Awesome-MoML-NeurIPS24
ALL Molecular ML papers from NeurIPS'24.
https://github.com/azminewasi/Awesome-MoML-NeurIPS24
Last synced: 3 days ago
JSON representation
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Graphs and GNNs
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Others
- Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed
- Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
- On the Scalability of GNNs for Molecular Graphs
- GFT: Graph Foundation Model with Transferable Tree Vocabulary
- LLaMo: Large Language Model-based Molecular Graph Assistant
- Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
- Variational Flow Matching for Graph Generation
- Equivariant Neural Diffusion for Molecule Generation
- Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
- Graph Diffusion Transformers for Multi-Conditional Molecular Generation
- Discrete-state Continuous-time Diffusion for Graph Generation
- Diffusion Twigs with Loop Guidance for Conditional Graph Generation
- SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning
- Graph Diffusion Policy Optimization
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Generative Modeling
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Graph, Geometry and GNN Models
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Flow-Matching
- Molecule Generation with Fragment Retrieval Augmentation
- Generalized Protein Pocket Generation with Prior-Informed Flow Matching
- ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
- Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation
- Fisher Flow Matching for Generative Modeling over Discrete Data
- Generating Highly Designable Proteins with Geometric Algebra Flow Matching
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GFlowNets
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Others
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Diffusion Models
- Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models
- Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment
- DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform
- Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
- Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
- Full-Atom Peptide Design with Geometric Latent Diffusion
- Geometric Trajectory Diffusion Models
- Capturing the denoising effect of PCA via compression ratio
- Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design
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Protein Language Models
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Others
- MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering
- ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention
- Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models
- Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction
- Training Compute-Optimal Protein Language Models
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Property Prediction and Optimization
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Multi-modal Models
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Interactions
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Single-cell
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3D Modeling and Representation Learning
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Others
- Multi-Scale Representation Learning for Protein Fitness Prediction
- Kermut: Composite kernel regression for protein variant effects
- Contrastive losses as generalized models of global epistasis
- A probability contrastive learning framework for 3D molecular representation learning
- Generative Modeling of Molecular Dynamics Trajectories
- Molecule Design by Latent Prompt Transformer
- S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search
- Conditional Synthesis of 3D Molecules with Time Correction Sampler
- Approximation-Aware Bayesian Optimization
- MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
- Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits
- Quadratic Quantum Variational Monte Carlo
- TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
- Foundation Inference Models for Markov Jump Processes
- CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy
- Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
- UniIF: Unified Molecule Inverse Folding
- Learning Macroscopic Dynamics from Partial Microscopic Observations
- Extracting Training Data from Molecular Pre-trained Models
- When is an Embedding Model More Promising than Another?
- Mixture of neural fields for heterogeneous reconstruction in cryo-EM
- Inversion-based Latent Bayesian Optimization
- From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
- b"Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling"
- DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
- MatrixNet: Learning over symmetry groups using learned group representations
- Cell ontology guided transcriptome foundation model
- Navigating Chemical Space with Latent Flows
- The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
- Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer
- Towards Stable Representations for Protein Interface Prediction
- Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework
- How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
- Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
- Association Pattern-aware Fusion for Biological Entity Relationship Prediction
- Deep Homomorphism Networks
- Contrastive dimension reduction: when and how?
- Neural Pfaffians: Solving Many Many-Electron Schrodinger Equations
- Approximating mutual information of high-dimensional variables using learned representations
- Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
- FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
- Generative Adversarial Model-Based Optimization via Source Critic Regularization
- CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference
- AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases
- ProtGO: Function-Guided Protein Modeling for Unified Representation Learning
- Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure
- Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
- Pruning neural network models for gene regulatory dynamics using data and domain knowledge
- Transferable Boltzmann Generators
- Persistent Homology for High-dimensional Data Based on Spectral Methods
- Exploring Molecular Pretraining Model at Scale
- On the Adversarial Robustness of Benjamini Hochberg
- Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization
- Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
- Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations
- Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
- HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection
- Learning Identifiable Factorized Causal Representations of Cellular Responses
- Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism
- Score-based 3D molecule generation with neural fields
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