Binding Pose Prediction Eval

---
name: binding-pose-prediction-eval
description: Evaluates a model's ability to predict the 3D spatial arrangement (binding pose) of a small molecule ligand when docked to a target protein structure. It probes geometric reasoning, conformational sampling, and the capacity to generate physically plausible protein-ligand complexes. Use when the user wants to benchmark on PDBBind, or asks about evaluating this task. Reports L-RMSD.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2306.11768
  bibtex_key: zhang2023geometric
  confidence: high
---

# binding-pose-prediction-eval

> Geometric Deep Learning for Structure-Based Drug Design: A Survey — Zaixi Zhang et al. (2023) (arXiv:2306.11768, 2023)

## What this evaluates

Evaluates a model's ability to predict the 3D spatial arrangement (binding pose) of a small molecule ligand when docked to a target protein structure. It probes geometric reasoning, conformational sampling, and the capacity to generate physically plausible protein-ligand complexes.

## Datasets

- **PDBBind** — total 17347; splits: train (-1), test (-1)

## Metrics

- `L-RMSD` **(primary)** — range: other
  - Ligand Root Mean Square Deviation (L-RMSD) measures the mean squared error between the atoms of the predicted and ground truth ligand coordinates. Computed as sqrt((1/n) * sum(||R_i - R_hat_i||^2)) for i=1 to n, where n is the number of atoms.
- `Centroid Distance` — range: other
  - Calculates the Euclidean distance between the averaged coordinates of the predicted ligand atoms and the truly bound ligand atoms.
- `Kabsch RMSD` — range: other
  - Computes the lowest possible RMSD by first applying the Kabsch algorithm to optimally superimpose the predicted and ground truth structures via roto-translation, then calculating the standard RMSD.

## Input / output format

**Input**: 3D structural representation of the target protein (receptor) and the 2D graph or 3D structure of the ligand.

**Output**: 3D coordinates of the ligand atoms representing the predicted binding pose relative to the protein.

## Scoring recipe

```python
def compute_l_rmsd(predicted_coords, true_coords):
    # predicted_coords and true_coords are (n, 3) numpy arrays
    n = predicted_coords.shape[0]
    diff = predicted_coords - true_coords
    sq_dist = np.sum(diff**2, axis=1)
    return np.sqrt(np.mean(sq_dist))

def compute_kabsch_rmsd(predicted_coords, true_coords):
    # Align predicted_coords to true_coords using Kabsch algorithm
    aligned = kabsch_align(predicted_coords, true_coords)
    return compute_l_rmsd(aligned, true_coords)
```

## Common pitfalls

- Treating proteins as rigid bodies during docking, ignoring inherent conformational flexibility that occurs upon ligand binding.
- Focusing exclusively on geometric accuracy (e.g., low RMSD) while neglecting chemical and physical plausibility, leading to poses with steric clashes or invalid bond geometries.

## Evidence (verbatim from paper)

> Ligand Root Mean Square Deviation (L-RMSD) is the mean squared error between the atoms of the predicted and bound ligands. Formally, let $R\in\mathbb{R}^{n\times 3}$ and $\hat{R}\in\mathbb{R}^{n\times 3}$ be the predicted and the ground truth ligand coordinates, where $n$ is the number of atoms. The L-RMSD is obtained with: $\text{L-RMSD}(R,\hat{R})=\big{(}\frac{1}{n}\sum_{i=1}^{n}||R_{i}-\hat{R}_{i}||^{2}\big{)}^{\frac{1}{2}}$

## Citation

```bibtex
@misc{zhang2023geometric,
  title={Geometric Deep Learning for Structure-Based Drug Design: A Survey},
  author={Zaixi Zhang et al. (2023)},
  year={2023},
  note={arXiv:2306.11768}
}
```

- arXiv: 2306.11768