AI for Scientific Discovery
Curated tools, papers, and opportunities. No hype—just what actually works.
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View all →50 papers from arXiv & bioRxiv · Updated 4/14/2026
Distilling Genomic Models for Efficient mRNA Representation Learning via Embe...
arXiv:2604.08574v1 Announce Type: new Abstract: Large Genomic Foundation Models have recently achieved remarkable results and in-vivo translation capabilities. However these models quickly grow to ...
paperMolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular ...
arXiv:2604.08575v1 Announce Type: new Abstract: Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these ob...
paperFluidFlow: a flow-matching generative model for fluid dynamics surrogates on ...
arXiv:2604.08586v1 Announce Type: new Abstract: Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query application...
paperAlphaFold3: Accurate structure prediction of biomolecular interactions
DeepMind releases updated model handling proteins, DNA, RNA, ligands, and modifications.
toolChai-1: Open foundation model for molecular structure prediction
Multi-modal foundation model achieving state-of-the-art on drug-target interaction benchmarks.
newsRecursion acquires Exscientia for $688M in AI drug discovery consolidation
Major merger signals maturation of AI-native drug discovery platforms.
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Research Papers
Curated papers from arXiv, bioRxiv, and journals with reproducible code and real-world impact.
ToolsModels & Tools
Open-source and commercial tools for drug discovery, protein design, and scientific computing.
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ML research roles at biotech companies, pharma, and academic labs.