Sean

Seeking narratives @offscriptvc

[Sean @seanrobins_]

Who’s up selling compute right now?

[Sean @seanrobins_]

Are the influx of launch videos a recession indicator?

[Sean @seanrobins_]

@anamika__x Underrated tweet

[Sean @seanrobins_]

@breakfast_nyc

[Sean @seanrobins_]

Whenever someone in SF asks me what I am working on I always answer with “myself”

[Sean @seanrobins_]

@nathansnonsense @sugarjammi I thought you should take pitches from the Corgi cafe

[Sean @seanrobins_]

Create shareholder value they say

[Anindyadeep @anindyadeeps]

Super proud and excited to finally present LiteMol-1. This is our first foundation model from LiteFold, pre-trained from scratch.

Today, LiteMol-1 can generate small molecules, peptides, cyclic peptides, depsipeptides, peptides with ncAAs, macrocycles, and PROTACs.

Across our peptide and small-molecule evaluations, the model shows competitive results. In several settings, we are on-par with or better than frontier structure-based models, at a fraction of the generation cost.

But the part I find most interesting is that this is a model for agents. We have seen ourselves how much compute, and how many tokens it can take to generate good binders using frontier structure-based models. Sometimes you need to generate tens of thousands of designs just to get a handful worth taking forward.

Now put this inside an AutoResearch loop. The agent has to continuously parse structures, inspect PDB/CIF files, compare candidates, run evaluations, modify the design, and repeat the whole thing again. It becomes extremely expensive very quickly.

Sequence space gives us a very different interface. LLMs are much more efficient at inspecting and manipulating compact molecular representations like SMILES than repeatedly operating over full structural files.

So LiteMol-1 becomes something like an infinite molecular canvas for the agent. For a given target and objective, the model can continuously propose what a biomolecule could look like. The agent can inspect those generations, take inspiration from them, preserve certain regions, edit others, optimize them, score them, and generate again.

Generate → inspect → evaluate → edit → generate again.

There is another problem I care a lot about. Most molecule design models today are heavily optimized around binding. But binding is only one part of whether something eventually becomes a therapeutic. What about ADME? Toxicity? Selectivity? Solubility? Membrane permeability? Synthesizability?

For this, we also built a Monte Carlo Tree Search-based multi-objective generation framework around LiteMol-1. Instead of combining everything into one score, the search keeps multiple strong candidates, each balancing the desired properties in a different way.

As our scoring functions and verifiers get better, the generation system gets better too. We can start steering molecules not just toward “binds well”, but toward a broader therapeutic design specification.

The bottleneck slowly moves from simply generating molecules to having sufficiently good verifiers and scoring functions to tell us what is actually worth generating. Check out our technical research blog post for all the details.

At LiteFold, our research is focused on engineering biomolecules and building systems that can carefully forecast their pre-clinical success.

To stay updated on our research, follow LiteFold.

Cheers!

[Sean @seanrobins_]

@nathansnonsense Bearish

[Sean @seanrobins_]

Dinner with a view

[Sean @seanrobins_]

@pangram @nathansnonsense Good job Nathan

[Sean @seanrobins_]

Nobody is talking about it, but the new Google Meet sound is not it

[Sean @seanrobins_]

Build something people want - apparently it’s AirPods

[Sean @seanrobins_]

@finn_hulse So this is how I find out

[Sean @seanrobins_]

Two of the most valuable lessons you can learn are knowing when to go all in and when to walk away from the table

[Sean @seanrobins_]

@henry0284928382 Not enough people talk about this

[Sean @seanrobins_]

@looksmaxxvc @rtrotisserie Wasn’t familiar with your game

[Sean @seanrobins_]

I’ve been putting everyone in Hayes onto @rtrotisserie

[Sean @seanrobins_]

@breakfast_nyc @rtrotisserie At the very least need a rewards card