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selfies
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| Metadata-Version: 2.1 | ||
| Name: selfies | ||
| Version: 1.0.2 | ||
| Version: 1.0.3 | ||
| Summary: SELFIES (SELF-referencIng Embedded Strings) is a general-purpose, sequence-based, robust representation of semantically constrained graphs. | ||
@@ -20,12 +20,12 @@ Home-page: https://github.com/aspuru-guzik-group/selfies | ||
| SELFIES (SELF-referencIng Embedded Strings) is a 100% robust molecular | ||
| string representation. | ||
| **Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation**<br> | ||
| _Mario Krenn, Florian Haese, AkshatKumar Nigam, Pascal Friederich, Alan Aspuru-Guzik_<br> | ||
| [*Machine Learning: Science and Technology* **1**, 045024 (2020)](https://iopscience.iop.org/article/10.1088/2632-2153/aba947), [extensive blog post January 2021](https://aspuru.substack.com/p/molecular-graph-representations-and).<br> | ||
| Major contributors since v1.0.0: _[Alston Lo](https://github.com/aspuru-guzik-group/selfies/commits?author=alstonlo) and [Seyone Chithrananda](https://github.com/seyonechithrananda)_ | ||
| A main objective is to use SELFIES as direct input into machine learning | ||
| models, in particular in generative models, for the generation of molecular | ||
| graphs which are syntactically and semantically valid. | ||
| A main objective is to use SELFIES as direct input into machine learning models,<br> | ||
| in particular in generative models, for the generation of molecular graphs<br> | ||
| which are syntactically and semantically valid. | ||
| See the paper by Mario Krenn, Florian Haese, AkshatKumar Nigam, | ||
| Pascal Friederich, and Alan Aspuru-Guzik at | ||
| https://arxiv.org/abs/1905.13741. | ||
| <center><img src="https://github.com/aspuru-guzik-group/selfies/blob/master/examples/VAE_LS_Validity.png" alt="SELFIES validity in a VAE latent space" width="666px"></center> | ||
@@ -104,3 +104,3 @@ | ||
| #### Label (Integer) encoding SELFIES: | ||
| #### Integer and one-hot encoding SELFIES: | ||
| In this example we first build an alphabet | ||
@@ -134,2 +134,8 @@ from a dataset of SELFIES, and then convert a SELFIES into a | ||
| enc_type='label')) | ||
| # [[0, 1, 0, 0, 0], [0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1]] | ||
| print(sf.selfies_to_encoding(dimethyl_ether, | ||
| vocab_stoi=symbol_to_idx, | ||
| pad_to_len=pad_to_len, | ||
| enc_type='one_hot')) | ||
| ``` | ||
@@ -136,0 +142,0 @@ |
+15
-9
@@ -12,12 +12,12 @@ # SELFIES | ||
| SELFIES (SELF-referencIng Embedded Strings) is a 100% robust molecular | ||
| string representation. | ||
| **Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation**<br> | ||
| _Mario Krenn, Florian Haese, AkshatKumar Nigam, Pascal Friederich, Alan Aspuru-Guzik_<br> | ||
| [*Machine Learning: Science and Technology* **1**, 045024 (2020)](https://iopscience.iop.org/article/10.1088/2632-2153/aba947), [extensive blog post January 2021](https://aspuru.substack.com/p/molecular-graph-representations-and).<br> | ||
| Major contributors since v1.0.0: _[Alston Lo](https://github.com/aspuru-guzik-group/selfies/commits?author=alstonlo) and [Seyone Chithrananda](https://github.com/seyonechithrananda)_ | ||
| A main objective is to use SELFIES as direct input into machine learning | ||
| models, in particular in generative models, for the generation of molecular | ||
| graphs which are syntactically and semantically valid. | ||
| A main objective is to use SELFIES as direct input into machine learning models,<br> | ||
| in particular in generative models, for the generation of molecular graphs<br> | ||
| which are syntactically and semantically valid. | ||
| See the paper by Mario Krenn, Florian Haese, AkshatKumar Nigam, | ||
| Pascal Friederich, and Alan Aspuru-Guzik at | ||
| https://arxiv.org/abs/1905.13741. | ||
| <center><img src="https://github.com/aspuru-guzik-group/selfies/blob/master/examples/VAE_LS_Validity.png" alt="SELFIES validity in a VAE latent space" width="666px"></center> | ||
@@ -96,3 +96,3 @@ | ||
| #### Label (Integer) encoding SELFIES: | ||
| #### Integer and one-hot encoding SELFIES: | ||
| In this example we first build an alphabet | ||
@@ -126,2 +126,8 @@ from a dataset of SELFIES, and then convert a SELFIES into a | ||
| enc_type='label')) | ||
| # [[0, 1, 0, 0, 0], [0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1]] | ||
| print(sf.selfies_to_encoding(dimethyl_ether, | ||
| vocab_stoi=symbol_to_idx, | ||
| pad_to_len=pad_to_len, | ||
| enc_type='one_hot')) | ||
| ``` | ||
@@ -128,0 +134,0 @@ |
| Metadata-Version: 2.1 | ||
| Name: selfies | ||
| Version: 1.0.2 | ||
| Version: 1.0.3 | ||
| Summary: SELFIES (SELF-referencIng Embedded Strings) is a general-purpose, sequence-based, robust representation of semantically constrained graphs. | ||
@@ -20,12 +20,12 @@ Home-page: https://github.com/aspuru-guzik-group/selfies | ||
| SELFIES (SELF-referencIng Embedded Strings) is a 100% robust molecular | ||
| string representation. | ||
| **Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation**<br> | ||
| _Mario Krenn, Florian Haese, AkshatKumar Nigam, Pascal Friederich, Alan Aspuru-Guzik_<br> | ||
| [*Machine Learning: Science and Technology* **1**, 045024 (2020)](https://iopscience.iop.org/article/10.1088/2632-2153/aba947), [extensive blog post January 2021](https://aspuru.substack.com/p/molecular-graph-representations-and).<br> | ||
| Major contributors since v1.0.0: _[Alston Lo](https://github.com/aspuru-guzik-group/selfies/commits?author=alstonlo) and [Seyone Chithrananda](https://github.com/seyonechithrananda)_ | ||
| A main objective is to use SELFIES as direct input into machine learning | ||
| models, in particular in generative models, for the generation of molecular | ||
| graphs which are syntactically and semantically valid. | ||
| A main objective is to use SELFIES as direct input into machine learning models,<br> | ||
| in particular in generative models, for the generation of molecular graphs<br> | ||
| which are syntactically and semantically valid. | ||
| See the paper by Mario Krenn, Florian Haese, AkshatKumar Nigam, | ||
| Pascal Friederich, and Alan Aspuru-Guzik at | ||
| https://arxiv.org/abs/1905.13741. | ||
| <center><img src="https://github.com/aspuru-guzik-group/selfies/blob/master/examples/VAE_LS_Validity.png" alt="SELFIES validity in a VAE latent space" width="666px"></center> | ||
@@ -104,3 +104,3 @@ | ||
| #### Label (Integer) encoding SELFIES: | ||
| #### Integer and one-hot encoding SELFIES: | ||
| In this example we first build an alphabet | ||
@@ -134,2 +134,8 @@ from a dataset of SELFIES, and then convert a SELFIES into a | ||
| enc_type='label')) | ||
| # [[0, 1, 0, 0, 0], [0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1]] | ||
| print(sf.selfies_to_encoding(dimethyl_ether, | ||
| vocab_stoi=symbol_to_idx, | ||
| pad_to_len=pad_to_len, | ||
| enc_type='one_hot')) | ||
| ``` | ||
@@ -136,0 +142,0 @@ |
@@ -28,3 +28,3 @@ #!/usr/bin/env python | ||
| __version__ = "1.0.1" | ||
| __version__ = "1.0.3" | ||
@@ -31,0 +31,0 @@ __all__ = [ |
@@ -129,4 +129,4 @@ from typing import Dict, Iterable, List, Set, Tuple, Union | ||
| _aromatic_valences = { | ||
| 'b': 3, 'c': 4, 'n': 5, 'p': 5, 'as': 5, | ||
| 'o': 6, 's': 6, 'se': 6, 'te': 6 | ||
| 'b': 3, 'al': 3, 'c': 4, 'si': 4, 'n': 5, 'p': 5, | ||
| 'as': 5, 'o': 6, 's': 6, 'se': 6, 'te': 6 | ||
| } | ||
@@ -133,0 +133,0 @@ |
+1
-1
@@ -10,3 +10,3 @@ #!/usr/bin/env python | ||
| name="selfies", | ||
| version="1.0.2", | ||
| version="1.0.3", | ||
| author="Mario Krenn", | ||
@@ -13,0 +13,0 @@ author_email="mario.krenn@utoronto.ca, alan@aspuru.com", |
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