What AI-Related Papers Do Nature and Science Publish?

Created by: Zifeng Wang Last Updated: 2025-11-05

TL;DR: In 2025, Artificial Intelligence has transcended its role as a computational tool, fundamentally reshaping the scientific method across diverse frontier research domains, from molecular biology and medicine to climate modeling and materials science, by enabling unprecedented data analysis, accelerating discovery, and unlocking previously unanswerable questions.

AI #ScientificDiscovery #Nature #Science #DeepLearning #Biomedicine #ClimateAI #MaterialsScience #FusionEnergy #DrugDiscovery #Genomics #Neuroscience #AIHardware #EthicalAI

❓ The Big Questions

The integration of AI into scientific research, as evidenced by publications in Nature and Science in 2025, raises several pivotal questions that are redefining the landscape of discovery:

How is AI fundamentally reshaping the scientific method across diverse domains? This question probes the shift from traditional hypothesis-driven, experimental approaches to increasingly data-driven, AI-accelerated discovery cycles, where AI not only analyzes but also generates hypotheses, designs experiments, and interprets complex outcomes (e.g., Qiao, 2025; Spears et al., 2025; Wang et al., 2025).
To what extent can AI models genuinely "reason" or "discover" new scientific principles, rather than merely optimizing or predicting based on existing data? This delves into the philosophical and practical implications of Large Reasoning Models (LRMs) and generative AI, questioning the nature of AI-driven insights and whether they challenge or merely extend human scientific understanding (Mitchell, 2025; De Kai, 2025).
What are the critical challenges in ensuring the interpretability, reproducibility, and ethical governance of AI-driven scientific discoveries? With the increasing complexity of AI models, concerns around "black-box" predictions, data biases, and the need for standardized benchmarking, transparency, and accountability are paramount for building trust and facilitating clinical and societal translation (Mahmood, 2025; Bommasani et al., 2025; Camps-Valls et al., 2025).
How can AI be leveraged to accelerate solutions for grand societal challenges, such as climate change, disease, and sustainable materials, and what new scientific questions does this unlock? This focuses on AI's transformative potential in areas like drug discovery (Wang et al., 2025), climate modeling (Price et al., 2025; Roobaert, 2025), and materials science (Riebesell et al., 2025), pushing the boundaries of what is scientifically possible and revealing new avenues for inquiry.

🔬 The Ecosystem

The scientific landscape of AI-driven discovery in 2025 is characterized by a vibrant interplay of leading institutions, pioneering researchers, and groundbreaking publications:

Key Researchers & Institutions:

Google DeepMind (Price et al., 2025; Silver D. et al., 2025; Topol, 2025) remains a dominant force, particularly in applying AI to complex scientific problems like weather forecasting (GenCast) and protein science (AlphaFold 3). Their work on AMIE also demonstrates significant strides in medical diagnostic AI.
Scripps Research (Topol, 2018; Topol, 2025) and institutions like Stanford University (Bommasani et al., 2025; Wang et al., 2025; Zaslavsky et al., 2025) are at the forefront of AI in biomedicine, from diagnostics to molecular biology and infectious disease modeling. Eric J. Topol's contributions consistently highlight AI's transformative impact on healthcare.
Lawrence Livermore National Laboratory (Spears et al., 2025) showcases AI's critical role in high-stakes physics research, specifically fusion ignition, demonstrating the power of physics-informed AI.
Microsoft Research (Lewis et al., 2025) is advancing generative AI for protein conformational ensembles, pushing the boundaries of computational biology.
Mass General Brigham, Harvard Medical School, and the Broad Institute (Mahmood, 2025; Jaganathan et al., 2025) are key players in applying AI to genomics and addressing the crucial issue of benchmarking in biomedical AI.
Caltech (Nair, 2025) is making significant strides in neuroscience, using interpretable ML to unravel complex neural codes.
University of Cambridge (Ghahramani, 2015) stands out for foundational work in probabilistic machine learning, which underpins much of the uncertainty quantification in modern AI applications.
EPFL (Marchand et al., 2024) and University of California, San Francisco (Guo et al., 2025) are leading in AI-driven protein engineering and de novo design.
Various European institutions (Camps-Valls et al., 2025; Roobaert, 2025) are collaborating on AI for climate modeling and extreme weather understanding, addressing global challenges.

Key Papers & Breakthroughs:

AlphaFold 3 & Boltz-1 (Topol, 2025; Qiao, 2025) represent a monumental leap in molecular biology, demonstrating AI's ability to predict biomolecular structures and interactions with unprecedented accuracy, effectively "rewiring life's interactome."
GenCast (Price et al., 2025) from Google DeepMind revolutionizes weather forecasting by outperforming traditional ensemble models in accuracy and speed, especially for extreme events, using diffusion models.
AMIE (Silver D. et al., 2025) from Google Research showcases an LLM that surpasses human physicians in diagnostic accuracy and empathy in simulated medical dialogues, signaling a new era for AI in clinical settings.
Physics-informed Deep Learning for Fusion Ignition (Spears et al., 2025) at LLNL highlights AI's predictive power in extreme physics, guiding complex experiments.
PromoterAI (Jaganathan et al., 2025) and PAMmla (unknown, 2025) demonstrate AI's capacity to decode non-coding genomic elements and engineer biological systems with precision, respectively, impacting rare disease diagnosis and CRISPR technology.
MaSIF-neosurf (Marchand et al., 2024) and BioEmu (Lewis et al., 2025) represent significant advances in protein engineering, enabling de novo design of dynamic proteins and scalable emulation of protein ensembles.
InterpolAI (Joshi et al., 2025) pushes the boundaries of biomedical imaging, restoring and enhancing 3D tissue mapping with advanced optical flow models.
The discussions around Large Reasoning Models (LRMs) (Mitchell, 2025) and Ethical AI Development (De Kai, 2025; Bommasani et al., 2025) underscore the growing maturity and self-reflection within the AI community regarding its capabilities and societal responsibilities.

This ecosystem demonstrates a clear trend: AI is not just a tool for automation but a partner in scientific inquiry, generating novel insights and accelerating discovery across a breathtaking array of disciplines.

🎯 Who Should Care & Why

The transformative impact of AI on scientific research in 2025 extends its relevance across a broad spectrum of stakeholders:

Scientists and Researchers (All Disciplines): Researchers across every scientific field, from molecular biologists (Topol, Qiao, Guo, Lewis, Jaganathan, Wang et al.) to glaciologists (Wang et Lai et al.), climate scientists (Roobaert, Price et al., Camps-Valls et al.), materials scientists (Riebesell et al.), and neuroscientists (Nair), should care because AI is fundamentally reshaping their methodologies. It offers unprecedented capabilities for data analysis, hypothesis generation, experimental design, and simulation acceleration, enabling discoveries previously thought impossible. Ignoring these advancements risks falling behind the rapid pace of AI-driven innovation.
Funding Agencies and Policymakers: Organizations like the NIH, NSF, DOE, and government bodies (Bommasani et al., 2025) need to understand AI's potential to strategically invest in AI-integrated research, develop ethical guidelines, and establish robust regulatory frameworks. AI's capacity to accelerate solutions for grand challenges (e.g., climate change, disease) necessitates informed policy to maximize benefits while mitigating risks. The "benchmarking crisis" in biomedicine (Mahmood, 2025) explicitly calls for their intervention.
Healthcare Professionals and Pharmaceutical Industry: The medical community and drug developers (Topol, Qiao, Wang et al., Silver D. et al., Zaslavsky et al., Mahmood) are witnessing a revolution in diagnostics, drug discovery, and personalized medicine. AI-powered diagnostic tools (e.g., AMIE, POCT, Mal-ID) promise higher accuracy and accessibility, while generative AI is designing novel therapeutic agents (e.g., AMPs, dynamic proteins). This means faster, more effective treatments and improved patient outcomes.
Technology Developers and AI Researchers: Those at the forefront of AI methodology (Mitchell, Ghahramani, Ahmed et al.) should care deeply. The demand for more robust, interpretable, and efficient AI systems, including novel architectures, reasoning capabilities, and hardware (e.g., photonic AI processors), is accelerating. The challenge of ethical AI development (De Kai, 2025) underscores the need for responsible innovation.
Environmental Scientists and Conservationists: AI is providing critical tools for monitoring and understanding the planet. From mapping coastal carbon cycles (Roobaert) to tracking illegal fishing (Raynor et al.) and predicting extreme weather (Price et al., Camps-Valls et al.), AI offers powerful capabilities for environmental protection and climate resilience. This enables more effective conservation strategies and climate adaptation planning.
Educators and Students: The integration of AI into scientific research means a fundamental shift in scientific training. Future scientists will need strong computational skills, an understanding of AI principles, and the ability to critically evaluate AI-generated insights. Curricula must adapt to prepare the next generation for AI-augmented discovery.
The General Public: Ultimately, everyone benefits from accelerated scientific discovery. AI's contributions to medicine, climate solutions, and new materials will lead to a healthier, more sustainable, and technologically advanced future. Understanding the role of AI in these breakthroughs fosters informed public discourse and trust in scientific progress.

In essence, AI is no longer a niche tool but a pervasive force driving scientific progress, making its understanding and responsible development critical for anyone invested in the future of humanity and the planet.

✍️ My Take

The year 2025, as depicted through this curated collection of Nature and Science publications, marks a profound inflection point in the scientific method. AI has moved beyond mere data processing; it is now an active, generative, and often indispensable partner in discovery, fundamentally reshaping how scientific questions are asked, hypotheses are formed, experiments are designed, and insights are derived.

One striking trend is the democratization of complex scientific analysis. Fields traditionally constrained by the sheer volume and complexity of data, such as molecular biology (Topol, Qiao), genomics (Jaganathan et al.), and climate science (Price et al., Roobaert), are witnessing unprecedented breakthroughs. AI's ability to discern subtle patterns in massive, heterogeneous datasets—from millions of protein structures to decades of reanalysis weather data or gigabytes of neural recordings—is unlocking knowledge that was previously inaccessible to human cognition or traditional statistical methods. The success of AlphaFold-like models (Topol, Qiao) in structural biology is a testament to this, propelling drug discovery and protein engineering into a new era of rational design (Guo et al., Marchand et al., Wang et al.).

However, this revolution is not without its nuances and challenges. The debate surrounding whether AI genuinely "reasons" or merely "mimics reasoning" (Mitchell, 2025) highlights a critical epistemological question. While Large Reasoning Models (LRMs) are demonstrating impressive problem-solving capabilities in math and science, the scientific community is grappling with the interpretability of these "black box" models. The call for "explainable AI" (XAI) and "physics-informed" models (Spears et al., Wang et Lai et al.) is a direct response to this, emphasizing the need for AI systems whose outputs can be understood and trusted by human experts. This isn't just about transparency; it's about fostering collaboration between human intuition and AI's computational power, ensuring that AI-generated insights are not just accurate but also mechanistically comprehensible.

Another crucial aspect emerging from these papers is the imperative for robust benchmarking and ethical governance. The "benchmarking crisis" in biomedicine (Mahmood, 2025) underscores a systemic issue: without standardized, transparent, and clinically meaningful evaluation frameworks, the impressive performance metrics reported by AI models risk remaining confined to academic papers rather than translating into real-world impact. Similarly, the discussions around "evidence-based AI policy" (Bommasani et al., 2025) and "raising AI" ethically (De Kai, 2025) reflect a growing recognition that AI's transformative power demands proactive, responsible development, especially in sensitive domains like healthcare (Silver D. et al., Zaslavsky et al.) and public health (Kraemer et al.). This includes addressing data biases, ensuring privacy, and establishing accountability for AI's decisions.

Looking ahead, I anticipate several key directions:

Hybrid AI-Human Intelligence: The future of scientific discovery will likely involve increasingly sophisticated "AI-augmented" scientists. AI will handle data synthesis, hypothesis generation, and simulation, allowing humans to focus on higher-level conceptualization, experimental validation, and ethical oversight. This synergy will accelerate discovery exponentially.
Autonomous Scientific Labs: We are moving towards laboratories where AI not only designs experiments but also controls robotic systems to execute them, analyze results, and iteratively refine hypotheses—minimal human intervention required. This "self-driving science" could dramatically compress research timelines.
Foundation Models for Science: Just as large language models have transformed NLP, we will see the emergence of "foundation models for science" that are pre-trained on vast repositories of scientific data (e.g., chemical reactions, protein structures, climate simulations). These models will serve as powerful general-purpose scientific assistants, adaptable to a multitude of tasks.
Hardware-Software Co-design: The demand for AI acceleration will drive innovation in specialized hardware, as exemplified by photonic AI processors (Ahmed et al.). This co-evolution of AI algorithms and hardware will be critical for scaling AI's capabilities to tackle even more complex scientific problems.
Focus on Causality and Interpretability: As AI's predictive power grows, so too will the need to understand why it makes certain predictions. Future research will increasingly focus on causal inference, mechanistic interpretability, and methods that allow scientists to extract actionable, theory-building insights from AI models, rather than just black-box answers.

In conclusion, 2025 is a testament to AI's burgeoning role as a scientific partner. The papers surveyed here paint a picture of a future where scientific discovery is faster, more insightful, and more comprehensive, driven by the intelligent analysis and generation capabilities of AI. The challenge, and indeed the opportunity, lies in harnessing this power responsibly, ensuring that AI serves to amplify human scientific endeavor for the betterment of society.

📚 The Reference List

Paper	Author(s)	Year	Data Used	Method Highlight	Core Contribution
High-performance medicine: the convergence of human and artificial intelligence	Eric J. Topol	2018	Millions of labeled medical images, EHRs, genomic data	Deep neural networks, CNNs, reinforcement learning	Comprehensive review of AI in medicine, emphasizing transformation of diagnostics and health systems.
AI in Scientific Discovery: A Review of 2025 Publications in Nature and Science	Unknown	2023	Scientific literature, experimental data, simulated data	Self-supervised learning, geometric deep learning, generative models	Review highlighting AI's role in hypothesis generation, data analysis, and solving complex scientific problems across disciplines.
Probabilistic machine learning and artificial intelligence	Zoubin Ghahramani	2015	Various scientific measurements, gene expression, sequential data	Probabilistic programming, Bayesian inference, Gaussian processes	Review of probabilistic ML, emphasizing uncertainty handling for robust AI and scientific data analysis.
Advancing science- and evidence-based AI policy	Rishi Bommasani et al.	2025	N/A (policy framework)	Policy mechanisms for evidence generation, certification, synthesis	Advocacy for evidence-based AI policy to ensure responsible AI governance.
AI to rewire life’s interactome: Structural foundation models help to elucidate and reprogram molecular biology	Zhuoran Qiao	2025	Experimentally determined structures, multiple sequence alignments, molecular conformations	Generative models, foundation models, diffusion models, geometric deep learning	AI-driven structural biology models revolutionize understanding of biomolecular interactions and design.
Raising AI: An Essential Guide to Parenting Our Future	De Kai	2025	N/A (conceptual, ethical framework)	Hybrid architectures (ML + symbolic reasoning)	Advocates for developing ethical, mindful AI through hybrid architectures and responsible 'parenting'.
Learning the language of life with AI	Eric J. Topol	2025	>200 million protein structures, 2.7 million genomes, billions of nucleotides	Large language models (LLMs), foundation models (e.g., AlphaFold 3, Boltz-1)	Review of foundation models in life sciences, accelerating understanding of biomolecular structures and functions.
Mapping the global coastal ocean with AI: Artificial neural networks can help better constrain the global carbon cycle in shallow seas	Alizée Roobaert	2025	18 million in situ CO2 observations, satellite data, reanalysis data	Self-organizing map, feedforward neural network	AI-based neural networks accurately estimate coastal CO2 exchange, improving climate modeling.
Predicting fusion ignition at the National Ignition Facility with physics-informed deep learning	Brian K. Spears et al.	2025	150,000 radiation hydrodynamics simulations, NIF experimental data	Physics-informed deep neural network, Bayesian inference, transfer learning	Physics-informed AI model accurately predicts fusion ignition probability and optimizes experiments.
Unraveling the emergent chorus of the mind: Machine learning reveals how a hidden neural code orchestrates diverse emotion states	Aditya Nair	2025	Experimental neural recordings from mouse brain circuits	Interpretable machine learning, state-space modeling, generative models	AI decodes neural ensemble activity to uncover a hidden neural code for affective states, validated by optogenetics.
Deep learning identifies promoter variants that affect gene expression and contribute to rare disease diagnosis	Jaganathan et al.	2025	ENCODE, FANTOM5 (502 datasets), GTEx, MPRA, gnomAD, GEL clinical cohorts	Convolutional deep neural network with MetaFormer blocks	PromoterAI predicts functional impact of noncoding promoter variants for rare disease diagnosis.
Deep learning the flow law of Antarctic ice shelves	Yongji Wang, Ching-Yao Lai, David J. Prior, Charlie Cowen-Breen	2025	Remote-sensing data of ice shelf velocities and thicknesses	Physics-informed neural networks (PINNs)	PINNs infer complex, heterogeneous rheology of Antarctic ice shelves, improving climate models.
Design of dynamic proteins with controllable motions, mimicking mechanisms in natural signaling switches	Guo et al.	2025	PDB structural data, AlphaFold2 predictions, experimental NMR data	AlphaFold2, ProteinMPNN, masked language models (Frame2seq)	Deep learning-guided de novo design of proteins with controllable conformational switching.
Massive experimental analysis of protein sequence space reveals principles of amyloid aggregation	M. Seuma et al.	2025	Experimental aggregation scores for >100,000 random 20-residue peptides	Convolution-attention neural network (CANYA)	AI model (CANYA) predicts amyloid aggregation from sequence, revealing physicochemical motifs.
Rapidly self-healing electronic skin for machine learning–assisted physiological and movement evaluation	Yongju Lee et al.	2025	sEMG signals from 21 human subjects	Convolutional neural network (CNN)	Self-healing E-Skin with AI for real-time muscle fatigue classification (>95% accuracy).
Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides	Yeji Wang et al.	2025	UniProt, AMP databases, non-AMP sequences (millions)	Latent diffusion models, variational autoencoders, molecular dynamics	AI pipeline designs novel, diverse, and potent antimicrobial peptides with experimental validation.
Little-to-no industrial fishing occurs in fully and highly protected marine areas	Jennifer Raynor et al.	2025	Satellite data (AIS, SAR), geospatial boundary datasets	Machine learning algorithms for geospatial data	AI and satellite data monitor industrial fishing, finding minimal activity in highly protected MPAs.
From blood to disease classification with immune receptor sequencing	Zaslavsky et al.	2025	BCR and TCR sequences from COVID-19, HIV, lupus, T1D patients, healthy controls (>39 million clones)	Protein language models (ESM-2), clustering, logistic regression, random forests	Mal-ID: ML framework for immune receptor sequencing to classify multiple immune-related diseases.
Artificial intelligence learns to reason	Melanie Mitchell	2025	Large corpora of human-generated text, reasoning examples	Large Language Models (LLMs), chain-of-thought prompting, reinforcement learning	Review of Large Reasoning Models (LRMs) and their ability to generate human-like reasoning chains in AI.
InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping	Saurabh Joshi et al.	2025	Histology, ssTEM, light-sheet microscopy, MRI images (hundreds to thousands)	Deep learning-based optical flow estimation (FILM adaptation)	InterpolAI accurately interpolates and restores damaged biomedical images for enhanced 3D tissue mapping.
De novo design of protein interactions with learned surface fingerprints	Anthony Marchand et al.	2024	PDB structural data, protein-protein interfaces, ligand-induced complexes	Geometric deep learning, surface fingerprints, neural network classifiers	MaSIF-neosurf framework for de novo design of protein interactions targeting neosurfaces.
Machine learning-assisted wearable sensing systems for speech recognition and interaction	Tao Liu et al.	2025	Speech data from participants (spectrograms)	Deep learning models (ResNet, residual neural networks)	Wearable flexible acoustic sensor with deep learning for robust speech recognition in noisy environments.
Scalable emulation of protein equilibrium ensembles with generative deep learning	Sarah Lewis et al.	2025	MD simulation data (~200 ms), AlphaFold structures, experimental stability data	Generative diffusion-based deep learning model (BioEmu)	BioEmu accurately and rapidly emulates protein conformational ensembles, accelerating understanding of protein function.
Probabilistic weather forecasting with machine learning	Ilan Price et al.	2025	40 years of ERA5 reanalysis data (0.25° resolution)	Conditional diffusion generative model with graph-transformer architecture (GenCast)	GenCast: ML model for probabilistic weather forecasting, outperforming traditional ensemble forecasts.
Artificial intelligence for modeling and understanding extreme weather and climate events	Gustau Camps-Valls et al.	2025	Earth observation data, reanalysis, climate model outputs	Deep learning (CNNs, transformers), Explainable AI (XAI), causal inference	Review of AI applications for detecting, predicting, and understanding extreme weather and climate events.
Transforming healthcare diagnostics with AI-powered point-of-care testing: recent advances and future prospects	Unknown	2025	Thousands of real-world images of test strips, clinical samples, simulated data	CNNs, Vision Transformers (ViT), SVMs, RNNs, LSTMs, deep learning	Review of AI-enhanced POCT devices for improved diagnostic accuracy and accessibility.
A benchmarking crisis in biomedical machine learning	Faisal Mahmood	2025	Proprietary clinical data, publicly available datasets	N/A (focus on evaluation frameworks)	Highlights the critical need for standardized, transparent, and clinically meaningful benchmarks in biomedical AI.
Scalable characterization of the PAM requirements of CRISPR–Cas enzymes using HT-PAMDA	Unknown	2025	HT-PAMDA results for 634 engineered SpCas9 enzymes	Neural network model (PAMmla)	PAMmla predicts PAM specificity from amino acid sequences, enabling design of bespoke Cas9 enzymes.
Transforming infectious disease epidemiology with artificial intelligence	Moritz U. G. Kraemer et al.	2025	Surveillance data, genomic sequences, mobility data, environmental data	Machine learning, computational statistics, information retrieval, foundation models	Review of AI's role in infectious disease modeling, outbreak forecasting, and public health decision-making.
Predicting gene expression from DNA sequence using deep learning models	Lucía Barbadilla-Martínez et al.	2025	Epigenome mapping data, high-throughput reporter assays, genomic sequences	Convolutional neural networks (CNNs), transformer architectures	Review of deep learning models for predicting gene expression from DNA sequences and understanding regulatory grammar.
Universal photonic artificial intelligence acceleration	Ahmed, S.R., Baghdadi, R., Bernadskiy, M., et al.	2025	ImageWoof, language tasks, reinforcement learning tasks	Photonic AI processor executing ResNet, BERT, Atari DQN	Photonic AI processor achieving near-digital precision and high throughput for AI acceleration.
Towards accurate differential diagnosis with large language models	Silver D. et al.	2025	Real-world medical dialogue transcripts, MedQA, long-form reasoning datasets (>98,000 conversations)	Large language model (LLM) (fine-tuned PaLM 2), chain-of-reasoning, self-play (AMIE)	AMIE: LLM optimized for diagnostic medical dialogue, outperforming physicians in accuracy and empathy.
A framework to evaluate machine learning crystal stability predictions	Janosh Riebesell et al.	2025	Materials Project database (~154,000 structures), WBM dataset (~215,000 structures)	Random forests, graph neural networks, one-shot predictors, Bayesian optimizers, universal interatomic potentials	Matbench Discovery: Benchmarking framework for ML models in materials discovery, highlighting UIPs for crystal stability.

Originally generated on 2025-11-05 22:29:53

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