

AI FOR SCIENCE · ON THE RECORD
Superluminal Medicines
Designs small-molecule drugs for G protein-coupled receptors (GPCRs) with machine learning, structural biology and its own data: cryo-EM, GPCR co-folding models, de novo design and predicted ADME and toxicity. Its lead programme is an MC4R agonist for rare genetic and hypothalamic obesity.
Full funding record →On the timeline →People filter →
Recent timeline
People
Only people the AI4S.fyi record has named: in a round, a timeline entry, commentary, an interview or a sourced role. Not the full team.
Named on AI4S.fyi
Named backers
- HHarsha PaladuguInvestment Analyst; member of the board · BVF Partners · led the round Series B · $60M
Nandita ShangariManaging Director; member of the board · RA Capital Management Series B · $60M- AAndrew LevinPartner and Managing Director; board member · RA Capital Management · led the round Seed · $33M
Dylan MorrisManaging Director; board member · Insight Partners Seed · $33M
Funding rounds tracked
- 2026-09-05Series B · $60MreportedLed by BVF Partners · With Deep Track Capital, Perceptive Advisors · Existing investors RA Capital Management, Insight Partners, NVIDIA, Catalio Capital Management, Eli Lilly and Company, Cooley, GaingelsStated use: take its lead MC4R obesity programme into a Phase 1 trial expected by the end of 2026, and expand its AI-enabled discovery platform
- 2023-08-28Seed · $33MconfirmedLed by RA Capital Management · With Insight Partners, NVIDIA, GaingelsStated use: progress its small-molecule pipeline, first aimed at GPCR targets
Each round links to its line on the funding record, with its source.
Also in Drug discovery on the funding board
The other companies the funding board files under the same field, latest round first. A list of what the record tracks, not a ranking or a market map.
- 2026-10-29ALLOXSeed · US$10.5M (€9.4M)Measures the effects of thousands of protein mutations at once to map allosteric and other regulatory sites, and builds AI models on that data to find drug-binding spots on hard targets such as protein-protein interactions and transcription factors, for its own small molecules.
- 2026-10-27Atlas BioSeed · $10MRuns in silico clinical trials, starting in immunology: a foundation model trained on about 2 million RNA readouts from human tissue predicts each virtual patient's response to several treatments and diseases, to help drug developers choose what to take into Phase II.
- 2026-10-23Pathos AISeries E · $237MLicenses clinical-stage drugs from other companies and develops them itself, using an agent platform (Foundry, earlier PathOS) trained on patient records to choose assets, pick the patients most likely to respond and adjust trials as they run. Its pipeline is in cancer: prostate cancer, multiple myeloma, lung and breast cancer and TROP2/HER3-expressing solid tumours; in October 2026 it began work in Alzheimer's disease with Bristol Myers Squibb and Tempus.
- 2026-10-23Zhichong PharmaSeries A1 · tens of millions of yuanDevelops new drugs for pets with generative AI, using its own small-molecule and large-molecule design engines (ChemoFetch and OmniFetch), and argues that testing in the target animal closes the loop from design to evidence faster than human drug development can; its programmes are in anti-infectives, chronic disease and ageing.
- 2026-10-14Gaowei YiyaoPre-A · undisclosed; PEdaily says the company has raised several hundred million yuan so far this yearMines protein-interaction and other high-dimensional biology data with its own models to find drug targets others treat as undruggable, then runs its own small-molecule and antibody programmes. Its GROOVY platform found the target behind GW-01, a Notch-pathway inhibitor now in Phase I; ACUMEN maps pairs of neighbouring cell-surface proteins for bispecifics and NEXUS looks for tumour neoantigens.
- 2026-10-14Iambic TherapeuticsIPO · $150MDesigns small-molecule cancer drugs with AI structure-prediction models (NeuralPLexer) linked to an automated chemistry and biology lab.
This page compiles what AI4S.fyi has recorded and links each item to its source. It is not a grade of the organization and not its complete history: a blank means the record has not caught up as often as it means nothing happened.