ICML 2025PastLarge language modelsAI for scienceMultimodal
ICML 2025 Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences
FM4LS 2025
- Submission deadline
- May 27, 2025, 18:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (69)
Fetched from OpenReview (v2) on 2026-06-10.
3D-SBDD meets LLM: Towards FDA-Level Drug Design
A Foundation Model for Mass Spectrometry Proteomics
A Genomic Language Model for Zero-Shot Prediction of Promoter Indel Effects
A Look at the Isotropy of Pretrained Protein Language Models
A Multi-Modal Large Language Model for Free-Form, Open-Ended, and Interactive Prediction of Properties and Mechanisms of Candidate Drug Molecules
Advancing Knotted Protein Design with ESM3: Guided Generation and Topological Insights
AIDO.Tissue: Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model
An Interventional Framework of Multimodal Epigenomic Regulation for Gene Expression Prediction
AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
ATOMICA: Learning Universal Representations of Intermolecular Interactions
Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models
Cell-Type-Aware Pooling for Robust Sample Classification in Single-Cell RNA-seq Data
Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies
Closing the gap between the biology and the clinic with a foundation model of immunology and inflammation
Conditional Normalizing Flows for the Design of T Cell Therapies
DeepSeq: High-Throughput Single-Cell RNA Sequencing Data Labeling via Web Search-Augmented Agentic Generative AI Foundation Models
Describe Anything in Medical Images
DisProtEdit: Exploring Disentangled Representations for Multi-Attribute Protein Editing
Enriched Instruction-Following Graph Alignment for Efficient Medical Vision-Language Models
Evaluating Multi-Modal Models for Enzyme-Reaction Retrieval
From Vision to Graph Self-Supervised Learning in Digital Pathology
GeneChat: A Multi-Modal Large Language Model for Gene Function Prediction
H&Enium, Applying Foundation Models to Computational Pathology and Spatial Transcriptomics to Learn an Aligned Latent Space
HPP-Voice: A Large-Scale Evaluation of Speech Embeddings for Multi-Phenotypic Classification
Ibex: Pan-immunoglobulin structure prediction
Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
Joint Diffusion Sampling via Positive-Unlabeled Guidance for Multi-Modal Data
KD-CPT: A Knowledge-Driven Cellular Phenotypic Transdifferentiation Model
Knowledge Graph-Augmented DNA Representation Learning
Learning Diffusion Models with Flexible Representation Guidance
Leveraging the Structure of Medical Data for Improved Representation Learning
Ligand Iterative Sampling for Affinity Refinement and Drug Discovery (LISARDD)
MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models
Molecularly informed analysis of histopathology images using natural language
Multi-Modal Interpretable Graph for Competing Risk Prediction with Electronic Health Records
Multi-Modal Large Language Model Enables Protein Function Prediction
Multi-Modal Medical Image Augmentation for Controlled Heterogeneity and Fair Outcomes
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Multi-Objective-Guided Generative Design of mRNA with Therapeutic Properties
Multimillion cell self-supervised representation learning enables organ-scale tissue niche discovery
Multimodal Benchmarking of Foundation Model Representations for Cellular Perturbation Response Prediction
Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and Chromatin Accessibility Data
Multimodal Protein Language Models for Flexibility Prediction and Loop Design
NextGenPLM: A Novel Structure-Infused Foundational Protein Language Model for Antibody Discovery and Optimization
PM1: A Foundation Model Fusing Genotype, Phenotype, and Image for Precision Medicine
Promoter Sequence Generation using Homology Prompting
ProteinAligner: A Tri-Modal Contrastive Learning Framework for Protein Representation Learning
ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
Rapid and Reproducible Multimodal Biological Foundation Model Development with AIDO.ModelGenerator
RepoLLM: A Multi-modal Foundation Model for Drug Repurposing via Alignment of Molecules, EHRs, and Knowledge Graphs
Retrieval Augmented Protein Language Models for Protein Structure Prediction
Robust Multi-Omics Integration from Incomplete Modalities Significantly Improves Prediction of Alzheimer’s Disease
Scaling up measurement noise scaling laws
Segmentation Helps Understanding: Mask-Infused Vision-Language Pre-training for 3D Medical Images
Self-Supervised Representation Learning for Microbiome Improves Downstream Prediction in Data-Limited Settings and Cross-Cohort Generalizability
SHIVER: Somatic Hypermutation Informed Vocabulary Encoder Representations
SOAPIA: Siamese-Guided Generation of Off Target-Avoiding Protein Interactions with High Target Affinity
Stabilizing protein fitness predictors via the PCS framework
Temporal Representation Learning for Ultrasound Analysis using Masked Modeling
TICA-Based Free Energy Matching for Machine-Learned Molecular Dynamics
Towards foundation models that learn across biological scales
Towards functional annotation with latent protein language model features
Towards Molecular Conformer Generation with Language Models
Transfer Learning of Condition-Specific Perturbation in Gene Interactions: Towards Multi-modal Foundational Modeling of Drug Response
Transferring Cell-level Drug Response to Patient via Tumor Heterogeneity-Aware Alignment and Gene-level Foundational Models
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Uncertainty-Aware Discrete Diffusion Improves Protein Design
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning