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 4/1/2026
Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Em...
arXiv:2603.26713v1 Announce Type: new Abstract: Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous d...
paperA Comparative Investigation of Thermodynamic Structure-Informed Neural Networks
arXiv:2603.26803v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) offer a unified framework for solving both forward and inverse problems of differential equations, yet their...
paperPiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning
arXiv:2603.26816v1 Announce Type: new Abstract: High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcement...
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.
JobsOpportunities
ML research roles at biotech companies, pharma, and academic labs.