---
title: "Nature Machine Intelligence 2024 — papers — Elamur Research"
description: "Nature Machine Intelligence 2024 — papers: 1 paper in the Elamur Research library, with our summaries, ranked by citations."
canonical: "https://research.elamur.ai/free/en/venues/nature-machine-intelligence/2024/"
---
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# Nature Machine Intelligence 2024 — papers

The works of Nature Machine Intelligence published in 2024 that our library holds (publisher: Nature Portfolio): 1 work, listed below with the most cited first; each has a page with our summary, its key points and its references.

[**178** works that year](#all) · [**1** work in our library](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/2024/#works) · [**392** citations of these works](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/2024/#works)

## Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. **[Augmenting large language models with chemistry tools ↗](https://doi.org/10.1038/s42256-024-00832-8)**Andres M Bran, Sam Cox, Oliver Tobias Schilter et al.*762 citations*
2. **[Leveraging large language models for predictive chemistry](https://md.research.elamur.ai/free/en/papers/jablonka-2024-leveraging-large-language-models-predictive/)**Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero et al.*367 citations · our summary*
3. **[Foundation model for cancer imaging biomarkers ↗](https://doi.org/10.1038/s42256-024-00807-9)**Suraj Pai, Dennis Bontempi, Ibrahim Hadžić et al.*195 citations*
4. **[Generative AI for designing and validating easily synthesizable and structurally novel antibiotics ↗](https://doi.org/10.1038/s42256-024-00809-7)**Kyle Swanson, Gary Liu, Denise B. Catacutan et al.*184 citations*
5. **[Factuality challenges in the era of large language models and opportunities for fact-checking](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/)**Isabelle Augenstein, Timothy J. Baldwin, Meeyoung Cha et al.*171 citations · our summary*
6. **[Machine learning-aided generative molecular design ↗](https://doi.org/10.1038/s42256-024-00843-5)**Yuanqi Du, Arian R. Jamasb, Jeff Guo et al.*169 citations*
7. **[Equivariant 3D-conditional diffusion model for molecular linker design ↗](https://doi.org/10.1038/s42256-024-00815-9)**Ilia Igashov, Hannes Stärk, Clément Vignac et al.*166 citations*
8. **[LLM-based agentic systems in medicine and healthcare ↗](https://doi.org/10.1038/s42256-024-00944-1)**Jianing Qiu, Kyle Lam, Guohao Li et al.*165 citations*
9. **[State-specific protein–ligand complex structure prediction with a multiscale deep generative model ↗](https://doi.org/10.1038/s42256-024-00792-z)**Zhuoran Qiao, Weili Nie, Arash Vahdat et al.*156 citations*
10. **[Laplace neural operator for solving differential equations ↗](https://doi.org/10.1038/s42256-024-00844-4)**Qianying Cao, Somdatta Goswami, George Em Karniadakis*153 citations*

## All works of the year (178)

The most cited first; 1 with our summary link to their page here, the others to the original.

1. **[Augmenting large language models with chemistry tools ↗](https://doi.org/10.1038/s42256-024-00832-8)**Andres M Bran, Sam Cox, Oliver Tobias Schilter et al.*762 citations*
2. **[Leveraging large language models for predictive chemistry](https://md.research.elamur.ai/free/en/papers/jablonka-2024-leveraging-large-language-models-predictive/)**Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero et al.*367 citations · our summary*
3. **[Foundation model for cancer imaging biomarkers ↗](https://doi.org/10.1038/s42256-024-00807-9)**Suraj Pai, Dennis Bontempi, Ibrahim Hadžić et al.*195 citations*
4. **[Generative AI for designing and validating easily synthesizable and structurally novel antibiotics ↗](https://doi.org/10.1038/s42256-024-00809-7)**Kyle Swanson, Gary Liu, Denise B. Catacutan et al.*184 citations*
5. **[Factuality challenges in the era of large language models and opportunities for fact-checking](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/)**Isabelle Augenstein, Timothy J. Baldwin, Meeyoung Cha et al.*171 citations · our summary*
6. **[Machine learning-aided generative molecular design ↗](https://doi.org/10.1038/s42256-024-00843-5)**Yuanqi Du, Arian R. Jamasb, Jeff Guo et al.*169 citations*
7. **[Equivariant 3D-conditional diffusion model for molecular linker design ↗](https://doi.org/10.1038/s42256-024-00815-9)**Ilia Igashov, Hannes Stärk, Clément Vignac et al.*166 citations*
8. **[LLM-based agentic systems in medicine and healthcare ↗](https://doi.org/10.1038/s42256-024-00944-1)**Jianing Qiu, Kyle Lam, Guohao Li et al.*165 citations*
9. **[State-specific protein–ligand complex structure prediction with a multiscale deep generative model ↗](https://doi.org/10.1038/s42256-024-00792-z)**Zhuoran Qiao, Weili Nie, Arash Vahdat et al.*156 citations*
10. **[Laplace neural operator for solving differential equations ↗](https://doi.org/10.1038/s42256-024-00844-4)**Qianying Cao, Somdatta Goswami, George Em Karniadakis*153 citations*
11. **[A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas ↗](https://doi.org/10.1038/s42256-024-00868-w)**Rui Jiang, Xiaoxu Yin, Pengshuai Yang et al.*146 citations*
12. **[A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions ↗](https://doi.org/10.1038/s42256-024-00823-9)**Yanyi Chu, Dan Yu, Yupeng Li et al.*139 citations*
13. **[Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves ↗](https://doi.org/10.1038/s42256-023-00780-9)**Arvin Tashakori, Zenan Jiang, Amir Servati et al.*135 citations*
14. **[Predicting equilibrium distributions for molecular systems with deep learning ↗](https://doi.org/10.1038/s42256-024-00837-3)**Shuxin Zheng, Jiyan He, Chang Liu et al.*134 citations*
15. **[Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning ↗](https://doi.org/10.1038/s42256-024-00836-4)**Ning Wang, Jiang Bian, Yuchen Li et al.*132 citations*
16. **[Protein function prediction as approximate semantic entailment ↗](https://doi.org/10.1038/s42256-024-00795-w)**Maxat Kulmanov, Francisco J. Guzmán‐Vega, Paula D Duek et al.*124 citations*
17. **[Autonomous 3D positional control of a magnetic microrobot using reinforcement learning ↗](https://doi.org/10.1038/s42256-023-00779-2)**Sarmad Ahmad Abbasi, Awais Ahmed, Seungmin Noh et al.*115 citations*
18. **[The benefits, risks and bounds of personalizing the alignment of large language models to individuals ↗](https://doi.org/10.1038/s42256-024-00820-y)**Hannah Rose Kirk, Bertie Vidgen, Paul Röttger et al.*111 citations*
19. **[Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations ↗](https://doi.org/10.1038/s42256-024-00897-5)**Nick McGreivy, Ammar H. Hakim*109 citations*
20. **[Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data ↗](https://doi.org/10.1038/s42256-024-00847-1)**Huan Yee Koh, Anh T. N. Nguyen, Shirui Pan et al.*106 citations*
21. **[Guidelines for ethical use and acknowledgement of large language models in academic writing ↗](https://doi.org/10.1038/s42256-024-00922-7)**Sebastian Porsdam Mann, Anuraag A Vazirani, Mateo Aboy et al.*105 citations*
22. **[DNA language model GROVER learns sequence context in the human genome ↗](https://doi.org/10.1038/s42256-024-00872-0)**Melissa Sanabria, Jonas Hirsch, Pierre M. Joubert et al.*97 citations*
23. **[Machine learning for micro- and nanorobots ↗](https://doi.org/10.1038/s42256-024-00859-x)**Lidong Yang, Jialin Jiang, Fengtong Ji et al.*97 citations*
24. **[A neural speech decoding framework leveraging deep learning and speech synthesis ↗](https://doi.org/10.1038/s42256-024-00824-8)**Xupeng Chen, Ran Wang, Amirhossein Khalilian-Gourtani et al.*95 citations*
25. **[Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging ↗](https://doi.org/10.1038/s42256-024-00912-9)**Sergios Gatidis, Marcel Früh, Matthias Philipp Fabritius et al.*95 citations*

[See all 178 papers in Nature Machine Intelligence 2024 — papers →](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/2024/papers/)

## Works with our summary in our library (1)

The most cited first (OpenAlex citations plus our library's).

1. **[Leveraging large language models for predictive chemistry](https://md.research.elamur.ai/free/en/papers/jablonka-2024-leveraging-large-language-models-predictive/) [Nature Machine Intelligence](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/)**Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, et al.*2024 · 392 citations*

## Top topics

Topics of its works in our library, with the number of works on each.

[Language models & pretraining (1)](https://md.research.elamur.ai/free/en/topics/language-models/)

## Fields

[Chemistry (1)](https://md.research.elamur.ai/free/en/shelves/chemistry/) · [Computer Science (1)](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Materials Science (1)](https://md.research.elamur.ai/free/en/shelves/materials-science/)

## Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

[🧑‍🔬**Philippe Schwaller**1 work here · 392 citations](https://md.research.elamur.ai/free/en/authors/philippe-schwaller/)

## Top institutions

Institutions on the bylines of its works here, by the number of works.

[🎓**École Polytechnique Fédérale de Lausanne** Lausanne · Switzerland 1 work here](https://md.research.elamur.ai/free/en/universities/ecole-polytechnique-federale-de-lausanne/) · [🎓**Friedrich Schiller University Jena** Jena · Germany 1 work here](https://md.research.elamur.ai/free/en/universities/friedrich-schiller-university-jena/)

works and citations from OpenAlex and our library.

- [← previous Nature Machine Intelligence 2023](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/2023/)
- [↑ back to Nature Machine Intelligence](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/)