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Molence

@MolenceBio
molence.com/
Joined October 2026
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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
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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
Two panels show FoldBench-AB success rates and the distribution over all 201,376 five-seed combinations from 32 seeds. Mol-1 scores 75.0%, compared with IsoDDE's plotted 75.6%; Mol-1 with constraints scores 88.3%. In the distribution, 62.22% of combinations reach at least 75% success and 17.85% reach at least 76%.

ALT Two panels show FoldBench-AB success rates and the distribution over all 201,376 five-seed combinations from 32 seeds. Mol-1 scores 75.0%, compared with IsoDDE's plotted 75.6%; Mol-1 with constraints scores 88.3%. In the distribution, 62.22% of combinations reach at least 75% success and 17.85% reach at least 76%.

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14/15 📍 Bring epitope knowledge into structure prediction. With epitope information, Mol-1-constraint improves antibody–antigen success at DockQ > 0.23 across all four benchmarks: • FoldBench-AB: 75.0% → 88.3% • AF3-AB: 62.7% → 84.2% • PXMeter-AB: 58.3% → 77.5% • MolBench-AB-26: 57.3% → 79.4% Evaluate Mol-1 and Mol-1-constraint with your own biological questions: lab.molence.com #AIforScience
Four antibody-antigen benchmark charts show success rates split by DockQ accuracy level. Adding epitope constraints increases Mol-1 success from 75.0% to 88.3% on FoldBench-AB, 62.7% to 84.2% on AF3-AB, 58.3% to 77.5% on PXMeter-AB and 57.3% to 79.4% on MolBench-AB-26.

ALT Four antibody-antigen benchmark charts show success rates split by DockQ accuracy level. Adding epitope constraints increases Mol-1 success from 75.0% to 88.3% on FoldBench-AB, 62.7% to 84.2% on AF3-AB, 58.3% to 77.5% on PXMeter-AB and 57.3% to 79.4% on MolBench-AB-26.

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15/15 📍 Three residues can provide a useful structural clue. In three case studies, we supplied three-residue site hypotheses informed by literature and monomer structures. With Mol-1-constraint, DockQ improved: • 0.014 → 0.874 • 0.011 → 0.739 • 0.200 → 0.863 These examples show how sparse biological hypotheses can guide complex prediction. #AIforScience
Three structural case studies compare Mol-1, sparse epitope guidance and oracle epitope guidance. Sparse three-residue site hypotheses improve DockQ from 0.014 to 0.874 for 7UA2, from 0.011 to 0.739 for 9Z76 and from 0.200 to 0.863 for 9GEI. Predicted structures are overlaid on gray references.

ALT Three structural case studies compare Mol-1, sparse epitope guidance and oracle epitope guidance. Sparse three-residue site hypotheses improve DockQ from 0.014 to 0.874 for 7UA2, from 0.011 to 0.739 for 9Z76 and from 0.200 to 0.863 for 9GEI. Predicted structures are overlaid on gray references.

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