AI for Scientific Discovery
Curated tools, papers, and opportunities. No hype—just what actually works.
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View all →60 papers from arXiv & bioRxiv · Updated 2/3/2026
Measurement for Opaque Systems: Multi-source Triangulation with Interpretable...
arXiv:2602.00022v1 Announce Type: new Abstract: We propose a measurement framework for difficult-to-access contexts that uses indirect data traces, interpretable machine-learning models, and theory...
paperRepresentation Learning Enhanced Deep Reinforcement Learning for Optimal Oper...
arXiv:2602.00027v1 Announce Type: new Abstract: Hydrogen-based multi-energy systems (HMES) have emerged as a promising low-carbon and energy-efficient solution, as it can enable the coordinated ope...
paperSheaf Neural Networks and biomedical applications
arXiv:2602.00159v1 Announce Type: new Abstract: The purpose of this paper is to elucidate the theory and mathematical modelling behind the sheaf neural network (SNN) algorithm and then show how SNN...
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.
Categories
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.
JobsOpportunities
ML research roles at biotech companies, pharma, and academic labs.