Molence

Introducing Mol-1: Open Frontier Biomolecular Intelligence 🚀🚀🚀
📊 75.0% on FoldBench-AB, near IsoDDE's reported 75.58%!!!
🧬 60k-token assemblies on one single H20, outperforming Anthropic 8/10.
🎯 VHHs binding hit rate as high as 63% with best SPR KD at 43 pM.

GitHub: github.com/molence/Mol-1
Try it: lab.molence.com

🧬 Mol-1 turns additional inference compute into better antibody–antigen predictions.

Two complementary ways to scale: sample more candidates and refine more deeply.

On MolBench-AB-26:

📈 More sampling: Increasing from 1 to 100 seeds raises mean DockQ from 0.359 to 0.463, with prediction quality continuing to improve through 100 seeds.

🔄 Deeper refinement: At a fixed 5-seed budget, increasing recycling from 10 → 20 → 30 cycles raises the success rate from 53.5% → 57.3% → 58.7%.

⚡ Better sampling efficiency: 30 cycles × 5 seeds achieves 58.64% success, compared with 58.69% for 10 cycles × 40 seeds—comparable accuracy with 8× fewer seeds.

Mol-1 gives researchers two practical ways to turn inference compute into stronger molecular predictions.

Code & weights: github.com/molence/Mol-1

#AIforScience #ProteinStructure

Mol-1 agent: large protein assemblies on a single NVIDIA H20, outperforming Anthropic 9/10.

In our 10-case evaluation (8,000-60,120 residues), 8 assemblies reached TM-score ≥0.9. The five 30K-60K-residue capsid cases scored 0.993-0.999; Anthropic's published runs for these cases produced collapsed structures.

The ~10K group is mixed: Mol-1 has higher reported TM-scores on 4 of 5 cases, but is lower on 7UTD (0.478 vs 0.96).

These results come from a long-running, iterative prediction-and-assembly workflow, taking tens of hours. The figure compares published results under different hardware and inference settings; it is not a controlled head-to-head benchmark.

Reports:
Mol-1: github.com/molence/Mol-1/…
Anthropic: anthropic.com/research/claud…

1/15 🧬 Mol-1 on antibody structure prediction: on par with IsoDDE, open to build on.

75.0% median success at DockQ > 0.23, on par with IsoDDE’s reported 75.58%.

Across all 201,376 five-seed combinations from 32 seeds, 62.22% matched or exceeded 75% success—showing robust performance across seed selections.

We release the model code and weights so researchers can run Mol-1 in their own environment and build on its capabilities.

Code & weights: github.com/molence/Mol-1
Try Mol-1: lab.molence.com

#AIforScience #DrugDiscovery