About RASPD+
RApid Screening with Physicochemical Descriptors + Machine Learning
RASPD⁺ is a computationally fast protocol for identifying lead-like molecules based on predicted binding free energy against a target protein with a 3D structure and a defined ligand binding pocket.
Originally developed at the Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio), IIT Delhi by Goutam Mukherjee and B. Jayaram, development continued at the Heidelberg Institute for Theoretical Studies (HITS).
In version 1.0, we introduced scaffold search capabilities and integrated advanced machine learning algorithms. The model was rigorously trained on approximately 4000 non-metallo protein-ligand complexes retrieved from the PDBBIND refined data set.
Key Capabilities
- Screen 1 Million molecules in minutes.
- No rigorous docking required.
- Pearson correlation: 0.74
- RMSE: ±1.86 kcal mol⁻¹
Model Performance
Validation results on a test set of 493 unseen protein–ligand complexes.
Root Mean Square Error
Pearson Correlation
Predictive Ability (q²)
Coeff. of Determination (R²)
Developed In Collaboration With