ICLR 2025 Past GenomicsGenerative models
ICLR 2025 Workshop on Generative and Experimental Perspectives for Biomolecular Design
GEM
- Submission deadline
- Feb 14, 2025, 17:00 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 (97)
Fetched from OpenReview (v2) on 2026-06-10.
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A data guided approach to building an ML ready protein expression dataset
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A Data-Driven Approach to Antigen-Antibody Complex Structure Modeling Using Labeled VHH Antibodies
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A generalized protein design ML model enables generation of functional de novo proteins
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A Guided Design Framework for the Optimization of Therapeutic-like Antibodies
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A latent back-projection network for novel projection synthesis for improved Cryo-ET
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A Mammalian High-Throughput Assay to Screen AI-Designed Protein Degraders
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Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
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Active Learning on Synthons for Molecular Design
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Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints
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AffinityFlow: Guided Flows for Antibody Affinity Maturation
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AI-guided data-scarce engineering of RfxCas13d to create a cell selection tool
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Aligning Chemical and Protein Language Models with Continuous Feedback using Energy Rank Alignment
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All-Atom Protein Generation with Latent Diffusion
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AlphaSAXS: Reconstructing Protein Structure with Physiologically Relevant Conformations from Small Angle X-ray Scattering Data
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An evaluation of unconditional 3D molecular generation methods
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Antibody design using preference optimization and structural inference
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Assessing Quantization and Efficient Fine-Tuning for Protein Language Models
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Bio2Token: All-atom tokenization of any biomolecular structure with Mamba
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CAMP: COMBINATORIAL ENGINEERING OF PROTEINS
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Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
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Conformation-specific Design: a New Benchmark and Algorithm with Application to Engineer a Constitutively Active MAP Kinase
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De Novo Design of Antigen-Specific Antibodies Using Structural Constraint-Based Generative Language Model
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Decoding the Mechanistic Impact of Genetic Variation on Regulatory Sequences with Deep Learning
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Design of Ligand-Binding Proteins with Atomic Flow Matching
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Efficient Molecular Conformer Generation with SO(3) Averaged Flow-Matching and Reflow
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Engineering modular bacteriophage genomes for targeted bacterial elimination
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EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
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ESM-Effect: An Effective and Efficient Fine-Tuning Framework towards accurate prediction of Mutation's Functional Effect
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EVOLUTIONARY POLICY GRADIENT BASED OPTIMIZATION FOR SMALL MOLECULE DRUG DISCOVERY
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Exploring zero-shot structure-based protein fitness prediction
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Fast and Accurate Antibody Sequence Design via Structure Retrieval
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Few-shot active learning for de novo dual-target peptide design with high bio-activity
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Flow-Based Fragment Identification via Contrastive Learning of Binding Site-Specific Latent Representations
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FLOWR -- Flow Matching for Structure- and Interaction-Aware De Novo Ligand Generation
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FragFM: Efficient Fragment-Based Molecular Generation via Discrete Flow Matching
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From Minimal Data To Maximal Insight: A Machine Learning Guided Platform For Peptide Discovery
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GENERATIVE PROTEIN DESIGN FOR OVERLAPPING GENES
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GLID$^2$E: A Gradient-Free Lightweight Fine-tune Approach for Discrete Sequence Design
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Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design
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GROQ-seq: A Collaborative, Open Data Approach to Addressing Protein Function Prediction
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Guided Generation of B-cell Receptors with Conditional Walk-Jump Sampling
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Guided Sequence-Structure Generative Modeling for Iterative Antibody Optimization
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Gumbel-Softmax Score and Flow Matching for Discrete Biological Sequence Generation
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Hierarchical Multiplex Pairwise Golden Gate Assembly: Converting short oligo-pools into longer DNA libraries
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Hierarchical Protein Backbone Generation with Latent and Structure Diffusion
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Higher-Order Molecular Learning: The Cellular Transformer
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IgCraft: A versatile sequence generation framework for antibody discovery and engineering
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Implicit Bayesian Markov Decision Process for Resource-Efficient Experimental Design in Drug Discovery
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Interpreting and Steering Protein Language Models through Sparse Autoencoders
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Inverse problems with experiment-guided AlphaFold
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It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design
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Large Drug Discovery Model
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Learning Representations of Instruments for Partial Identification of Treatment Effects
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MeMDLM: De Novo Membrane Protein Design with Property-Guided Discrete Diffusion
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Metalorian: De Novo Generation of Heavy Metal-Binding Peptides with Classifier-Guided Diffusion Sampling
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Mol-MoE: Training Preference-Guided Routers for Molecule Generation
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Molecular design using graph Bayesian optimization with shortest-path kernels
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Molecular Property Prediction using Pretrained-BERT and Bayesian Active Learning: A Data-Efficient Approach to Drug Design
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moPPIt: De Novo Generation of Motif-Specific Peptide Binders via Conditional Uniform Discrete Diffusion
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OPUS-GO: Unlocking Residue-level Insights from Sequence-level Annotations Using Biological Language Models
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Path Planning for Masked Diffusion Models with Applications to Biological Sequence Generation
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PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
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PickPocket Enables Binding Site Prediction at the Proteome Scale
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Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
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Preferential Multi-Objective Bayesian Optimization for Drug Discovery
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Preventing cell-to-cell tranmission of disordered proto-fibrils of $\alpha$-Synuclein
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Programmable Protein Stabilization with Language Model-Derived Peptide Guides
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Programming co-folding to design binders for intrinsically disordered epitopes
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Protein structure predictors implicitly define binding energy functions
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Reframing Retreival-Augmented Generation for *in silico* optimization of antibody solubility
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Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding
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Repurposing AlphaFold3-like Protein Folding Models for Antibody Sequence and Structure Co-design
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Residue-level text conditioning for protein language model mutation effect prediction
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RNA-EFM : Energy based Flow Matching for Protein-conditioned RNA Sequence-Structure Co-design
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RNAGym: Benchmarks for RNA Fitness and Structure Prediction
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Sampling Protein Language Models for Functional Protein Design
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Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
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Scaling Deep Learning Solutions for Transition Path Sampling
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Sequence-based protein models for the prediction of mutations across priority viruses
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Sesame: Opening the door to protein pockets
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SOAPI: Siamese-guided generation of Off-Target-Avoiding Protein Interactions
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Steering Generative Models with Experimental Data for Protein Fitness Optimization
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Structural modeling of antibody variant epitope specificity with complementary experimental and computational techniques
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Structure-Aware Language Models Trained on Ultra-Mega-Scale Metagenomic Data Improve Protein Folding Stability Prediction
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Structure-based synthetic data augmentation for protein language models
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Substrate-Aware Zero-Shot Predictors for Non-Native Enzyme Activities
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SweetBERT: exploring BERT-based models for IUPAC glycan nomenclature modeling
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SYNEVO: towards synthetic evolution of biomolecules via aligning protein language models to biological hardware
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Targeting Aggregating Proteins with Language Model-Designed Degraders
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TCR-TRANSLATE: CONDITIONAL GENERATION OF REAL ANTIGEN- SPECIFIC T-CELL RECEPTOR SEQUENCES
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Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning
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Test-Time View Selection for Multi-Modal Decision Making
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Towards Interpretable Protein Structure Prediction with Sparse Autoencoders
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Towards More Accurate Full-Atom Antibody Co-Design
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Towards Protein Sequence & Structure Co-Design with Multi-Modal Language Models
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Towards Scaling Laws for Language Model Powered Evolutionary Algorithms: Case Study on Molecular Optimization
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Why risk matters for protein binder design