Journal & Article

Interdisciplinary Applications

I/F
# Journal I/F

Nature Machine Intelligence

Robot planning with LLMs Personalized uncertainty quantification in artificial intelligence A text-guided protein design framework. Optimal transport for generating transition states in chemical reactions Transparency (in training data) is what we want. Seeking visions for sustainable AI Physical benchmarks for testing algorithms: Causal AI. Bridging peptide presentation and T cell recognition with multi-task learning: Machine learning in immunology. Goals as reward-producing programs. Machine learning solutions looking for PDE problems Modern maxims for an AI oracle: Ai ethics. Learning from models beyond fine-tuning. Moving towards genome-wide data integration for patient stratification with Integrate Any Omics. The design space of E(3)-equivariant atom-centred interatomic potentials. Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry. Visual cognition in multimodal large language models. Causal chambers as a real-world physical testbed for AI methodology. Exploring scalable medical image encoders beyond text supervision. A unified evolution-driven deep learning framework for virus variation driver prediction. Investigating machine moral judgement through the Delphi experiment. Sequential memory improves sample and memory efficiency in episodic control ARNLE model identifies prevalence potential of SARS-CoV-2 variants Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules Evolutionary optimization of model merging recipes Author Correction: Kernel approximation using analogue in-memory computing What large language models know and what people think they know A quantitative analysis of knowledge-learning preferences in large language models in molecular science A machine learning approach to leveraging electronic health records for enhanced omics analysis Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning Discovering fully semantic representations via centroid- and orientation-aware feature learning The promise of generative AI for suicide prevention in India Preserving and combining knowledge in robotic lifelong reinforcement learning Why the carbon footprint of generative large language models alone will not help us assess their sustainability On the caveats of AI autophagy Towards a more inductive world for drug repurposing approaches Benchmarking AI-powered docking methods from the perspective of virtual screening Image-based generation for molecule design with SketchMol On board with COMET to improve omics prediction models: AI for healthcare data Rethinking machine unlearning for large language models Large language models that replace human participants can harmfully misportray and flatten identity groups Scalable and robust DNA-based storage via coding theory and deep learning Categorizing robots by performance fitness into the tree of robots Deep lead optimization enveloped in protein pocket and its application in designing potent and selective ligands targeting LTK protein Teaching robots to build simulations of themselves Large language models for scientific discovery in molecular property prediction A unified deep framework for peptide–major histocompatibility complex–T cell receptor binding prediction Data-driven federated learning in drug discovery with knowledge distillation Bridging the gap between machine confidence and human perceptions: Language models