Emanuele Rossi

I am currently working as a Machine Learning Researcher at Vant AI, where I focus on generative models for structural biology and drug discovery. Our team recently introduced Neo-1, an all-atom multimodal and multi-task latent diffusion model that achieves state-of-the-art performance in both structure prediction and de-novo generation of biomolecules.

I earned my PhD from Imperial College London, working on Graph Neural Networks under the supervision of Prof. Michael Bronstein. During my PhD, I also worked as a Machine Learning Researcher at Twitter, focusing on both foundamental research and applied product problems. Prior to that, I was part of Fabula AI, a startup creating deep learning technology for fake news detection, subsequently acquired by Twitter in 2019. My educational background includes an undergraduate degree from Imperial College London and a master’s degree from the University of Cambridge, both in Computer Science.

Outside of machine learning, you’ll find me scuba diving, hiking trails, or simply enjoying quiet moments in nature.

News

Mar 21, 2025	Excited to finally share what we’ve been working on at Vant AI for the past year and a half: Neo-1, a unified model for all-atom structure prediction and generation of all biomolecules 🔬
Jul 19, 2024	The lack of large, high-quality datasets and robust evaluation is holding back ML in Drug Discovery. We are releasing Pinder (Protein-Protein) and Plinder (Protein-Ligand) to help bridge this gap and drive meaningful progress 🧬
Jan 16, 2024	I’m thrilled to announce that I’ve joined Vant AI as a Machine Learning Researcher. Vant combines a compelling ML vision with a proprietary data generation platform, focusing on the novel field of molecular glues. I’ll be developing generative models for structural biology to advance the drug discovery process 🚀
Jul 31, 2023	We are excited to release the Temporal Graph Benchmark, a collection of seven realistic, large-scale and diverse benchmarks for learning on temporal graphs! The accompanying paper has been accepted to NeurIPS 2023 Datasets and Benchmark track 📝
Jun 08, 2023	Our new paper on Graph Neural Networks for Directed Graphs, and how they improve learning on heterophilic graph, is out along with its associated blog post 📝

Selected Publications

bioRxiv

PINDER: The Protein Interaction Dataset and Evaluation Resource

Daniel Kovtun, Mehmet Akdel, Alexander Goncearenco, and 16 more authors

bioRxiv, 2024

Bib Code Docs

@article{kovtun_pinder_2024,
  title = {PINDER: The Protein Interaction Dataset and Evaluation Resource},
  author = {Kovtun, Daniel and Akdel, Mehmet and Goncearenco, Alexander and Zhou, Guoqing and Holt, Graham and Baugher, David and Lin, Dejun and Adeshina, Yusuf and Castiglione, Thomas and Wang, Xiaoyun and Marquet, Céline and McPartlon, Matt and Geffner, Tomas and Corso, Gabriele and Stärk, Hannes and Carpenter, Zachary and Kucukbenli, Emine and Bronstein, Michael and Naef, Luca},
  year = {2024},
  journal = {bioRxiv},
  biorxiv = {2024.07.17.603980v4},
  docs = {https://pinder-org.github.io/pinder},
}

bioRxiv

PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource

Janani Durairaj, Yusuf Adeshina, Zhonglin Cao, and 20 more authors

ICML ML for Life and Material Science Workshop, 2024

Bib Code Dataset Docs

@article{durairaj_plinder_2024,
  title = {PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource},
  author = {Durairaj, Janani and Adeshina, Yusuf and Cao, Zhonglin and Zhang, Xuejin and Oleinikovas, Vladas and Duignan, Thomas and McClure, Zachary and Robin, Xavier and Kovtun, Danny and Rossi, Emanuele and Zhou, Guoqing and Veccham, Srimukh and Isert, Clemens and Peng, Yuxing and Sundareson, Prabindh and Akdel, Mehmet and Corso, Gabriele and Stärk, Hannes and Carpenter, Zachary and Bronstein, Michael and Kucukbenli, Emine and Schwede, Torsten and Naef, Luca},
  year = {2024},
  journal = {ICML ML for Life and Material Science Workshop},
  biorxiv = {2024.07.17.603980v4},
  dataset = {https://console.cloud.google.com/storage/browser/plinder},
  docs = {https://plinder-org.github.io/plinder},
}

NeurIPS

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, and 7 more authors

Advances in Neural Information Processing Systems, 2023

arXiv Bib Code Website

@article{tgb_huang_2023,
  title = {Temporal Graph Benchmark for Machine Learning on Temporal Graphs},
  author = {Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael M. and Rabusseau, Guillaume and Rabbany, Reihaneh},
  year = {2023},
  journal = {Advances in Neural Information Processing Systems},
}

arXiv

Edge Directionality Improves Learning on Heterophilic Graphs

Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, and 3 more authors

Learning on Graphs Conference (LoG), 2023

arXiv Bib Blog Code

@article{dirgnn_rossi_2023,
  title = {Edge Directionality Improves Learning on Heterophilic Graphs},
  author = {Rossi, Emanuele and Charpentier, Bertrand and Giovanni, Francesco Di and Frasca, Fabrizio and Günnemann, Stephan and Bronstein, Michael M.},
  year = {2023},
  journal = {Learning on Graphs Conference (LoG)},
}

ICML

Temporal Graph Networks for Deep Learning on Dynamic Graphs

Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, and 3 more authors

ICML Workshop on Graph Representation Learning, 2020

arXiv Bib Blog Code Slides

@article{rossi2020tgn,
  title = {Temporal Graph Networks for Deep Learning on Dynamic Graphs},
  author = {Rossi, Emanuele and Chamberlain, Ben and Frasca, Fabrizio and Eynard, Davide and Monti, Federico and Bronstein, Michael},
  year = {2020},
  journal = {ICML Workshop on Graph Representation Learning},
}

ICML

SIGN: Scalable Inception Graph Neural Networks

Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, and 3 more authors

ICML Workshop on Graph Representation Learning, 2020

arXiv Bib Blog Code

@article{rossi2020sign,
  title = {SIGN: Scalable Inception Graph Neural Networks},
  author = {Rossi, Emanuele and Frasca, Fabrizio and Chamberlain, Ben and Eynard, Davide and Bronstein, Michael and Monti, Federico},
  year = {2020},
  journal = {ICML Workshop on Graph Representation Learning},
}