Static SELFIES embedding (320d) trained on COCONUT + ChEMBL34 pairs

This is a sentence-transformers model trained on the coconut-chembl34-mol-sim dataset. It maps inputs to a 320-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Maximum Sequence Length: 200 tokens
Output Dimensionality: 320 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text
Training Dataset:
- coconut-chembl34-mol-sim
Language: en
License: cc-by-nc-sa-4.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): StaticEmbedding({})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/static-chemselfies-molsim")
# Run inference
sentences = [
    '[O] [=S] [Branch1] [Ring1] [O] [C] [S] [S] [S] [S] [S] [N]',
    '[S] [C] [=C] [C] [=C] [Ring1] [Branch1] [C] [=C]',
    '[O] [=C] [Branch1] [O] [C] [C] [=C] [C] [#C] [C] [#C] [C] [=C] [C] [C] [C]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 320]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6880, 0.1453],
#         [0.6880, 1.0000, 0.1629],
#         [0.1453, 0.1629, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Datasets: molsim and molsim-fulltest
Evaluated with EmbeddingSimilarityEvaluator

Metric	molsim	molsim-fulltest
pearson_cosine	0.7647	0.7735
spearman_cosine	0.7428	0.7519

Baseline comparison

All numbers are Spearman/Pearson of embedding cosine similarity against MACCS fingerprint similarity labels on held-out SELFIES pairs of gbyuvd/coconut-chembl34-mol-sim.

Model	Type	Params	Spearman cosine	Pearson cosine
gbyuvd/chemembed-chemselfies (8-layer BERT, hidden 320)	contextual	11.1M	0.952*	0.961*
This model, untrained (token-overlap floor)	static	~1M	~0.54	-
This model (Matryoshka 320/160/80)	static	~1M	0.7519	0.7735

* as reported by the authors of gbyuvd/chemembed-chemselfies on their held-out "combined-test" split of the same data family, with EmbeddingSimilarityEvaluator-style Spearman/Pearson of cosine vs MACCS. This model was evaluated on the dataset's own full test split (118,318 pairs), so the numbers are directly comparable in kind but not bit-identical in split.

The ~0.20 Spearman gap is the price of going static: a mean-pooled embedding table (one vector per SELFIES symbol, no context, ~1M parameters) cannot model token interactions the way the 11.1M-parameter BERT can. In exchange, this model is orders of magnitude faster at inference (a lookup + average; ~20x faster than a comparable-size encoder on GPU and ~80x on CPU) and supports Matryoshka truncation to 160/80 dims.

Limitations & Notes

Static model: mean-pooled SELFIES token embeddings, no context or attention. It trails contextual encoders on fine-grained similarity; it trades that for ~1M parameters and very fast inference.
Input format: SELFIES strings must be space-separated tokens, e.g. [C] [=C] [N] (use " ".join(re.findall(r"\[[^\]]*\]", s)) on raw SELFIES output). Raw glued SELFIES ([C][=C][N]) encodes to [UNK].
The tokenizer's [CLS]/[SEP] post-processing was stripped for training and inference (noise for a token-bag model). Vocab: 3,095 SELFIES symbols, taken from gbyuvd/chemembed-chemselfies.
Labels are MACCS fingerprint cosine similarity, so the embeddings primarily encode MACCS-like similarity. 1 epoch reached 0.7473 Spearman, 3 epochs 0.7519 — diminishing returns. Recipe: CosineSimilarityLoss + MatryoshkaLoss [320/160/80], lr 0.05, batch 2048, cosine scheduler, best-checkpoint restore; ~15 min training on a T4.
License cc-by-nc-sa-4.0, inherited from the baseline model and dataset by gbyuvd.

Training Details

Training Dataset

coconut-chembl34-mol-sim

Dataset: coconut-chembl34-mol-sim
Size: 1,049,286 training samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 100 samples:

	sentence1	sentence2	label
type	string	string	float
modality	text	text
details	min: 60 characters mean: 436.88 characters max: 1766 characters	min: 48 characters mean: 446.99 characters max: 1875 characters	min: 0.2 mean: 0.54 max: 0.92

Samples:

sentence1	sentence2	label
`[O] [=C] [N] [C] [C] [C] [C] [Ring1] [=Branch1] [=C] [O] [C] [Branch2] [Ring1] [Branch1] [O] [C] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O] [C] [Ring2] [Ring1] [C] [C] [C]`	`[O] [=C] [C] [=C] [Branch2] [Ring2] [=Branch1] [O] [C] [=Branch1] [Branch1] [=C] [Ring1] [=Branch1] [O] [C] [NH1] [C] [=Branch1] [=N] [=N] [C] [=Ring1] [Branch1] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [O] [C]`	`0.57`
[O] [=C] [O] [C] [Branch1] [C] [C] [C] [Branch2] [Ring2] [O] [N] [=C] [Branch1] [C] [O] [C] [Branch2] [Ring1] [Branch1] [N] [=C] [Branch1] [C] [O] [C] [Branch1] [Ring1] [O] [C] [C] [O] [S] [=Branch1] [C] [=O] [=Branch1] [C] [=O] [O] [C] [C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [C] [=Branch2] [=Branch1] [#Branch2] [=N] [C] [Branch2] [=Branch1] [Ring2] [C] [=Branch2] [Branch1] [#C] [=N] [C] [C] [=Branch1] [C] [=O] [N] [Branch1] [=Branch2] [C] [Branch1] [C] [O] [C] [C] [Ring1] [Branch2] [C] [Branch2] [Ring2] [Ring2] [C] [=Branch1] [C] [=O] [N] [Branch1] [C] [C] [C] [Branch1] [S] [C] [=Branch1] [N] [=N] [C] [Ring2] [Branch1] [Ring2] [C] [Branch1] [C] [C] [C] [C] [O] [C] [C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1] [C] [Branch1] [C] [C] [C] [C] [O] [C] [O]	`[O] [=C] [C] [C] [C] [Ring1] [Ring2] [C] [C] [C] [C] [C] [=C] [C] [C] [=C] [C] [C] [C] [C] [C]`	`0.38`
`[O] [=C] [N] [C] [Branch2] [Ring1] [C] [O] [C] [=Branch1] [C] [=O] [C] [C] [Branch1] [Ring2] [N] [C] [=O] [C] [Branch1] [C] [C] [C] [C] [Branch1] [C] [C] [C]`	[O] [=C] [Branch3] [Ring1] [C] [Branch2] [O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [=Branch1] [O] [C] [Ring1] [Branch2] [C] [O] [C] [Branch1] [Ring1] [C] [=C] [Branch1] [C] [C] [C] [C] [C] [=C] [Branch2] [S] [Branch1] [C] [=Branch1] [C] [=O] [O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [=Branch1] [O] [C] [Ring1] [Branch2] [C] [O] [C] [Branch1] [Ring1] [C] [=C] [Branch1] [C] [C] [C] [C] [C] [=C] [Branch2] [=N] [=C] [C] [=Branch1] [C] [=O] [O] [C] [C] [C] [Branch2] [Branch1] [O] [C] [=Branch1] [C] [=O] [O] [C] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O] [C] [O] [C] [Branch1] [C] [C] [C] [Branch1] [P] [O] [C] [O] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [Branch2] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [C] [Branch2] [Branch2] [#C] [C] [=Branch2] [#Branch1] [=C] [=C] [C] [C] [C] [Branch1] [C] [C] [C] [C] [C] [Branch2] [Branch1]...	`0.49`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "CosineSimilarityLoss",
    "matryoshka_dims": [
        320,
        160,
        80
    ],
    "matryoshka_weights": [
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Evaluation Dataset

coconut-chembl34-mol-sim

Dataset: coconut-chembl34-mol-sim
Size: 10,000 evaluation samples
Columns: sentence1, sentence2, and label

Approximate statistics based on the first 100 samples:

	sentence1	sentence2	label
type	string	string	float
modality	text	text
details	min: 47 characters mean: 424.23 characters max: 1441 characters	min: 37 characters mean: 459.26 characters max: 1583 characters	min: 0.0 mean: 0.53 max: 0.92

Samples:

sentence1	sentence2	label
[O] [=C] [C] [Branch2] [Ring2] [=Branch2] [O] [C] [O] [C] [Branch2] [Ring1] [Branch1] [C] [O] [C] [O] [C] [Branch1] [C] [C] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [=Branch2] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring1] [Ring2] [O] [=C] [Branch2] [Branch1] [O] [O] [C] [=C] [C] [Branch2] [Ring2] [Branch2] [O] [C] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O] [C] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O] [=C] [C] [Branch1] [C] [O] [=C] [Ring2] [Branch1] [Branch2] [Ring2] [Ring1] [=C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [=C] [Ring1] [Branch2]	`[S] [C] [C] [N] [C] [C] [C] [N] [C] [C] [Ring1] [#Branch2]`	`0.33`
`[F] [C] [=Branch2] [Ring1] [C] [=C] [Branch1] [C] [F] [C] [C] [S] [C] [=N] [N] [=C] [Branch1] [Ring2] [S] [Ring1] [Branch1] [N] [F]`	`[O] [=C] [Branch1] [#Branch2] [N] [C] [C] [S] [C] [=C] [C] [=Ring1] [Branch1] [C] [C] [C] [N] [Branch1] [C] [C] [C] [C] [C] [N] [Branch2] [Ring1] [Branch1] [C] [C] [=C] [C] [=C] [C] [Branch1] [#Branch2] [O] [C] [Branch1] [C] [F] [Branch1] [C] [F] [F] [=C] [Ring1] [O] [C] [Ring1] [P] [C] [=Branch1] [C] [=O] [N] [C] [Ring2] [Ring1] [Branch2]`	`0.49`
[O] [=C] [Branch2] [=Branch1] [N] [O] [C] [C] [O] [C] [Branch2] [Branch1] [#Branch1] [O] [C] [=C] [C] [=C] [Branch2] [Ring1] [Branch1] [O] [C] [O] [C] [Branch1] [Ring1] [C] [O] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring1] [#Branch2] [O] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring2] [Ring1] [Ring1] [OH1+1] [C] [Ring2] [Ring1] [#Branch1] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [=C] [Ring1] [#Branch1] [C] [Branch1] [C] [O] [C] [Branch1] [C] [O] [C] [Ring2] [Ring2] [#Branch1] [O] [C] [=C] [C] [=C] [C] [=C] [Branch1] [C] [O] [C] [Branch1] [Ring1] [O] [C] [=C] [Ring1] [=Branch2]	`[O] [C] [Branch1] [C] [C] [C] [C] [C] [C] [C] [Ring1] [Ring1] [C] [C] [Ring1] [=Branch2] [C] [C] [C] [Ring1] [Branch1] [Branch1] [C] [O] [C]`	`0.42`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "CosineSimilarityLoss",
    "matryoshka_dims": [
        320,
        160,
        80
    ],
    "matryoshka_weights": [
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 2048
learning_rate: 0.05
lr_scheduler_type: cosine
warmup_steps: 0.1
bf16: True
per_device_eval_batch_size: 2048
load_best_model_at_end: True
seed: 12

All Hyperparameters

Click to expand

per_device_train_batch_size: 2048
num_train_epochs: 3
max_steps: -1
learning_rate: 0.05
lr_scheduler_type: cosine
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 2048
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 12
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
dataloader_multiprocessing_context: None
dataloader_in_order: True
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}
warmup_ratio: None

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	molsim_spearman_cosine	molsim-fulltest_spearman_cosine
-1	-1	-	-	0.5371	-
0.0019	1	0.4891	-	-	-
0.0312	16	0.4880	-	-	-
0.0624	32	0.4098	-	-	-
0.0936	48	0.2228	-	-	-
0.1248	64	0.1083	-	-	-
0.1559	80	0.0858	-	-	-
0.1871	96	0.0715	-	-	-
0.2183	112	0.0621	-	-	-
0.2495	128	0.0568	-	-	-
0.2807	144	0.0528	-	-	-
0.3002	154	-	0.0509	0.7186	-
0.3119	160	0.0497	-	-	-
0.3431	176	0.0484	-	-	-
0.3743	192	0.0465	-	-	-
0.4055	208	0.0452	-	-	-
0.4366	224	0.0444	-	-	-
0.4678	240	0.0442	-	-	-
0.4990	256	0.0434	-	-	-
0.5302	272	0.0432	-	-	-
0.5614	288	0.0430	-	-	-
0.5926	304	0.0418	-	-	-
0.6004	308	-	0.0438	0.7342	-
0.6238	320	0.0424	-	-	-
0.6550	336	0.0412	-	-	-
0.6862	352	0.0414	-	-	-
0.7173	368	0.0422	-	-	-
0.7485	384	0.0418	-	-	-
0.7797	400	0.0417	-	-	-
0.8109	416	0.0419	-	-	-
0.8421	432	0.0417	-	-	-
0.8733	448	0.0414	-	-	-
0.9006	462	-	0.0426	0.7393	-
0.9045	464	0.0410	-	-	-
0.9357	480	0.0414	-	-	-
0.9669	496	0.0409	-	-	-
0.9981	512	0.0416	-	-	-
1.0292	528	0.0414	-	-	-
1.0604	544	0.0409	-	-	-
1.0916	560	0.0410	-	-	-
1.1228	576	0.0413	-	-	-
1.1540	592	0.0411	-	-	-
1.1852	608	0.0402	-	-	-
1.2008	616	-	0.0420	0.7388	-
1.2164	624	0.0418	-	-	-
1.2476	640	0.0408	-	-	-
1.2788	656	0.0406	-	-	-
1.3099	672	0.0410	-	-	-
1.3411	688	0.0404	-	-	-
1.3723	704	0.0404	-	-	-
1.4035	720	0.0405	-	-	-
1.4347	736	0.0406	-	-	-
1.4659	752	0.0410	-	-	-
1.4971	768	0.0409	-	-	-
1.5010	770	-	0.0417	0.7415	-
1.5283	784	0.0405	-	-	-
1.5595	800	0.0407	-	-	-
1.5906	816	0.0404	-	-	-
1.6218	832	0.0404	-	-	-
1.6530	848	0.0403	-	-	-
1.6842	864	0.0400	-	-	-
1.7154	880	0.0408	-	-	-
1.7466	896	0.0401	-	-	-
1.7778	912	0.0405	-	-	-
1.8012	924	-	0.0415	0.7413	-
1.8090	928	0.0407	-	-	-
1.8402	944	0.0404	-	-	-
1.8713	960	0.0404	-	-	-
1.9025	976	0.0405	-	-	-
1.9337	992	0.0407	-	-	-
1.9649	1008	0.0406	-	-	-
1.9961	1024	0.0402	-	-	-
2.0273	1040	0.0404	-	-	-
2.0585	1056	0.0399	-	-	-
2.0897	1072	0.0397	-	-	-
2.1014	1078	-	0.0415	0.7425	-
2.1209	1088	0.0405	-	-	-
2.1520	1104	0.0404	-	-	-
2.1832	1120	0.0399	-	-	-
2.2144	1136	0.0405	-	-	-
2.2456	1152	0.0403	-	-	-
2.2768	1168	0.0398	-	-	-
2.3080	1184	0.0402	-	-	-
2.3392	1200	0.0412	-	-	-
2.3704	1216	0.0402	-	-	-
2.4016	1232	0.0409	0.0414	0.7428	-
2.4327	1248	0.0403	-	-	-
2.4639	1264	0.0407	-	-	-
2.4951	1280	0.0406	-	-	-
2.5263	1296	0.0396	-	-	-
2.5575	1312	0.0399	-	-	-
2.5887	1328	0.0395	-	-	-
2.6199	1344	0.0405	-	-	-
2.6511	1360	0.0405	-	-	-
2.6823	1376	0.0407	-	-	-
2.7018	1386	-	0.0414	0.7427	-
2.7135	1392	0.0397	-	-	-
2.7446	1408	0.0403	-	-	-
2.7758	1424	0.0402	-	-	-
2.8070	1440	0.0400	-	-	-
2.8382	1456	0.0398	-	-	-
2.8694	1472	0.0400	-	-	-
2.9006	1488	0.0407	-	-	-
2.9318	1504	0.0402	-	-	-
2.9630	1520	0.0403	-	-	-
2.9942	1536	0.0401	-	-	-
3.0	1539	-	0.0414	0.7426	-
-1	-1	-	-	0.7428	0.7519

The bold row denotes the saved checkpoint.

Training Time

Training: 14.4 minutes
Evaluation: 1.2 minutes
Total: 15.6 minutes

Framework Versions

Python: 3.12.12
Sentence Transformers: 6.0.1
Transformers: 5.16.1
PyTorch: 2.14.0+cu130
Accelerate: 1.14.0
Datasets: 5.0.1
Tokenizers: 0.23.2

Additional Resources

Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Downloads last month: -; Downloads are not tracked for this model. How to track

Safetensors

Model size

990k params

Tensor type

F32

Papers for tomaarsen/static-chemselfies-molsim

Matryoshka Representation Learning

Paper • 2205.13147 • Published May 26, 2022 • 30

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 18

Evaluation results

Pearson Cosine on molsim
self-reported

0.765
Spearman Cosine on molsim
self-reported

0.743
Pearson Cosine on molsim fulltest
self-reported

0.774
Spearman Cosine on molsim fulltest
self-reported

0.752