Physics-based simulation

  • Enhanced MD Simulation
  • Hot-spot Mapping
  • HT Virtual Screening
  • Free Energy Calculation

Artificial Intelligence

  • Conformation Prediction
  • Pocket Prediction
  • Hit finding and Ligand Design
  • DMPK Property Prediction
EnsemGen animation


  • Metastable states generation
  • De Novo molecule design

Kinetic Ensemble®


  • Cryptic pocket prediction
  • Fine-tuned predictive apps
EnsemNet animation


Biophysical Technologies

  • HDX-MS
  • Cryo-Electron Microscopy
  • X-Ray Crystallography
  • NMR dynamics (CEST+CPMG)

Chemical Biology

  • Experimental Screen

Developing First-In-Class or Best-In-Class Medicines Faster, Better and Cheaper

Our Kinetic Ensemble® platform effectively integrates novel computational and experimental technologies to discover cryptic pockets on macromolecules and hit compounds for precision medicine approaches to drug discovery.


  • A state-of-the-art deep learning architecture for predicting protein cryptic pockets, pocket similarity, protein-ligand binding and enabling SAR transfer learning.
  • Pre-trained on large protein and ligand databases, EnsemNET™ continuously improves its accuracy by fine-tuning with newly published and proprietary datasets.
  • Synergistic with equilibrium-state predictions by physics-based computational approaches.


  • An advanced generative neural network designed to predict protein kinetic metastable states, transient pockets and generate de novo ligands with desired properties.
  • EnsemGEN™ was trained on vast protein conformations and ligand libraries to capture protein dynamics and intricate protein-ligand interactions.
  • By harnessing the power of generative AI, EnsemGEN™ reveals uncharted protein functional states and uncovers new pockets for unique drug design strategies.