# Ood Mol Opt Eval

> Out-of-domain molecular property prediction and Bayesian optimization for molecular design. Tests transferability of learned representations to novel tasks. Use when the user wants to benchmark on Out-of-domain molecular design tasks, or asks about evaluating this task. Reports Top performing molecule property.

- Skill: `qhjqhj00/ood-mol-opt-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ood-mol-opt-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ood-mol-opt-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/ood-mol-opt-eval

---


# ood-mol-opt-eval

> Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction — Chen et al. (2022) (arXiv:2205.02708, 2022)

## What this evaluates

Out-of-domain molecular property prediction and Bayesian optimization for molecular design. Tests transferability of learned representations to novel tasks.

## Datasets

- **Out-of-domain molecular design tasks** — total ?; splits: test (-1)

## Metrics

- `Top performing molecule property` **(primary)** — range: other
  - Maximum property value found during Bayesian optimization over a fixed number of queries.

## Input / output format

**Input**: Molecules from out-of-domain datasets. Feature representations extracted from models trained on FS-Mol.

**Output**: Selected molecules via Bayesian optimization with expected improvement acquisition function.

## Scoring recipe

```python
def score_ood_opt(features, dataset):
    best_score = -inf
    for _ in range(20):
        init_mols = sample_worst_700(dataset, k=16)
        score = bayesian_optimization(init_mols, features, acquisition='EI', batch_size=1)
        best_score = max(best_score, score)
    return best_score
```

## Common pitfalls

- Uses expected improvement acquisition function with query-batch size 1.
- Starts from 16 randomly sampled molecules from the worst ~700 molecules in the dataset.

## Evidence (verbatim from paper)

> We repeat each BO experiment 20 times, each time starting from 16 randomly sampled molecules from the worst ~700 molecules within the dataset. Figure 4 shows that the ADKF-IFT representation enables fastest discovery of top performing molecules for the molecular docking, antibiotic discovery, and material design tasks.

## Citation

```bibtex
@misc{chen2022metalearning,
  title={Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction},
  author={Chen et al. (2022)},
  year={2022},
  note={arXiv:2205.02708}
}
```

- arXiv: 2205.02708