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