Deep Learning Molecular Properties

Molecular property prediction via machine learning

Binding Affinity Prediction With Machine Learning

The KDeep app provides ligand-based and structure-based machine learning predictors for multiple molecular properties. One example is binding affinity of a set of ligands against any given target with proven accuracy. Built using the TorchMD-net framework, they model the ligand-target complex as a 3D graph and apply state-of-the-art equivariant transformer layers to retrieve the binding affinities. Thanks to their computational efficiency they are able to screen multi-million datasets in just hours.

Schematic overview of KDeep and the type of input.

All of our predictors have been published in peer-reviewed journals and validated on data from pharmaceutical companies and in blind challenges. KDeep won two blind subchallenges of the D3R Grand Challenge 4 and it is actively used in the pharmaceutical industry. The ligand-based predictor reached the second highest rank with no significant statistical difference with the top-ranked predictor in the Sampl challenge.

The various predictors provide different prediction modalities and can be applied to any use case:

  • KDeep: Absolute binding affinity predictor for regression of congeneric series
  • KDeep-Bindscope: Binding affinity classifier for screening.
  • KDeep-Trainer: Retraining on internal datasets drastically improves the reliability of prediction reaching correlations similar to FEP-based methods after retraining on approximately 50-100 molecules.
KDeep outputs a list of screened ligands for a particular target, scored and ranked according to their predicted affinity value. Thanks to the multiple heads, KDeep is able to be trained on and predict multiple affinity metrics (e.g. pKd, pKi, pIC50, …) simultaneously.

Use Cases

  • Virtual Screening: Rank your library of docked compounds for binding affinity against a given target with KDeep or use together with AceDock. Use Bindscope to additionally detect non-binding ligands.
  • Lead Optimization: Rank congeneric series of compounds with a common core with DeltaDelta.
  • Molecule Generation: Use KDeep as a scoring function in AceGen to design compounds that meet a certain binding affinity threshold.
  • Model Training and Finetuning: Train your own predictor or finetune one of Acellera´s predictors using your in-house data with KDeepTrainer. Our models are designed to use and predict different affinity metrics (e.g. pKd, pIC50,…) simultaneously.


  • A list of ranked compounds with their predicted affinity and probability of the compound being a binder.
  • The newly trained or finetuned model file that can further be finetuned or used to generate predictions.


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