3D Molecule Generation
11 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in 3D Molecule Generation
Most implemented papers
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
bioinfomachinelearning/bio-diffusion
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However, such methods are unable to learn important geometric and physical properties of 3D molecules during molecular graph generation, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which negatively impacts their ability to effectively scale to datasets of large 3D molecules.
Equivariant Energy-Guided SDE for Inverse Molecular Design
Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties.
Geometric Latent Diffusion Models for 3D Molecule Generation
minkaixu/geoldm
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Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as molecule design.
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
graph-0/jodo
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To capture the correlation between molecular graphs and geometries in the diffusion process, we develop a Diffusion Graph Transformer to parameterize the data prediction model that recovers the original data from noisy data.
Graph Generation with Diffusion Mixture
harryjo97/drum
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Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures.
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
In particular, GraphVF represents the first controllable geometry-aware, protein-specific molecule generation method, which can generate binding 3D molecules with tailored sub-structures and physio-chemical properties.
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Deep generative models have recently achieved superior performance in 3D molecule generation.
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
zaixizhang/flag
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Generating molecules with high binding affinities to target proteins (a. k. a.
3D molecule generation by denoising voxel grids
genentech/voxmol
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NeurIPS 2023
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids.
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
The second challenge involves accommodating molecule generation to diffusion and accurately predicting the existence of bonds.
