Enter a ChEMBL compound identifier
Format: CHEMBL followed by digits — e.g. CHEMBL25, CHEMBL192, CHEMBL941
A concise reference for users and researchers. Full details will be available in the associated publication.
Ligand preparation converts a 2D chemical structure into a stable, 3D conformation ready for computational use. It includes adding hydrogens, assigning bond orders, generating 3D coordinates, and optimizing geometry via a molecular force field.
Molecular docking algorithms evaluate ligand–target interactions using three-dimensional geometry, atomic charges, and physicochemical interactions. Poorly prepared ligands can lead to unreliable binding poses and misleading docking scores.
IntelligentLigPrep uses an explainable machine learning ensemble to perform physicochemical profiling of prepared ligands. This AI module is designed to support pre-docking ligand triage by helping identify compounds that are chemically suitable, borderline, or potentially problematic for docking workflows.
The composite score reflects docking-oriented physicochemical suitability and should be interpreted as a pre-docking decision-support metric rather than a predictor of target-specific binding affinity.
Retrieve molecular structure (SMILES) and compound metadata from the ChEMBL database.
Generate three-dimensional molecular coordinates from the input structure.
Optimize molecular geometry using force-field minimization to obtain a stable conformation.
Analyze molecular descriptors using an explainable ML model to classify physicochemical suitability for docking workflows.
Evaluate synthetic accessibility, PAINS alerts, ADMET properties, and drug-likeness indicators.
Generate a comprehensive report including prepared structures, profiling results, and docking-oriented assessment.
If you use this platform in your research, please cite:
[Mahjoub R et al.] IntelligentLigPrep: A Web-Based Platform for Automated Ligand Preparation and AI-Assisted Physicochemical Profiling for Docking Workflows, in preparation, 2026.
Questions, bug reports, or collaboration: