Anindyadeep
@anindyadeeps • 4,003 subscribers
Founder @try_litefold | Trying to understand biology one fold at a time
Videos
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!
Anindyadeep53,667 просмотров • 24 дней назад
Introducing LiteFold: making protein engineering more accessible. Starting with Protein Folding - live now!
Anindyadeep53,898 просмотров • 1 год назад
Introducing LiteFold DeNovo. Generate hundreds of chemically viable ligands against any protein pocket, evaluate them in real time, and refine with our built in molecular editor. The problem: hit generation in drug discovery is slow, fragmented, and outsourced. With LiteFold DeNovo, it becomes interactive and instant. You can also fragment grow base generated molecules, iterate, and soon run molecular dynamics to test stability. Docking and large scale screening are on the way. When we said we’re building the AI first lab for drug discovery, we meant it. Super excited to what's coming next. Codename: Rosalind Try LiteFold DeNovo today. It’s free. Links in the comments.
Anindyadeep23,474 просмотров • 1 год назад
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