This page shows the most relevant public items for Drug Discovery, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • May 20, 2025 • arXiv • Lilian W. Zhang, Michael O. Fasanya, Hiroshi Tanaka
The traditional drug discovery pipeline is hindered by siloed computational predictions and slow, manual wet-lab validation. We present a fully autonomous, closed-loop Agentic AI system that seamle...
Paper • May 8, 2024 • Nature • Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, John Jumper
The interactions of proteins with other molecules, such as DNA, RNA, ligands and ions, are fundamental to biological function. Here we describe AlphaFold 3, a model that can predict the joint three...
Paper • Feb 20, 2026 • bioRxiv • L. Zheng, J. Drucker, A. Chen, W. Wang
Designing therapeutic peptides requires balancing binding affinity with complex ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, a task that overwhelms traditional ...
Paper • Feb 23, 2026 • bioRxiv • Zhang, X., Jeong, E., Yan, C., Feng, Y., Lyu, L., Guo, X., Chen, Y.
CellAwareGNN is a new graph neural network (GNN) foundation model that integrates single-cell enhanced knowledge graphs for drug indication prediction. By modeling the complex relationships between...
Paper • Feb 23, 2026 • bioRxiv • Zhang, X., Jeong, E., Yan, C., Feng, Y.
Predicting novel drug indications requires synthesizing vast, heterogeneous networks of gene, disease, and chemical data. We upgrade the CellAwareGNN foundation model into a multi-agent swarm archi...
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