What We Showed?
An AI Engine for Every Step of Early Drug Discovery. Bioptic’s platform is built around three core models:
1. B1: Ligand-Based Hit ID
Using only a known active compound as input, our ultra-high-throughput engine screens 40B+ molecules in under 2.5 minutes — with 100% recall.
- It’s powered by a RoBERTa-based transformer trained on 160M+ SMILES, optimized using techniques from search engines and recommender systems.
- In a recent case study with Oncobox, we discovered VEGFR-3 inhibitors for solid tumors — with the top molecule showing IC50 = 420 nM and 4x faster hit-to-lead conversion.
2. B2: Sequence-Based Hit ID
Don’t have a known binder? No problem. Our Flamingo-inspired architecture can go directly from a protein sequence to hit compounds.
- Screens massive libraries in under a day
- Finds chemically diverse and novel molecules (< 0.4 Tanimoto similarity to known binders), making it ideal for IP generation and first-in-class drug programs
3. B3: ADME Predictions with Gemini
Early pharmacokinetics (PK) and safety profiles are built in. Our ADME module:
- Won the ASAP Antiviral ADMET challenge
- Uses few-shot learning to adapt to sparse or heterogeneous data
- Predicts key ADMET properties even in unexplored chemical space
