awesome-moml-neurips24
github.com/azminewasi/awesome-moml-neurips24 ↗ALL Molecular ML papers from NeurIPS'24.
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Generative ModelingMulti-modal ModelsInteractionsSingle-cellGraphs and GNNsProtein Language ModelsProperty Prediction and OptimizationRNA3D Modeling and Representation LearningMore Collectons:
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Installation instructions →What's inside
Single-cell
3D Modeling and Representation Learning
- AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases
- Approximating mutual information of high-dimensional variables using learned representations
- Approximation-Aware Bayesian Optimization
- A probability contrastive learning framework for 3D molecular representation learning
- Association Pattern-aware Fusion for Biological Entity Relationship Prediction
- b"Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling"
Generative Modeling
- Aligning Target-Aware Molecule Diffusion Models with Exact Energy OptimizationDiffusion Models
- Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion ModelsDiffusion Models
- Capturing the denoising effect of PCA via compression ratioDiffusion Models
- DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transformDiffusion Models
- Equivariant Blurring Diffusion for Hierarchical Molecular Conformer GenerationDiffusion Models
- ET-Flow: Equivariant Flow-Matching for Molecular Conformer GenerationFlow-Matching
Graphs and GNNs
- Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
- Diffusion Twigs with Loop Guidance for Conditional Graph GenerationDiffusion Models
- Discrete-state Continuous-time Diffusion for Graph GenerationDiffusion Models
- Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
- Equivariant Neural Diffusion for Molecule GenerationDiffusion Models
- GFT: Graph Foundation Model with Transferable Tree Vocabulary
More Collectons:
Interactions
Protein Language Models
- Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models
- MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering
- ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention
- Training Compute-Optimal Protein Language Models
- Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction
Showing a sample of 117 resources. View the full list on GitHub →