Understand the source comparison
AI Drug Discovery Peptides Future Medicine: Comparison
AlphaFold 3 Peptide structure prediction 95% match to crystallography at >90 pLDDT Minutes per sequence Poor performance on non-standard amino acids, cyclic peptides Best-in-class for natural peptide folding. Unreliable for modified therapeutics AutoDock Vina
No winner is assigned.
This page preserves a source comparison for education. It does not add a rating, recommendation or clinical judgment.
- AlphaFold 3
- Peptide structure prediction
- 95% match to crystallography at >90 pLDDT
- Minutes per sequence
- Poor performance on non-standard amino acids, cyclic peptides
- Best-in-class for natural peptide folding. Unreliable for modified therapeutics
- AutoDock Vina
- Binding affinity prediction
- ±2 kcal/mol ΔG (±1 log unit Kd)
- Hours per target-ligand pair
- Cannot predict allosteric effects or dynamic binding
- Reliable for rank-ordering candidates. Not for absolute affinity quantification
- Generative peptide GANs
- Novel sequence generation
- 40–60% hit rate in functional assays
- Days for model training, seconds for generation
- Requires large training datasets; prone to overfitting on narrow target classes
- Useful for scaffold hopping and sequence diversification. Not de novo design
- Molecular dynamics (GROMACS, AMBER)
- Conformational stability prediction
- Qualitative only; no validated accuracy metric
- Days to weeks per simulation
- Computationally expensive; results depend heavily on force field choice
- Essential for understanding peptide flexibility. Too slow for high-throughput screening