Fred Manby
Researcher · Founder
Now Founder, Iambic Therapeutics
Latest on AI4S.fyiFunding · Oct 14 · founderIambic Therapeutics · IPO · $150MPhotos come from each person's X account, their own site, lab or team page, a conference page that lists them as a speaker in the same role, or Wikimedia Commons under the licence credited, so some may be out of date. Anyone can ask for theirs to be replaced or removed. Contact →
Affiliations
Current
- Founder · Iambic Therapeutics
Current roles come from these dated records, not from the bio below. Each links the page it was read from.
About
Fred Manby is co-founder and chief technology officer of Iambic Therapeutics. Before that he was a professor of theoretical chemistry at the University of Bristol, where he won the Royal Society of Chemistry's Corday Morgan Prize and, in 2007, the annual medal of the International Academy of Quantum Molecular Sciences.
Read on 2026-09-29; for current roles see Affiliations above. Read at: iambic.ai ↗ · blogs.rsc.org ↗ · bristol.ac.uk ↗
Selected work
- OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features ↗
A deep learning model that predicts quantum-chemistry properties from atomic-orbital features.
Also on this record: Tom Miller
Each byline was read on the paper itself. Only people on this record are named, not the full author list, and a shared byline does not say who did which part.
Founded
Iambic TherapeuticsDrug discovery · 2019 · 5 rounds on record · disclosed $353M · IPO $150MBackground: University of Bristol (chemistry professor) (as the company's own announcements put it)
Co-founders: Tom Miller
Backed byMubadala CapitalExor VenturesAscenta CapitalAbingworthCoatueCatalio Capital Management
On the timeline
- 2026-10-14Iambic prices its Nasdaq IPO at $16 a share, the middle of its range, raising $150M before costs
- 2026-10-08Iambic sets its IPO at $15-17 a share, about $150M at the midpoint, and two investors signal interest in up to $60M of it
- 2026-09-21Iambic describes Enchant v3, a 41-billion-parameter in-house model that predicts drug properties from 16 kinds of data, and says accuracy has kept rising with scale
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