cyberneticlibrary
← library

Generate and test data-driven hypotheses

hypogenicskillsetup L2★27,559
K-Dense-AI/scientific-agent-skills ↗
What it does

Generate spatial protein-ligand interaction models from sequence + structure

Best for

Drug discovery when rational design beats screening; structure-guided SAR (structure-activity relationships) directly from sequences.

Inputs
  • · protein FASTA
  • · ligand SMILES or SDF
  • · binding site residues (optional)
  • · tissue/cellular context
Outputs
  • · 3D docked pose (PDB)
  • · binding affinity prediction (ΔG, Kd)
  • · interaction hotspot residues
  • · off-target prediction
Requires
  • · AlphaFold2 (structure prediction)
  • · AutoDock Vina (docking)
  • · Rosetta (scoring)
Preconditions

Protein sequence valid; ligand structure valid SMILES/SDF; ~5 min runtime on GPU

Failure modes
  • · unfolded region in protein → structure undefined → docking ambiguous
  • · ligand too flexible → many local minima in docking
  • · tissue context missing → off-target false positives
Trust signals
  • · AlphaFold2 backbone (Nobel Prize 2024 confidence)
  • · Multi-scoring ensemble (Rosetta + docking consensus)
  • · Tissue context integration