Foundational Model for Functional Molecules


The future of biology is designer and 310 is building the AI engine to power this reality. In this future, novel functional molecules with desired properties and function will be designed precisely, accurately, and almost instantly – delivering near limitless value to medicine, climate and industry.

Tech Stack

Our multi-modal, multi-task approach will result in the best foundation, which will result in the best performance in downstream applications.


310’s AI engine is Large – spanning the broadest possible range of multi-context and multi-modal data, Active – self informing additional data to be collected, and Generative – performing end-to-end design against desired specifications


Highly multi-context & multi-modal


Model informs what data needs to be collected


Get exactly what you need


310 leverages the broadest possible range of multi-context and multi-modal data. This includes nearly 20 Terabytes of highly curated public and proprietary data. The data is continuously improved with addition of new high quality data sets.

310 AI Data

Partners Data

FMFM v3.0 Results

Our model can generate novel (%SeqID < 80) protein sequences of variable length, of high quality (pLDDT > 80), and high structural match to a target (TM-Score > 0.8)

 – Natural    Generated

Gene name: n/a
UniParc: UPI001747D4E7
TMScore: 0.87
pLDDT: 87.8
%SeqID: 87.8

  – Natural    – Generated

Gene name: n/a
UniParc: UPI000521F1E1
TMScore: 0.92
pLDDT: 92.6
%SeqID: 65.7

 – Natural    – Generated

Gene name: rpsl
UniParc: UPI00121FE1C6
TMScore: 0.87
pLDDT: 80.2
%SeqID: 65.2

 – Natural    – Generated

Gene name: hisl
UniParc: UPI000D3E516D
TMScore: 0.87
pLDDT: 92.9
%SeqID: 92.6

 – Natural    – Generated

Gene name: cysQ
UniParc: UPI000F8EA1D0
TMScore: 0.95
pLDDT: 93.8
%SeqID: 71.4

 – Natural    – Generated

Gene name: glmU
UniParc: UPI000C4DE6F3
TMScore: 0.87
pLDDT: 95.2
%SeqID: 61.6