Our unique AI-enabled process designs innovative NCEs targeting any class of protein at unprecedented speed.
- Identify and validate structural proteins
- Characterize target druggability through In Silico MolecuLern-driven workflow and experimental data
- Real data tracking models
- Hotspot residues
- Validate binding pocket
- AA property ennumeration
- Iterative screening
- Med-Chem tractability
- Lead identification/selection
- NCEs to synthesis
- SAR to complete lead selection
Your training set of real empirical/wet lab data allows us to cover the spectrum of protein/small molecule interactions and discover targetable hot spots which minimizes the hit-and-miss nature of traditional drug discovery.
Cutting-edge platform based on decades of work, supported by over 5 years of AI/ML development that discovers new medicines with previously impossible speed and accuracy.
MolecuLern has been trained on hundreds of 3D structures, from both PDB and DeepMind AlphaFold protein targets, screened against our IP rich proprietary fragment and NCE library.