Drug Discovery
AI Drug Discovery and Molecular Design This program targets the most expensive uncertainty in drug discovery: which molecules and mechanisms deserve experimental attention.
01 Molecules and targets
02 Representation learning
03 Property and interaction prediction
04 Prioritized experimental candidates
Recent papers
What this program is building Selected recent and foundational papers, summarized around the task, why it matters, and the main technical result.
2026 AAAI
Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
Task Infer biological pathways from knowledge bases using graph learning and explanation.
Why it matters Pathway inference helps connect molecular observations to mechanisms that can guide therapeutic hypotheses.
Main result The work emphasizes explainable graph reasoning so inferred pathways can be inspected by scientists. Paper details 2025 AAAI
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations
Task Learn molecule representations that combine molecular structure with external knowledge.
Why it matters Better representations improve downstream property prediction and reduce wasted experimental search.
Main result Bi-level contrastive learning aligns molecular and knowledge views into stronger predictive embeddings. Paper details 2025 ICLR
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Task Infer gene interactions and disease subtype networks from biomedical data.
Why it matters Disease subtyping can reveal mechanisms and patient groups that matter for precision therapeutics.
Main result The model generates subtype-specific interaction networks that make disease heterogeneity more actionable. Paper details 2025 Scientific Data
MLOmics: Cancer Multi-Omics Database for Machine Learning
Task Build a machine-learning-ready cancer multi-omics resource.
Why it matters Shared, well-structured datasets make therapeutic and biomarker modeling more reproducible.
Main result MLOmics packages multi-omics data into a resource designed for ML benchmarking and discovery. Paper details 2025 JDMLR
MolTextQA: A Question-Answering Dataset and Benchmark for Molecular Structure-Text Understanding
Task Evaluate models that reason over molecule structures and scientific text.
Why it matters Drug discovery needs models that understand both chemical graphs and the language scientists use to describe them.
Main result MolTextQA creates a benchmark for testing multimodal molecule-language understanding. Paper details 2023 NeurIPS
CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift
Task Quantify uncertainty for drug property prediction when test molecules differ from training data.
Why it matters Distribution shift is routine in discovery, so calibrated uncertainty is essential for deciding what to test.
Main result CoDrug uses conformal prediction and density estimation to make predictions more reliable under shift. Paper details