From Cellular Responses to Pharmacological Domains: Multimodal Zero-Shot Drug Representation Learning

2026/07/28 by Jintao Huang, Lu Leng, Ziyuan Yang
#cs.AI

paper · pdf

Abstract

Multimodal drug discovery enables drug representation learning beyond chemical structure by incorporating cellular responses such as gene expression and cell morphology. However, direct fusion and instance-level contrastive alignment may mix mechanism-related signals with modality-specific noise and incorrectly separate structurally dissimilar but biologically related compounds. This limitation can obscure transferable mechanism patterns required for predicting the properties of unseen compounds. We introduce PMRD, a pharmacological response domain-guided framework for multimodal zero-shot drug property prediction. PMRD separates mechanism-consistent factors from modality-specific information and constructs a consensus response domain across three modalities. Mechanism candidate augmentation identifies locally stable factors, while retrieval-geometry attribution dynamically reweights the alignment and augmentation objectives according to whether their updates preserve inter-drug discriminability.This feedback suppresses training signals that conflict with mechanism-discriminative retrieval. PMRD further combines complementary representations through reliability-aware multiview retrieval. Experiments on public datasets show improved zero-shot property prediction and more biologically coherent drug neighborhoods. Hard-negative analysis further indicates fewer conflicts between structurally dissimilar but response-related compounds. These results support PMRD as an effective framework for mechanism-aware multimodal drug representation learning.\footnoteThe code will be released upon publication.

Citations

• Learning Molecular Representation in a Cell
• On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
• Can contrastive learning avoid shortcut solutions?
• Self-supervised Graph-level Representation Learning with Local and Global Structure
• Learning Transferable Visual Models From Natural Language Supervision
• Graph Contrastive Learning with Augmentations
• Strategies for Pre-training Graph Neural Networks
• Neural Message Passing for Quantum Chemistry
• MoleculeNet: A Benchmark for Molecular Machine Learning
• ChEMBL: a large-scale bioactivity database for drug discovery

Related

• How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
• AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
• MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
• Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening
• ProtoMol: Enhancing Molecular Property Prediction via Prototype-Guided Multimodal Learning