NeurIPS 2024 Past Genomics
NeurIPS 2024 Workshop on AI for New Drug Modalities
AIDrugX
- Submission deadline
- Oct 2, 2024, 14:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (111)
Fetched from OpenReview (v2) on 2026-06-10.
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3D Interaction Geometric Pre-training for Molecular Relational Learning
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A Deep Generative Model for the Design of Synthesizable Ionizable Lipids
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A Deep Learning Approach for RNA-Compound Interaction Prediction with Binding Site Interpretability
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A Foundational Multi-Modal Knowledge Graph for Pancreatic Cancer Drug Effects Prediction
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A Large-Scale Foundation Model for RNA Function and Structure Prediction
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Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale
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Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space
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Alignment-based and protein foundation models for viral evolution, vaccines and vectors
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AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactions
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An Efficient Tokenization for Molecular Language Models
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Antibody Library Design by Seeding Linear Programming with Inverse Folding and Protein Language Models
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Applications of Modular Co-Design for De Novo 3D Molecule Generation
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AptaBLE: A Deep Learning Platform for SELEX Optimization
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Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
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Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all
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Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
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Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
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BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein Interactions
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Bridging the Gap between Database Search and \emph{De Novo} Peptide Sequencing with SearchNovo
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CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
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Cell ontology guided transcriptome foundation model
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Chain-of-thoughts for molecular understanding
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Computational Antigen Optimization through Symbolic Optimization and Affinity Maturation Simulation
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Correlational Lagrangian Schrodinger Bridge: Learning Dynamics with Population-Level Regularization
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Deep Interactions for Multimodal Molecular Property Prediction
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DeepADAR: A deep learning approach to model regulatory elements of ADAR-based RNA editing and its application to gRNA design
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DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
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Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding
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Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion
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Detection of RNA Editing Sites by GPT Fine-tuning
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DiffER: Categorical Diffusion Models for Chemical Retrosynthesis
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Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
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Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
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Disentangling the Peptide Space: A Contrastive Approach with Wasserstein Autoencoders
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Distilling Structural Representations into Protein Sequence Models
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Diverse Genomic Embedding Benchmark for functional evaluation across the tree of life.
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Effective Protein-Protein Interaction Exploration with PPIretrieval
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EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
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Epitope Generation for Peptide-based Cancer Vaccine using Goal-directed Wasserstein Generative Adversarial Network with Gradient Penalty
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Evaluating synergies among generative design models for multi-objective optimization of drug-like proteins
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Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs
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Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design
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FluxGAT: Integrating Flux Sampling with Graph Neural Networks for Unbiased Gene Essentiality Classification
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Foundational Model-aided Automatic High-throughput Drug Screening Using Self-controlled Cohort Study
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GeneGench: Systematic Evaluation of Genomic Foundation Models and Beyond
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Generalized Flow Matching for Transition Dynamics Modeling
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Generative Flows on Synthetic Pathway for Drug Design
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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach
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Geometry-text Multi-modal Foundation Model for Reactivity-oriented Molecule Editing
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GFlowNet Pretraining with Inexpensive Rewards
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GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
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Harnessing Preference Optimisation in Protein LMs for Hit Maturation in Cell Therapy
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HELM: Hierarchical Encoding for mRNA Language Modeling
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Homomorphism Counts as Structural Encodings for Molecular Property Prediction
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IgBlend: Unifying 3D Structure and Sequence for Antibody LLMs
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Improved Off-policy Reinforcement Learning in Biological Sequence Design
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Improving Antibody Design with Force-Guided Sampling in Diffusion Models
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Improving Molecular Graph Generation with Flow Matching and Optimal Transport
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Improving Structural Plausibility in 3D Molecule Generation via Property-Conditioned Training with Distorted Molecules
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Interpretable Causal Representation Learning for Biological Data in the Pathway Space
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JAMUN: Transferable Molecular Conformational Ensemble Generation with Walk-Jump Sampling
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Language Models for Text-guided Protein Evolution
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Latent Diffusion Models for Controllable RNA Sequence Generation
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LatentDE: Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design
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Learning Molecular Representation in a Cell
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Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
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Learning Protocols for Non-Equilibrium Conformational Free-Energy Estimation Using Optimal Transport and Conditional Flow Matching
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Learning to refine domain knowledge for biological network inference
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Leveraging Disease-Specific Topologies and Counterfactual Relationships in Knowledge Graphs for Inductive Reasoning in Drug Repurposing
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LLMs are Highly-Constrained Biophysical Sequence Optimizers
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Machine learning enables engineering of potent, specific, and therapeutically developable proteases
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MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models
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MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
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Mixture of Experts Enable Efficient and Effective Protein Understanding and Design
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ML-driven design of 3’ untranslated regions for mRNA stability
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Modeling CAR Response at the Single-Cell Level Using Conditional OT
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Modeling Complex System Dynamics with Flow Matching Across Time and Conditions
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Modeling variable guide efficiency in pooled CRISPR screens with ContrastiveVI+
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MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
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Molecular Generation with State Space Sequence Models
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MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
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Molphenix: A Multimodal Foundation Model for PhenoMolecular Retrieval
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MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model
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Natural Language Prompts Guide the Design of Novel Functional Protein Sequences
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Orthrus: Towards Evolutionary and Functional RNA Foundation Models
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PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation
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PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
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PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
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Pharmacophore-based design by learning on voxel grids
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PLMFit: Benchmarking Transfer Learning with Protein Language Models for Protein Engineering
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PQA: Zero-shot Protein Question Answering for Free-form Scientific Enquiry with Large Language Models
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Probing the Embedding Space of Protein Foundation Models through Intrinsic Dimension Analysis
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ProtPainter: Draw or Drag Protein via Topology-guided Diffusion
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Reinforcement Learning for Enhanced Targeted Molecule Generation Via Language Models
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Representation Learning based Target Discovery from UKBB MRI data
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Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
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Scaling Dense Representations for Single Cell Gene Expression with Transcriptome-Scale Context
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Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics
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Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction
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Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph
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SmileyLlama: Modifying Large Language Models \\for Directed Chemical Space Exploration
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SMORE-DRL: Scalable Multi-Objective Robust and Efficient Deep Reinforcement Learning for Molecular Optimization
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Structure Language Models for Protein Conformation Generation
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TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
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TCRGenesis: Generation of SIINFEKL-specific T-cell receptor sequences using autoregressive Transformer
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Training-Free Guidance with Applications to Protein Engineering
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TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction
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Understanding Protein-DNA Interactions by Paying Attention to Protein and Genomics Foundation Models
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Understanding the Sources of Performance in Deep Drug Response Models Reveals Insights and Improvements
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Video Representation Learning of Cardiac MRI for Genetic Discovery
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Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery