[RetMol] Retrieval-based Controllable Molecule Generation (spotlight) [Paper]
[GAFlowNets] Generative Augmented Flow Networks (spotlight) [Paper]
[Zaidi's model] Pre-training via Denoising for Molecular Property Prediction (spotlight) [Paper]
[Equiformer] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs (spotlight) [Paper] [Code]
[FAB] Flow Annealed Importance Sampling Bootstrap (spotlight) [Paper]
[FLAG] Molecule Generation For Target Protein Binding with Structural Motifs [Paper]
[TargetDiff] 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction [Paper]
[HMR] Learning Harmonic Molecular Representations on Riemannian Manifold [Paper]
[GeoSSL] Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching [Paper] [Code]
[MiCaM] De Novo Molecular Generation via Connection-aware Motif Mining [Paper]
[EEGSDE] Equivariant Energy-Guided SDE for Inverse Molecular Design [Paper] [Code]
[MHNfs] Context-enriched molecule representations improve few-shot drug discovery [Paper]
[DiGress] DiGress: Discrete Denoising diffusion for graph generation [Paper] [Code]
[Mole-BERT] Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules [Paper]
[Transformer-M] One Transformer Can Understand Both 2D & 3D Molecular Data [Paper] [Code]
[O-GNN] O-GNN: incorporating ring priors into molecular modeling [Paper] [Code]
[ #Circles] How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules [Paper]
[ADKF-IFT] Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction [Paper]
[PlaNet] Sampling-free Inference for Ab-Initio Potential Energy Surface Networks [Paper]
[BetaAlign] Multiple sequence alignment as a sequence-to-sequence learning problem [Paper]
[I^2-GNNs] Boosting the Cycle Counting Power of Graph Neural Networks with I^2-GNNs [Paper]
[PiFold] PiFold: Toward effective and efficient protein inverse folding (spotlight) [Paper] [Code]
[ProtSeed] Protein Sequence and Structure Co-Design with Equivariant Translation [Paper]
[GearNet] Protein Representation Learning by Geometric Structure Pretraining [Paper] [Code]
[KeAP] Protein Representation Learning via Knowledge Enhanced Primary Structure Reasoning [Paper] [Code]
[RDE] Rotamer Density Estimators are Unsupervised Learners of the Effect of Mutations on Protein-Protein Interaction [Paper]
[HotProtein] HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing [Paper]
[ProtDiff;SMCDiff] Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem [Paper]
[PromptProtein] Multi-level Protein Structure Pre-training via Prompt Learning [Paper]
[ProNet] Hierarchical Protein Representations via Complete 3D Graph Networks [Paper]
[ATUE] On Pre-training Language Model for Antibody [Paper]
[ModelAngelo] ModelAngelo: Automated Model Building for Cryo-EM Maps [Paper]
[CDConv] Continuous-Discrete Convolution for (3+1)D Geometry-Sequence Modeling in Proteins [Paper]
[Rec2Odorant] Matching receptor to odorant with protein language and graph neural networks [Paper] [Code]
[E3Bind] E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking [Paper]
[Uni-Mol] A Universal 3D Molecular Representation Learning Framework [Paper] [Code]
[DiffDock] DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking [Paper] [Code]
[SQUID] Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design [Paper] [Code]