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# Benchmarks

With BioNeMo GenMol, a benchmark for both performance and accuracy is built on two fragment completion
tasks, **motif-extension** and **scaffold-decoration**, with 10 tests per task drawn
from the [SAFE-DRUGS](https://huggingface.co/datasets/datamol-io/safe-drugs) dataset. These are derived by following tests from [SAFE-GPT](https://github.com/datamol-io/safe/tree/main).
For more information about these tasks, refer to
[Gotta be SAFE: A New Framework for Molecular Design](https://arxiv.org/abs/2310.10773).

For the task of **motif-extension**, parameters are chosen as:

* mask_length = 17
* temperature = 1.2
* noise = 1.6

For the task of **scaffold-decoration**, parameters are chosen as:

* mask_length = 17
* temperature = 1.2
* noise = 2.0

## Performance

Average wall-time (in seconds) for generating 1000 molecules across 10 tests on each
supported GPU.

| GPU                 | motif-extension | scaffold-decoration |
| ------------------- | --------------- | ------------------- |
| A10G                | 3.893           | 3.051               |
| A100                | 1.598           | 1.379               |
| L40S                | 1.625           | 1.458               |
| RTX6000 Ada         | 1.680           | 1.423               |
| RTX6000 Blackwell   | 0.909           | 0.811               |
| H100                | 1.051           | 0.961               |
| H200                | 0.896           | 0.804               |
| B200                | 0.684           | 0.653               |
| B300                | 1.676           | 0.947               |
| GH200               | 0.911           | 0.901               |
| GB10 (DGX Spark)    | 3.938           | 2.811               |
| GB200               | 0.941           | 0.870               |
| GB300               | 1.483           | 0.924               |

## Accuracy

Accuracy is evaluated from generating 100 molecules and computing the following metrics:

* **validity**: fraction of generated SMILES that are valid.
* **uniqueness**: ratio of unique molecules from all valid molecules.
* **diversity**: average pair-wise distances in molecular fingerprints of generated molecules.
* **novelty**: average distances of molecular fingerprints from the input molecule to generated molecules.
* **quality**: fraction of generated molecules with a QED score greater than 0.6 and an SA score less than 4.

Reference values measured on H100:

| metric     | motif-extension | scaffold-decoration |
| ---------- | --------------- | ------------------- |
| validity   | 0.889           | 0.995               |
| uniqueness | 0.670           | 0.756               |
| diversity  | 0.674           | 0.564               |
| novelty    | 0.691           | 0.624               |
| quality    | 0.188           | 0.354               |

Values are single-run measurements and standard deviations across runs are not reported.
Accuracy is broadly consistent across the validated GPU matrix, while validity stays within
approximately ±2 percentage points and quality within approximately ±4 percentage points
across all 13 supported SKUs.

## v1 Compared to v2 on H100

The following table compares v1 and v2 measurements on the same H100 reference SKU,
using the same benchmark harness and methodology in both runs.

| Metric                              | v1 H100 | v2 H100 |       Δ |
| ----------------------------------- | ------: | ------: | ------: |
| **Performance (wall-time, seconds)**|         |         |         |
| motif-extension                     |   1.824 |   1.051 |  −42.4% |
| scaffold-decoration                 |   0.972 |   0.961 |   −1.1% |
| **Accuracy — motif-extension**      |         |         |         |
| validity                            |   0.915 |   0.889 |  −0.026 |
| uniqueness                          |   0.671 |   0.670 |  −0.001 |
| diversity                           |   0.604 |   0.674 |  +0.070 |
| novelty                             |   0.684 |   0.691 |  +0.007 |
| quality                             |   0.273 |   0.188 |  −0.085 |
| **Accuracy — scaffold-decoration**  |         |         |         |
| validity                            |   0.967 |   0.995 |  +0.028 |
| uniqueness                          |   0.763 |   0.756 |  −0.007 |
| diversity                           |   0.555 |   0.564 |  +0.009 |
| novelty                             |   0.655 |   0.624 |  −0.031 |
| quality                             |   0.346 |   0.354 |  +0.008 |

Motif-extension quality is sensitive to hyperparameter selection (`temperature`,
`noise`, and mask-length). The values reported above use the defaults listed in the
test parameters at the top of this page; customer applications can achieve different
accuracy/quality trade-offs by tuning these parameters.