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2505.14613
Cited By

Virtual Cells: Predict, Explain, Discover

v1v2v3 (latest)

Virtual Cells: Predict, Explain, Discover

20 May 2025

Emmanuel Noutahi

Prudencio Tossou

Shawn T. Whitfield

Alisandra K. Denton

Kristina Ulicna

Michael Cuccarese

Emmanuel Bengio

Dominique Beaini

Christopher Gibson

Berton Earnshaw

ArXiv (abs)PDF HTML Github

Papers citing "Virtual Cells: Predict, Explain, Discover"

12 / 12 papers shown

Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network

Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network

125

1

0

24 Sep 2025

TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction

TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction

Frederik Wenkel

Cassandra Masschelein

...

Berton Earnshaw

Emmanuel Noutahi

Alisandra K. Denton

365

11

0

20 May 2025

Efficient Biological Data Acquisition through Inference Set Design

Efficient Biological Data Acquisition through Inference Set DesignInternational Conference on Learning Representations (ICLR), 2024

Ihor Neporozhnii

Julien Roy

Emmanuel Bengio

414

2

0

25 Oct 2024

Generative Modeling of Molecular Dynamics Trajectories

Generative Modeling of Molecular Dynamics TrajectoriesNeural Information Processing Systems (NeurIPS), 2024

Bowen Jing

279

66

0

26 Sep 2024

How to Build the Virtual Cell with Artificial Intelligence: Priorities
and Opportunities

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

Charlotte Bunne

...

Theofanis Karaletsos

Stephen R. Quake

429

123

0

18 Sep 2024

Automated Discovery of Pairwise Interactions from Unstructured Data

Automated Discovery of Pairwise Interactions from Unstructured Data

Zuheng

Shawn Whitfield

Aniket Didolkar

Berton Earnshaw

Jason S. Hartford

244

6

0

11 Sep 2024

On the Scalability of GNNs for Molecular Graphs

On the Scalability of GNNs for Molecular Graphs

Maciej Sypetkowski

Frederik Wenkel

Farimah Poursafaei

Dominique Beaini

579

43

0

17 Apr 2024

Training and Serving System of Foundation Models: A Comprehensive Survey

Training and Serving System of Foundation Models: A Comprehensive Survey

283

15

0

05 Jan 2024

Gotta be SAFE: A New Framework for Molecular Design

Gotta be SAFE: A New Framework for Molecular DesignDigital Discovery (DD), 2023

Emmanuel Noutahi

Cristian Gabellini

Jonathan S.C Lim

Prudencio Tossou

392

46

0

16 Oct 2023

Generative artificial intelligence for de novo protein design

Generative artificial intelligence for de novo protein design

Adam Winnifrith

251

58

0

15 Oct 2023

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular
Design

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

Pierre-Luc Bacon

Emmanuel Bengio

212

20

0

07 Jun 2023

GFlowNets for AI-Driven Scientific Discovery

GFlowNets for AI-Driven Scientific DiscoveryDigital Discovery (DD), 2023

Moksh Jain

Jason S. Hartford

Alex Hernandez-Garcia

391

78

0

01 Feb 2023

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