 1) the generation of linkers is conditional on the two given molecules, in contrast to generating full molecules from scratch in previous works; 2) linkers heavily depend on the anchor atoms of the two molecules to be connected, which are not known beforehand; 3) 3D structures and orientations of the molecules need to be considered to avoid atom clashes, for which equivariance to E(3) group are necessary.
