Valence Labs

3/ We only have 8 presentation slots and sign-ups close on November 3rd.

Make sure to RSVP below 👇

RSVP here: luma.com/n47bshdx

2/ We want to connect the people reimagining how drug discovery happens, to share ideas, spark collaborations, and explore ways to build together.

If you want to present a “Show & Tell”, we review submissions on a rolling, first-come, first-served basis.

1/ Valence Labs is bringing together next generation of Montréal researchers and innovators in Bio-AI.

We are hosting a Bio-AI Show & Tell — a quick and fun showcase of ideas, demos and projects in ML.

RSVP here: luma.com/n47bshdx

5/ If you’re attending ICML, make sure to connect with @jhartford.bsky.social and Ihab to learn more about the research happening at Valence Labs and @recursionpharma.bsky.social.

4/ “Towards Scientific Discovery with Dictionary Learning: Extracting Biological Concepts from Microscopy Foundation Models”

Link: arxiv.org/abs/2412.16247

3/ “A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features”

Link: arxiv.org/abs/2505.21317

2/ ”ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy”

Link: openreview.net/forum?id=niy...

1/ Valence Labs, @recursionpharma.bsky.social's AI research engine, is at ICML this week!

Two of our scientists, @jhartford.bsky.social and Ihab Bendidi are presenting posters on work featured in their papers 👇🧵

3/ By combining SynFlowNet with Boltz-2, this new release aims to accelerate the design of high-affinity binders for more efficient early-stage drug discovery.

Check out the GitHub repository here: github.com/recursionpha...

Learn more about Boltz-2 here: www.rxrx.ai/boltz-2

2/ Originally introduced by Valence Labs, Recursion’s AI research engine, SynFlowNet helps ensure generative models propose molecules that are not only novel but also synthetically feasible.

1/ We are excited to open-source the SynFlowNet-Boltz-2 trainer today! 🧵

Pleased to have hosted the 2025 Molecular Machine Learning Conference last week at @mila-quebec.bsky.social

Thank you to everyone who joined us and shared their valuable insights!

We hope to see you all again soon.

Looking forward to seeing everyone at MoML this Wednesday, June 18th, at @mila-quebec.bsky.social in Montréal.

We anticipate a day of engaging discussions, talks and poster presentations, exploring research at the intersection of machine learning and drug discovery.

Agenda Here: portal.ml4dd.com

Check out some of the early media coverage here: www.forbes.com/sites/alexkn...

Proud to share @recursionpharma.bsky.social's exciting announcement on Boltz-2! Valence Labs, Recursion’s AI research engine, contributed to the foundational research supporting the @mit.edu team led by Regina Barzilay.

Learn more about Boltz-2 here: www.rxrx.ai/boltz-2

3/ MoML is an excellent opportunity to engage with leading minds, explore cutting-edge research, and connect with the community in TechBio.

We encourage everyone to secure their spot before it’s too late!

Register now to be part of MoML 2025: portal.ml4dd.com/events/molec...

2/ Our list of speakers include:
— Aaron Newman (@stanford.edu)
— Smita Krishnaswamy (@yale.edu)
— Gabriele Corso (@mit.edu)
— Kirill Neklyudov (@mila-quebec.bsky.social)
— Emmanuel Noutahi (@valenceai.bsky.social | @recursionpharma.bsky.social)
— Michael LeVine (Genesis Therapeutics)

1/ We're excited to announce our speaker lineup for MoML 2025!

Join us on June 18th, 2025, at Mila in Montreal for a day focused on the intersection of machine learning and drug discovery. 🧵

A new perspective paper from Recursion and our AI research engine @valenceai.bsky.social lays out our vision for a virtual cell as a system that can reliably drive the discovery of new drugs via an iterative loop of: predict, explain, discover. arxiv.org/abs/2505.14613 #TechBio 🧪

TxPert: Predicting Cellular Responses to Unseen Genetic Perturbations - Valence Labs We introduce TxPert: a state-of-the-art model that leverages multiple biological knowledge networks to accurately predict transcriptional responses under OOD scenarios.

6/ Learn more

Try the app: txpert.valencelabs.com
Read the blog: www.valencelabs.com/txpert-predi...
Read the paper: arxiv.org/abs/2505.14919
Access the code: github.com/valence-labs...

Advancing Drug Discovery Outcomes with Virtual Cells at Recursion - Valence Labs Predict, Explain, Discover: The Pillars of the Virtual Cell Our view of the virtual cell rests on three interconnected capabilities:

5/ TxPert addresses the "Predict" capability of our virtual cell framework

This represents an initial, but important, step in a much larger journey: building models that can predict, explain, and eventually discover new therapeutic opportunities

www.valencelabs.com/advancing-dr...

4/ We’re also releasing the TxPert App, a tool that makes the model accessible to researchers

- Select a cell type and gene to perturb
- Visualize the gene interaction networks
- Analyze predicted expression changes

Explore more: txpert.valencelabs.com

3/ TxPert achieves state-of-the-art performance by integrating multiple biological knowledge networks

These include curated public resources like STRINGdb and GO, alongside @recursionpharma.bsky.social proprietary PxMap and TxMap graphs derived from large-scale perturbation screens

2/ Observing how cells respond to perturbations is key to understanding disease and designing more effective therapies

But doing this experimentally across an intractable combination of genes, cell types, and conditions is slow, expensive, and infeasible

TxPert: Predicting Cellular Responses to Unseen Genetic Perturbations - Valence Labs We introduce TxPert: a state-of-the-art model that leverages multiple biological knowledge networks to accurately predict transcriptional responses under OOD scenarios.

1/ Introducing TxPert: a new model that predicts transcriptional responses across diverse biological contexts

It’s designed to generalize across unseen single-gene perturbations, novel combinations of gene perturbations, and even new cell types 🧵

www.valencelabs.com/txpert-predi...

Advancing Drug Discovery Outcomes with Virtual Cells at Recursion - Valence Labs Predict, Explain, Discover: The Pillars of the Virtual Cell Our view of the virtual cell rests on three interconnected capabilities:

8/ Links below

Blog: www.valencelabs.com/advancing-dr...

Perspective paper: www.valencelabs.com/publications...

TxPert: www.valencelabs.com/publications...

7/ This work represents an important step towards our mission of decoding biology to radically improve lives

We are excited to share our vision and invite the scientific community to explore these resources together

6/ By leveraging multiple biological knowledge graphs, TxPert can generalize to unseen single perturbations and combinations of novel cell types and multiple perturbations

Read the paper: www.valencelabs.com/publications...

5/ As an important first step toward the Predict capability of this framework, we are also introducing TxPert: a state-of-the-art model for predicting transcriptional responses to genetic perturbations, especially in out-of-distribution scenarios

Virtual Cells: Predict, Explain, Discover - Valence Labs Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient respons...

4/ We share our views on this new frontier in AI-enabled drug discovery in a perspective paper built on our experience @recursionpharma.bsky.social: www.valencelabs.com/publications...