Emmanuel Noutahi

@ENoutahi

Principal Scientist at @valence_ai.

Montréal, Québec

Joined January 2015

Pinned
Emmanuel Noutahi
@ENoutahi
Aug 27
Replying to @ENoutahi
2/ I’m starting a blog series called Beyond the Assay, about drugs that changed medicine in ways few could have anticipated, and what their stories might teach us about drug discovery in the age of AI. The first essay is about Thorazine / chlorpromazine:
Largactil: What Happens After a Drug Appears to Work?
From enoutahi.com
Emmanuel Noutahi
@ENoutahi
Aug 27
1/ Looking back at drug marketing from the 1960s and 70s is… an experience. Here a Thorazine ad literally placed African masks and ritual objects under the heading “basic tools of primitive psychiatry,” in contrast with Thorazine, presented as a tool of Western medicine.
Emmanuel Noutahi
@ENoutahi
Jul 20
Incredible work from my colleagues @NikhilShenoy12 and @Francesco_dgv ! And this is only a glimpse of what they have been cooking lately🔥
Valence Labs
@valence_ai
Jul 20
To identify the most promising drug candidates, we need to understand not only whether a molecule is likely to bind to a target, but how strongly. Today, we’re excited to introduce Nesso-1, an open-source, coarse-grained co-folding model that significantly accelerates
Emmanuel Noutahi
@ENoutahi
May 4
Shocking new result 🙃: telling a deep learning model what we already know about gene interactions helps it predict biology it hasn't seen before. Until high quality biological data catches up, scaling might not be the only path forward.
Valence Labs
@valence_ai
May 4
1/ Our most recent Inside Valence blog post delves into TxPert, a SOTA model published in @NatureBiotech. The key finding: scale alone is insufficient, even smaller model architectures can achieve top performance when coupled with biological priors. @IhabBendid35780
Emmanuel Noutahi
@ENoutahi
Apr 16
Connecting molecular mechanisms to phenotypic outcomes is the missing piece for truly useful virtual cells. This work led by @yunhuijang_ shows we can structure LLM bioreasoning into verifiable, grounded mechanistic explanations that improve perturbation response prediction.
Valence Labs
@valence_ai
Apr 16
To realize our vision of better and more effective drug discovery, we must turn predictions into testable hypotheses and experimental designs. Dive into the Explain pillar of our Virtual Cell framework: why it matters and how we adapt LLM reasoning to biology.