| Oct 16, 2026 | 🎉 Honored to be recognized as a Top Reviewer at NeurIPS 2026! |
| Apr 7, 2026 | 🎉 Our work CAML (A Conflict-Aware Molecular Language Model Merging Framework for Multi-Constraint Molecular Generation) was accepted by ACL 2026 (Main Conference, Long Paper)! It links language-model parameter composition to generated molecular properties and jointly controls four predicted properties without further model training. ** The published paper is available. |
| Apr 1, 2026 | Our multidisciplinary manuscript, “AI Identifies Compounds that Protect Retinal Ganglion Cells and Optic Nerve in Neurodegeneration by Alleviating ER Stress,” studies an AI-driven screening pipeline using ImageMol to identify neuroprotective candidates targeting endoplasmic reticulum stress, with potential relevance to glaucoma. Nature Communications · Under review: major revision. |
| Mar 1, 2026 | 🚀 Historical code release: our earlier work, “Multi-objective generative algorithm enables broad-spectrum antibiotic derivatization,” was open-sourced. This announcement is retained as a historical record; see the current publication and manuscript list for up-to-date titles and submission statuses. |
| Feb 21, 2026 | 🎉 Our work STEPH (Sparse Task Vector Mixup with Hypernetworks for Efficient Knowledge Transfer in Whole-Slide Image Prognosis) was accepted by CVPR 2026. It studies efficient knowledge transfer for whole-slide image prognosis. ** The published paper and code are available. |
| Dec 7, 2025 | 🎉 Our paper “Self-supervised Blending Structural Context of Visual Molecules for Robust Drug Interaction Prediction” was accepted by NeurIPS 2025! The work uses molecular images for robust drug–drug interaction prediction. Published paper. |
| Feb 25, 2025 | 🎉 Our paper “Multi-Objective Molecular Design Through Learning Latent Pareto Set” was accepted by AAAI 2025! We propose a novel framework to approximate the entire Pareto set, empowering researchers to fine-tune molecular properties with ease. 🧪✨ Code |
| Aug 30, 2024 | 🎉 Our paper “DeepAMP A Foundation Model Identifies Broad-Spectrum Antimicrobial Peptides against Drug-Resistant Bacterial Infection” is published in Nature Communications! In this study, we developed deepAMP, a peptide language-based deep generative framework designed to expedite the discovery of potent, broad-spectrum antimicrobial peptides (AMPs). By employing a novel sequence degradation strategy and multi-stage fine-tuning, we identified 29 high-potential candidates. These peptides proved effective in treating P. aeruginosa skin wound infections in mouse models with excellent biocompatibility. Notably, the identified peptide T2-9 showed in vitro activity against E. coli matching that of the clinical antibiotic levofloxacin, with the same MIC of 2.93 μg/mL. |
| May 5, 2024 | 🎉 Our work HydrogelFinder (A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules) is accepted to Advanced Science. By leveraging non-peptidal small molecules to guide chemical space exploration, we’ve achieved a rapid 19-day workflow from generation to wet-lab validation. 🧪 Our model successfully identified 9 novel self-assembling peptides (1-10 amino acids), including the shortest lipid-peptide documented to date. PD-DP-008 (JIJ02), has entered Phase I clinical evaluation (CTR20250456). |