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README.md
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license: mit
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---
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license: mit
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dataset:
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- chembl
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- zinc15
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---
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## Overview
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This dataset contains Functional Group (FG)-enhanced SMILES designed for molecule representation learning. It consists of approximately **200 million** FG-enhanced SMILES collected from a wide range of chemical suppliers and databases, as detailed below:
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| **Supplier** | **Number of Compounds** | **Source** |
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|-------------------------------|-------------------------|----------------------------------------------------------|
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| Targetmol | 22,555 | [Targetmol](https://www.targetmol.com/) |
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| Chemdiv | 1,741,620 | [Chemdiv](https://www.chemdiv.com/) |
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| Enamine | 862,698 | [Enamine](https://enamine.net/) |
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| Life Chemical | 347,657 | [Life Chemicals](https://lifechemicals.com/) |
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| Chembridge | 1,405,499 | [Chembridge](https://chembridge.com/) |
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| Vitas-M | 1,430,135 | [Vitas-M](https://vitasmlab.biz/) |
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| InterBioScreen | 560,564 | [InterBioScreen](https://www.ibscreen.com/) |
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| Maybridge | 97,367 | [Maybridge](https://chembridge.com/) |
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| Asinex | 601,936 | [Asinex](https://www.asinex.com/) |
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| Eximed | 61,281 | [Eximed](https://eximedlab.com/) |
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| Princeton BioMolecular | 1,647,078 | [Princeton BioMolecular](https://princetonbio.com/) |
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| Otava | 9,203,151 | [Otava](https://www.otava.com/) |
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| Alinda Chemical | 733,152 | [Alinda Chemical](https://www.alinda.ru/synthes_en.html) |
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| ChEMBL 25 | 1,785,415 | [ChEMBL](https://www.ebi.ac.uk/chembl/) |
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| ZINC15 | 175,000,000 | [ZINC15](https://zinc15.docking.org/) |
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| **Total** | **200,000,000** | |
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## Dataset Details
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This dataset contains **42,364** unique tokens (vocabulary size = 42,364), which is an extension from 93 tokens in the corresponding standard SMILES dataset. This expansion helps bridge the gap between SMILES and natural language, which has a large set of vocabulary to sufficiently express the nuances of language.
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## Examples of FG-enhanced SMILES
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Below are some examples of FG-enhanced SMILES from the dataset. Each SMILES string is augmented with functional group annotations (e.g., `C_alkyl`, `O_ether`, `c_6`, `n_tertiary_amine`, etc.) to enhance the representation of molecular structures for machine learning models.
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1. ```C_alkyl O_ether c_6 1 c_6 c_6 c_6 ( - c_5-6 2 n_5-6 c_5-6 3 n_tertiary_amine_5-6 ( C_alkyl c_6 4 c_6 c_6 c_6 c_6 c_6 4 F_fluoro ) c_5-6 ( C_alkyl ) c_5-6 ( - c_5-6 4 c_5-6 c_5-6 c_5-6 5 c_5-6 ( c_5-6 4 ) O_ether_5-6 C_5-6 O_ether_5-6 5 ) c_amide_5-6 ( = O_amide ) n_amide_5-6 3 c_5-6 2 C_alkyl N_tertiary_amine ( C_alkyl ) C_alkyl C_alkyl c_6 2 c_6 c_6 c_6 c_6 c_6 2 ) c_6 c_6 1```
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2. ```C_alkyl C_tertiary_carbon ( C_alkyl ) C_tertiary_carbon ( C_ester ( = O_ester ) O_ester C_ester c_5-6 1 c_5-6 n_tertiary_amine_5-6 2 c_5-6 c_5-6 c_5-6 c_5-6 c_5-6 2 n_5-6 1 ) C_tertiary_carbon ( C_alkyl ) N_secondary_amine C_ester ( = O_ester ) O_ester C_ester ( C_alkyl ) ( C_alkyl ) C_alkyl```
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3. ```C_alkyl C_tertiary_carbon_6 1 C_6 N_tertiary_amine_6 ( c_5 2 n_5 n_5 c_5 ( C_tertiary_carbon_3 3 C_3 C_3 3 ) n_tertiary_amine_5 2 C_alkyl C_alkyl N_secondary_amine C_ketone ( = O_ketone ) c_5 2 c_5 c_5 c_5 s_5 2 ) C_6 C_amide_6 ( = O_amide ) N_amide_6 1 C_alkyl```
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4. ```C_alkyl O_ether C_alkyl C_alkyl n_tertiary_amine_5 1 c_5 ( C_alkyl c_5 2 c_5 c_5 ( C_alkyl ) n_secondary_amine_5 n_5 2 ) n_5 n_5 c_5 1 N_tertiary_amine_7 1 C_7 C_7 C_7 N_amide_7 ( C_amide ( = O_amide ) C_tertiary_carbon ( C_alkyl ) C_alkyl ) C_7 C_7 1```
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5. ```C_alkyl C_alkyl n_amide_6-6 1 c_amide_6-6 ( = O_amide ) n_6-6 c_6-6 ( O_hydroxyl ) c_6-6 2 c_6-6 ( C_ketone ( = O_ketone ) N_secondary_amine N_tertiary_amine_5 3 C_5 C_5 C_5 C_tertiary_carbon_5 3 C_alkyl O_ether C_alkyl ) c_6-6 c_6-6 ( C_tertiary_carbon ( C_alkyl ) C_alkyl ) n_6-6 c_6-6 1 2```
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6. ```C_alkyl C_tertiary_carbon_3 1 C_3 C_tertiary_carbon_3 1 C_alkyl n_tertiary_amine_5 1 c_5 ( C_alkyl c_6 2 c_6 c_6 n_6 c_6 c_6 2 ) n_5 n_5 c_5 1 N_tertiary_amine_6 1 C_6 C_6 N_tertiary_amine_6 ( C_alkyl C_amide ( = O_amide ) N_amide ( C_alkyl ) C_alkyl ) C_6 C_6 1```
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