PROTAC-Splitter-EncoderDecoder-lr_cosine_restarts

ailab-bio/PROTAC-Splitter-EncoderDecoder-lr_cosine_restarts

PROTAC-Splitter-EncoderDecoder-lr_cosine_restarts is a machine learning model from ailab-bio. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: mit
Model type: encoder-decoder
Access: Public
Created: Mar 8, 2025
Updated: Mar 9, 2025
SHA: 5ddd846c

Base models

seyonec/ChemBERTa-zinc-base-v1

Model card

From the Hugging Face model README

ailab-bio/PROTAC-Splitter-EncoderDecoder-lr_cosine_restarts

This model is a fine-tuned version of seyonec/ChemBERTa-zinc-base-v1 on the ailab-bio/PROTAC-Splitter-Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.3421
Linker Heavy Atoms Difference Norm: 0.0015
Poi Valid: 0.9263
E3 Graph Edit Distance: inf
Poi Graph Edit Distance: 737429178470255015019840581324076533822563653950209220564484096.0000
Poi Has Attachment Point(s): 0.9263
E3 Has Attachment Point(s): 0.9785
Poi Heavy Atoms Difference Norm: 0.0735
Poi Equal: 0.7917
Linker Tanimoto Similarity: 0.0
Linker Graph Edit Distance: inf
Linker Equal: 0.8455
Linker Valid: 0.9975
Reassembly: 0.6035
Has All Attachment Points: 0.9868
Heavy Atoms Difference Norm: 0.1010
Reassembly Nostereo: 0.6258
E3 Graph Edit Distance Norm: inf
All Ligands Equal: 0.5964
E3 Equal: 0.8260
E3 Valid: 0.9785
E3 Heavy Atoms Difference Norm: 0.0174
Heavy Atoms Difference: 7.5459
E3 Tanimoto Similarity: 0.0
Has Three Substructures: 0.9998
E3 Heavy Atoms Difference: 0.6771
Linker Heavy Atoms Difference: 0.2282
Linker Graph Edit Distance Norm: inf
Linker Has Attachment Point(s): 0.9975
Valid: 0.9041
Poi Tanimoto Similarity: 0.0
Poi Heavy Atoms Difference: 2.1196
Tanimoto Similarity: 0.0
Num Fragments: 3.0002
Poi Graph Edit Distance Norm: inf

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 128
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 800
training_steps: 100000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	All Ligands Equal	E3 Equal	E3 Graph Edit Distance	E3 Graph Edit Distance Norm	E3 Has Attachment Point(s)	E3 Heavy Atoms Difference	E3 Heavy Atoms Difference Norm	E3 Tanimoto Similarity	E3 Valid	Has All Attachment Points	Has Three Substructures	Heavy Atoms Difference	Heavy Atoms Difference Norm	Linker Equal	Linker Graph Edit Distance	Linker Graph Edit Distance Norm	Linker Has Attachment Point(s)	Linker Heavy Atoms Difference	Linker Heavy Atoms Difference Norm	Linker Tanimoto Similarity	Linker Valid	Validation Loss	Num Fragments	Poi Equal	Poi Graph Edit Distance	Poi Graph Edit Distance Norm	Poi Has Attachment Point(s)	Poi Heavy Atoms Difference	Poi Heavy Atoms Difference Norm	Poi Tanimoto Similarity	Poi Valid	Reassembly	Reassembly Nostereo	Tanimoto Similarity	Valid
0.0156	0.4932	5000	0.4596	0.7815	inf	inf	0.9916	0.3913	0.0061	0.0	0.9916	0.9863	0.9988	7.0030	0.0918	0.6623	35410764872521246890440289523238443113605795566260484204134400.0000	inf	0.9965	0.3805	0.0005	0.0	0.9965	0.2891	3.0006	0.7265	inf	inf	0.9252	2.3129	0.0739	0.0	0.9252	0.4665	0.4911	0.0	0.9169
0.0077	0.7398	7500	0.4898	0.7908	inf	inf	0.9857	0.4845	0.0088	0.0	0.9857	0.9812	0.9995	8.2839	0.1093	0.7095	56657223796033995024704463237181508981769272906016774726615040.0000	inf	0.9943	0.4483	0.0090	0.0	0.9943	0.3073	3.0002	0.7363	inf	inf	0.9161	2.5750	0.0832	0.0	0.9161	0.4975	0.5220	0.0	0.8987
0.0046	0.9864	10000	0.5462	0.8045	inf	inf	0.9896	0.5553	0.0131	0.0	0.9896	0.9836	0.9991	7.0102	0.0939	0.7666	inf	inf	0.9961	0.3144	0.0033	0.0	0.9961	0.3086	2.9998	0.7680	inf	inf	0.9272	2.1208	0.0719	0.0	0.9272	0.5544	0.5796	0.0	0.9157
0.0003	6.9047	70000	0.3416	0.0013	0.9247	inf	inf	0.9247	0.9850	0.0743	0.7900	0.0	28328611898017003221343002442430278724028514250988932894294016.0000	0.8408	0.9972	0.5992	0.9859	0.0987	0.6233	inf	0.5914	0.8266	0.9850	0.0135	7.3665	0.0	1.0	0.5428	0.2086	inf	0.9972	0.9082	0.0	2.1749	0.0	3.0	inf
0.0004	7.1513	72500	0.3364	0.0027	0.9271	inf	inf	0.9271	0.9849	0.0707	0.7934	0.0	inf	0.8451	0.9971	0.6019	0.9846	0.0960	0.6242	inf	0.5942	0.8275	0.9849	0.0122	7.1871	0.0	1.0	0.5763	0.2224	inf	0.9971	0.9109	0.0	2.0791	0.0	3.0	inf
0.0005	7.3979	75000	0.3489	0.0030	0.9274	inf	inf	0.9274	0.9839	0.0704	0.7870	0.0	inf	0.8362	0.9971	0.5971	0.9873	0.0985	0.6222	inf	0.5900	0.8249	0.9839	0.0187	7.4559	0.0	0.9997	0.6882	0.2505	inf	0.9971	0.9108	0.0	2.1431	0.0	2.9997	inf
0.0012	7.6445	77500	0.3375	0.0072	0.9271	inf	inf	0.9271	0.9820	0.0760	0.7898	0.0	inf	0.8247	0.9965	0.5921	0.9864	0.0983	0.6127	inf	0.5848	0.8250	0.9820	0.0149	7.4127	0.0	0.9997	0.6424	0.3465	inf	0.9965	0.9095	0.0	2.2373	0.0	2.9997	inf
0.0003	7.8911	80000	0.3411	0.0019	0.9295	inf	inf	0.9295	0.9783	0.0750	0.7928	0.0	inf	0.8451	0.9971	0.6030	0.9868	0.0997	0.6278	inf	0.5955	0.8263	0.9783	0.0179	7.4869	0.0	0.9995	0.7170	0.2144	inf	0.9971	0.9079	0.0	2.1623	0.0	2.9998	inf
0.0004	8.1377	82500	0.3505	0.0009	0.9268	inf	inf	0.9268	0.9796	0.0742	0.7927	0.0	inf	0.8377	0.9966	0.5997	0.9863	0.1033	0.6243	inf	0.5924	0.8275	0.9796	0.0176	7.7488	0.0	0.9999	0.6833	0.2141	inf	0.9966	0.9047	0.0	2.1771	0.0	3.0001	inf
0.0005	8.3843	85000	0.3462	0.0021	0.9173	inf	inf	0.9173	0.9818	0.0824	0.7883	0.0	inf	0.8387	0.9964	0.5984	0.9820	0.1082	0.6215	inf	0.5915	0.8260	0.9818	0.0158	8.1167	0.0	0.9996	0.6591	0.2392	inf	0.9964	0.8986	0.0	2.4100	0.0	3.0	inf
0.0009	8.6309	87500	0.3325	0.0020	0.9225	inf	inf	0.9225	0.9796	0.0794	0.7900	0.0	inf	0.8345	0.9972	0.5966	0.9834	0.1044	0.6196	inf	0.5887	0.8239	0.9796	0.0165	7.8794	0.0	0.9996	0.6269	0.2329	inf	0.9972	0.9019	0.0	2.3541	0.0	2.9996	inf
0.0003	8.8775	90000	0.3373	0.0020	0.9258	inf	inf	0.9258	0.9801	0.0744	0.7946	0.0	25672804532577906850064595316267633373936140684529123813490688.0000	0.8446	0.9974	0.6029	0.9847	0.1014	0.6285	inf	0.5953	0.8287	0.9801	0.0175	7.6187	0.0	0.9999	0.6809	0.2217	inf	0.9974	0.9058	0.0	2.1887	0.0	2.9999	inf
0.0003	9.1241	92500	0.3412	0.0040	0.9302	inf	inf	0.9302	0.9817	0.0684	0.7867	0.0	31869688385269122201396260570914598802245216009634435783720960.0000	0.8426	0.9968	0.6011	0.9828	0.0971	0.6213	inf	0.5941	0.8295	0.9817	0.0157	7.3307	0.0	0.9997	0.6020	0.2689	inf	0.9968	0.9106	0.0	2.0255	0.0	2.9997	inf
0.0004	9.3707	95000	0.3420	0.0071	0.9275	inf	inf	0.9275	0.9796	0.0735	0.7880	0.0	23902266288951841651047195428185949101683912007225826837790720.0000	0.8352	0.9976	0.5992	0.9868	0.1005	0.6217	inf	0.5911	0.8257	0.9796	0.0189	7.5129	0.0	0.9996	0.6888	0.3183	inf	0.9976	0.9073	0.0	2.1638	0.0	2.9998	inf
0.001	9.6173	97500	0.3328	0.0020	0.9207	inf	inf	0.9207	0.9771	0.0780	0.7890	0.0	35410764872521246890440289523238443113605795566260484204134400.0000	0.8340	0.9965	0.5977	0.9896	0.1080	0.6202	inf	0.5900	0.8252	0.9771	0.0217	8.1480	0.0	0.9993	0.7837	0.2400	inf	0.9965	0.8982	0.0	2.2722	0.0	3.0004	inf
0.0003	9.8639	100000	0.3421	0.0015	0.9263	inf	737429178470255015019840581324076533822563653950209220564484096.0000	0.9263	0.9785	0.0735	0.7917	0.0	inf	0.8455	0.9975	0.6035	0.9868	0.1010	0.6258	inf	0.5964	0.8260	0.9785	0.0174	7.5459	0.0	0.9998	0.6771	0.2282	inf	0.9975	0.9041	0.0	2.1196	0.0	3.0002	inf

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1

Library: transformers
Type: encoder-decoder
License: mit
Created: Mar 8, 2025
Updated: Mar 9, 2025