mgyigit commited on
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1 Parent(s): 8dde4ec

Update app.py

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  1. app.py +3 -3
app.py CHANGED
@@ -234,7 +234,7 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
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  This is a general-purpose model that generates diverse drug-like molecules without targeting a specific protein. Trained with a general [ChEMBL dataset]((https://drive.google.com/file/d/1oyybQ4oXpzrme_n0kbwc0-CFjvTFSlBG/view?usp=drive_link)
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  Molecules larger than 45 heavy atoms were excluded.
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- - Useful for exploring chemical space, generating diverse scaffolds, and creating molecules with drug-like properties.
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  For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
@@ -256,7 +256,7 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
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  discriminator during training.
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  ### Structural Metrics
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- - **Average Length**: Normalized average number of atoms in the generated molecules, normalized by the maximum atom count (e.g., 45 for AKT1/NoTarget, 38 for CDK2)
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  - **Mean Atom Type**: Average number of distinct atom types in the generated molecules
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  - **Internal Diversity**: Diversity within the generated set (higher is more diverse)
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@@ -302,7 +302,7 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
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  with gr.TabItem("Custom Input SMILES"):
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  custom_smiles = gr.Textbox(
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  label="Input SMILES (one per line, maximum 100 molecules)",
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- info="This space runs on a CPU, which may result in slower performance. Generating 100 molecules takes approximately 6 minutes. Therefore, we set a 100-molecule cap.\n molecules larger than allowed maximum length (45 for AKT1/NoTarget and 38 for CDK2) and allowed atom types are going to be filtered.",
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  placeholder="C(C(=O)O)N\nCCO\n...",
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  lines=10
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  )
 
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  This is a general-purpose model that generates diverse drug-like molecules without targeting a specific protein. Trained with a general [ChEMBL dataset]((https://drive.google.com/file/d/1oyybQ4oXpzrme_n0kbwc0-CFjvTFSlBG/view?usp=drive_link)
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  Molecules larger than 45 heavy atoms were excluded.
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+ - Useful for exploring chemical space, generating diverse scaffolds, and creating molecules with drug-like properties.
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  For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
 
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  discriminator during training.
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  ### Structural Metrics
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+ - **Average Length**: Normalized average number of atoms in the generated molecules, normalized by the maximum number of atoms (e.g., 45 for AKT1/NoTarget, 38 for CDK2)
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  - **Mean Atom Type**: Average number of distinct atom types in the generated molecules
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  - **Internal Diversity**: Diversity within the generated set (higher is more diverse)
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  with gr.TabItem("Custom Input SMILES"):
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  custom_smiles = gr.Textbox(
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  label="Input SMILES (one per line, maximum 100 molecules)",
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+ info="This space runs on a CPU, which may result in slower performance. Generating 100 molecules takes approximately 6 minutes. Therefore, we set a 100-molecule cap.\n\n Molecules larger than allowed maximum length (45 for AKT1/NoTarget and 38 for CDK2) and allowed atom types are going to be filtered.\n\n Novelty (Inference) metric will be calculated using these input smiles.",
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  placeholder="C(C(=O)O)N\nCCO\n...",
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  lines=10
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  )