Duplicate from GT4SD/regression_transformer

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) 2022 Generative Toolkit 4 Scientific Discovery
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: Regression Transformer
+emoji: 💡
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 3.9.1
+app_file: app.py
+pinned: false
+python_version: 3.8.13
+pypi_version: 20.2.4
+duplicated_from: GT4SD/regression_transformer
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+

app.py

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+import logging
+import pathlib
+
+import gradio as gr
+import pandas as pd
+from gt4sd.algorithms.conditional_generation.regression_transformer import (
+    RegressionTransformer,
+)
+from gt4sd.algorithms.registry import ApplicationsRegistry
+from utils import (
+    draw_grid_generate,
+    draw_grid_predict,
+    get_application,
+    get_inference_dict,
+    get_rt_name,
+)
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+
+def regression_transformer(
+    algorithm: str,
+    task: str,
+    target: str,
+    number_of_samples: int,
+    search: str,
+    temperature: float,
+    tolerance: int,
+    wrapper: bool,
+    fraction_to_mask: float,
+    property_goal: str,
+    tokens_to_mask: str,
+    substructures_to_mask: str,
+    substructures_to_keep: str,
+):
+
+    if task == "Predict" and wrapper:
+        logger.warning(
+            f"For prediction, no sampling_wrapper will be used, ignoring: fraction_to_mask: {fraction_to_mask}, "
+            f"tokens_to_mask: {tokens_to_mask}, substructures_to_mask={substructures_to_mask}, "
+            f"substructures_to_keep: {substructures_to_keep}."
+        )
+        sampling_wrapper = {}
+    elif not wrapper:
+        sampling_wrapper = {}
+    else:
+        substructures_to_mask = (
+            []
+            if substructures_to_mask == ""
+            else substructures_to_mask.replace(" ", "").split(",")
+        )
+        substructures_to_keep = (
+            []
+            if substructures_to_keep == ""
+            else substructures_to_keep.replace(" ", "").split(",")
+        )
+        tokens_to_mask = [] if tokens_to_mask == "" else tokens_to_mask.split(",")
+
+        property_goals = {}
+        if property_goal == "":
+            raise ValueError(
+                "For conditional generation you have to specify `property_goal`."
+            )
+        for line in property_goal.split(","):
+            property_goals[line.split(":")[0].strip()] = float(line.split(":")[1])
+
+        sampling_wrapper = {
+            "substructures_to_keep": substructures_to_keep,
+            "substructures_to_mask": substructures_to_mask,
+            "text_filtering": False,
+            "fraction_to_mask": fraction_to_mask,
+            "property_goal": property_goals,
+        }
+    algorithm_application = get_application(algorithm.split(":")[0])
+    algorithm_version = algorithm.split(" ")[-1].lower()
+    config = algorithm_application(
+        algorithm_version=algorithm_version,
+        search=search.lower(),
+        temperature=temperature,
+        tolerance=tolerance,
+        sampling_wrapper=sampling_wrapper,
+    )
+    model = RegressionTransformer(configuration=config, target=target)
+    samples = list(model.sample(number_of_samples))
+    if algorithm_version == "polymer" and task == "Generate":
+        correct_samples = [(s, p) for s, p in samples if "." in s]
+        while len(correct_samples) < number_of_samples:
+            samples = list(model.sample(number_of_samples))
+            correct_samples.extend(
+                [
+                    (s, p)
+                    for s, p in samples
+                    if "." in s and (s, p) not in correct_samples
+                ]
+            )
+        samples = correct_samples
+    if task == "Predict":
+        return draw_grid_predict(samples[0], target, domain=algorithm.split(":")[0])
+    else:
+        return draw_grid_generate(samples, domain=algorithm.split(":")[0])
+
+
+if __name__ == "__main__":
+
+    # Preparation (retrieve all available algorithms)
+    all_algos = ApplicationsRegistry.list_available()
+    rt_algos = list(
+        filter(lambda x: "RegressionTransformer" in x["algorithm_name"], all_algos)
+    )
+    rt_names = list(map(get_rt_name, rt_algos))
+
+    properties = {}
+    for algo in rt_algos:
+        application = get_application(
+            algo["algorithm_application"].split("Transformer")[-1]
+        )
+        data = get_inference_dict(
+            application=application, algorithm_version=algo["algorithm_version"]
+        )
+        properties[get_rt_name(algo)] = data
+    properties
+
+    # Load metadata
+    metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
+
+    examples = pd.read_csv(
+        metadata_root.joinpath("regression_transformer_examples.csv"), header=None
+    ).fillna("")
+
+    with open(metadata_root.joinpath("regression_transformer_article.md"), "r") as f:
+        article = f.read()
+    with open(
+        metadata_root.joinpath("regression_transformer_description.md"), "r"
+    ) as f:
+        description = f.read()
+
+    demo = gr.Interface(
+        fn=regression_transformer,
+        title="Regression Transformer",
+        inputs=[
+            gr.Dropdown(rt_names, label="Algorithm version", value="Molecules: Qed"),
+            gr.Radio(choices=["Predict", "Generate"], label="Task", value="Generate"),
+            gr.Textbox(
+                label="Input", placeholder="CC(C#C)N(C)C(=O)NC1=CC=C(Cl)C=C1", lines=1
+            ),
+            gr.Slider(
+                minimum=1, maximum=50, value=10, label="Number of samples", step=1
+            ),
+            gr.Radio(choices=["Sample", "Greedy"], label="Search", value="Sample"),
+            gr.Slider(minimum=0.5, maximum=2, value=1, label="Decoding temperature"),
+            gr.Slider(minimum=5, maximum=100, value=30, label="Tolerance", step=1),
+            gr.Radio(choices=[True, False], label="Sampling Wrapper", value=True),
+            gr.Slider(minimum=0, maximum=1, value=0.5, label="Fraction to mask"),
+            gr.Textbox(label="Property goal", placeholder="<qed>:0.75", lines=1),
+            gr.Textbox(label="Tokens to mask", placeholder="N, C", lines=1),
+            gr.Textbox(
+                label="Substructures to mask", placeholder="C(=O), C#C", lines=1
+            ),
+            gr.Textbox(
+                label="Substructures to keep", placeholder="C1=CC=C(Cl)C=C1", lines=1
+            ),
+        ],
+        outputs=gr.HTML(label="Output"),
+        article=article,
+        description=description,
+        examples=examples.values.tolist(),
+    )
+    demo.launch(debug=True, show_error=True)

model_cards/regression_transformer_article.md

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+# Model documentation & parameters
+
+## Parameters
+
+### Algorithm Version
+Which model checkpoint to use (trained on different datasets).
+
+### Task
+Whether the multitask model should be used for property prediction or conditional generation (default).
+
+### Input
+The input sequence. In the default setting (where `Task` is *Generate* and `Sampling Wrapper` is *True*) this can be a seed SMILES (for the molecule models) or amino-acid sequence (for the protein models). The model will locally adapt the seed sequence by masking `Fraction to mask` of the tokens.
+If the `Task` is *Predict*, the sequences are given as SELFIES for the molecule models. Moreover, the tokens that should be predicted (`[MASK]` in the input) have to be given explicitly. Populate the examples to understand better. 
+NOTE: When setting `Task` to *Generate*, and `Sampling Wrapper` to *False*, the user has maximal control about the generative process and can explicitly decide which tokens should be masked.
+
+### Number of samples
+How many samples should be generated (between 1 and 50). If `Task` is *Predict*, this has to be set to 1.
+
+### Search
+Decoding search method. Use *Sample* if `Task` is *Generate*. If `Task` is *Predict*, use *Greedy*.
+
+### Tolerance
+Precision tolerance; only used if `Task` is *Generate*. This is a single float between 0 and 100 for the the tolerated deviation between desired/primed property and predicted property of the generated molecule. Given in percentage with respect to the property range encountered during training.
+NOTE: The tolerance is *only* used for post-hoc filtering of the generated samples.
+
+### Sampling Wrapper
+Only used if `Task` is *Generate*. If set to *False*, the user has to provide a full RT-sequence as `Input` and has to **explicitly** decide which tokens are masked (see example below). This gives full control but is tedious. Instead, if `Sampling Wrapper` is set to *True*, the RT stochastically determines which parts of the sequence are masked.
+**NOTE**: All below arguments only apply if `Sampling Wrapper` is *True*.
+
+#### Fraction to mask
+Specifies the ratio of tokens that can be changed by the model. Argument only applies if `Task` is *Generate* and `Sampling Wrapper` is *True*.
+
+#### Property goal
+Specifies the desired target properties for the generation. Need to be given in the format `<prop>:value`. If the model supports multiple properties, give them separated by a comma `,`. Argument only applies if `Task` is *Generate* and `Sampling Wrapper` is *True*.
+
+#### Tokens to mask
+Optionally specifies which tokens (atoms, bonds etc) can be masked. Please separate multiple tokens by comma (`,`). If not specified, all tokens can be masked. Argument only applies if `Task` is *Generate* and `Sampling Wrapper` is *True*.
+
+#### Substructures to mask
+Optionally specifies a list of substructures that should *definitely* be masked (excluded from stochastic masking). Given in SMILES format. If multiple are provided, separate by comma (`,`). Argument only applies if `Task` is *Generate* and `Sampling Wrapper` is *True*.
+*NOTE*: Most models operate on SELFIES and the matching of the substructures occurs in SELFIES simply on a string level.
+
+#### Substructures to keep
+Optionally specifies a list of substructures that should definitely be present in the target sample (i.e., excluded from stochastic masking). Given in SMILES format. Argument only applies if `Task` is *Generate* and `Sampling Wrapper` is *True*.
+*NOTE*: This keeps tokens even if they are included in `tokens_to_mask`.
+*NOTE*: Most models operate on SELFIES and the matching of the substructures occurs in SELFIES simply on a string level.
+
+
+
+# Model card -- Regression Transformer
+
+**Model Details**: The [Regression Transformer](https://arxiv.org/abs/2202.01338) is a multitask Transformer that reformulates regression as a conditional sequence modeling task. This yields a dichotomous language model that seamlessly integrates property prediction with property-driven conditional generation.
+
+**Developers**: Jannis Born and Matteo Manica from IBM Research.
+
+**Distributors**: Original authors' code wrapped and distributed by GT4SD Team (2023) from IBM Research.
+
+**Model date**: Preprint released in 2022, currently under review at *Nature Machine Intelligence*.
+
+**Algorithm version**: Models trained and distributed by the original authors.
+- **Molecules: QED**: Model trained on 1.6M molecules (SELFIES) from ChEMBL and their QED scores.
+- **Molecules: Solubility**: QED model finetuned on the ESOL dataset from [Delaney et al (2004), *J. Chem. Inf. Comput. Sci.*](https://pubs.acs.org/doi/10.1021/ci034243x) to predict water solubility. Model trained on augmented SELFIES.
+- **Molecules: USPTO**: Model trained on 2.8M [chemical reactions](https://figshare.com/articles/dataset/Chemical_reactions_from_US_patents_1976-Sep2016_/5104873) from the US patent office. The model used SELFIES and a synthetic property (total molecular weight of all precursors).
+- **Molecules: Polymer**: Model finetuned on 600 ROPs (ring-opening polymerizations) with monomer-catalyst pairs. Model used three properties: conversion (`<conv>`), PDI (`<pdi>`) and Molecular Weight (`<molwt>`). Model trained with augmented SELFIES, optimized only to generate catalysts, given a monomer and the property constraints. See the example for details.
+- **Molecules: Cosmo_acdl**: Model finetuned on 56k molecules with two properties (*pKa_ACDL* and *pKa_COSMO*). Model used augmented SELFIES.
+- **Molecules: Pfas**: Model finetuned on ~1k PFAS (Perfluoroalkyl and Polyfluoroalkyl Substances) molecules with 9 properties including some experimentally measured ones (biodegradability, LD50 etc) and some synthetic ones (SCScore, molecular weight). Model trained on augmented SELFIES.
+- **Molecules: Logp_and_synthesizability**: Model trained on 2.9M molecules (SELFIES) from PubChem with **two** synthetic properties, the logP (partition coefficient) and the [SCScore by Coley et al. (2018); *J. Chem. Inf. Model.*](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00622?casa_token=JZzOrdWlQ_QAAAAA%3A3_ynCfBJRJN7wmP2gyAR0EWXY-pNW_l-SGwSSU2SGfl5v5SxcvqhoaPNDhxq4THberPoyyYqTZELD4Ck)
+- **Molecules: Crippen_logp**: Model trained on 2.9M molecules (SMILES) from PubChem, but *only* on logP (partition coefficient).
+- **Proteins: Stability**: Model pretrained on 2.6M peptides from UniProt with the Boman index as property. Finetuned on the [**Stability**](https://www.science.org/doi/full/10.1126/science.aan0693) dataset from the [TAPE benchmark](https://proceedings.neurips.cc/paper/2019/hash/37f65c068b7723cd7809ee2d31d7861c-Abstract.html) which has ~65k samples.
+
+**Model type**: A Transformer-based language model that is trained on alphanumeric sequence to simultaneously perform sequence regression or conditional sequence generation.
+
+**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
+All models are trained with an alternated training scheme that alternated between optimizing the cross-entropy loss on the property tokens ("regression") or the self-consistency objective on the molecular tokens. See the [Regression Transformer](https://arxiv.org/abs/2202.01338) paper for details.
+
+**Paper or other resource for more information**: 
+The [Regression Transformer](https://arxiv.org/abs/2202.01338) paper. See the [source code](https://github.com/IBM/regression-transformer) for details.
+
+**License**: MIT
+
+**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).
+
+**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular drug discovery.
+
+**Primary intended uses/users**: Researchers and computational chemists using the model for model comparison or research exploration purposes.
+
+**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
+
+**Factors**: Not applicable.
+
+**Metrics**: High predictive power for the properties of that specific algorithm version.
+
+**Datasets**: Different ones, as described under **Algorithm version**.
+
+**Ethical Considerations**: No specific considerations as no private/personal data is involved. Please consult with the authors in case of questions.
+
+**Caveats and Recommendations**: Please consult with original authors in case of questions.
+
+Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
+
+
+## Citation
+
+```bib
+@article{born2022regression,
+  title={Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens},
+  author={Born, Jannis and Manica, Matteo},
+  journal={arXiv preprint arXiv:2202.01338},
+  note={Spotlight talk at ICLR workshop on Machine Learning for Drug Discovery},
+  year={2022}
+}
+```
+

model_cards/regression_transformer_description.md

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+
+
+<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
+
+### Concurrent sequence regression and generation for molecular language modeling
+
+The [Regression Transformer](https://arxiv.org/abs/2202.01338) is a multitask Transformer that reformulates regression as a conditional sequence modeling task.
+This yields a dichotomous language model that seamlessly integrates property prediction with property-driven conditional generation. For details see the [arXiv preprint](https://arxiv.org/abs/2202.01338), the [development code](https://github.com/IBM/regression-transformer) and the [GT4SD endpoint](https://github.com/GT4SD/gt4sd-core) for inference.
+
+Each `algorithm_version` refers to one trained model. Each model can be used for **two tasks**, either to *predict* one (or multiple) properties of a molecule or to *generate* a molecule (given a seed molecule and a property constraint).
+
+For **examples** and **documentation** of the model parameters, please see below.
+Moreover, we provide a **model card** ([Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)) at the bottom of this page.

model_cards/regression_transformer_examples.csv

@@ -0,0 +1,9 @@
+Molecules: Logp_and_synthesizability,Generate,CCOC1=NC=NC(=C1C)NCCOC(C)C,3,Sample,1.2,20,True,0.3,"<logp>:0.390, <scs>:2.628",N,(C)C,CCO
+Molecules: Qed,Generate,CC(C#C)N(C)C(=O)NC1=CC=C(Cl)C=C1,10,Sample,1.0,30,True,0.5,<qed>:0.75,"N, C","C(=O), CC",C1=CC=C(Cl)C=C1
+Molecules: Logp_and_synthesizability,Predict,<logp>[MASK][MASK][MASK][MASK][MASK]|<scs>[MASK][MASK][MASK][MASK][MASK]|[C][C][O][C][=N][C][=N][C][Branch1_2][Branch1_1][=C][Ring1][Branch1_2][C][N][C][C][O][C][Branch1_1][C][C][C],1,Greedy,1.0,30,False,0.0,,,,
+Proteins: Stability,Predict,<stab>[MASK][MASK][MASK][MASK][MASK]|GSQEVNSGTQTYKNASPEEAERIARKAGATTWTEKGNKWEIRI,1,Greedy,1.0,1,False,0.0,,,,
+Proteins: Stability,Generate,GSQEVNSGTQTYKNASPEEAERIARKAGATTWTEKGNKWEIRI,10,Sample,1.2,30,True,0.3,<stab>:0.393,,SQEVNSGTQTYKN,WTEK
+Molecules: Qed,Generate,<qed>0.717|[MASK][MASK][MASK][MASK][MASK][C][Branch2_1][Ring1][Ring1][MASK][MASK][=C][C][Branch1_1][C][C][=N][C][MASK][MASK][=C][C][=C][Ring1][O][Ring1][Branch1_2][=C][Ring2][MASK][MASK],10,Sample,1.2,30,False,0.0,,,,
+Molecules: Solubility,Generate,ClC(Cl)C(Cl)Cl,5,Sample,1.3,40,True,0.4,<esol>:0.754,,,
+Molecules: Polymer,Predict,<conv>[MASK][MASK][MASK][MASK]|<pdi>[MASK][MASK][MASK][MASK][MASK]|<molwt>[MASK][MASK][MASK][MASK][MASK]|[C][Branch1_2][C][=O][O][C@@Hexpl][Branch1_1][C][C][C][Branch1_2][C][=O][O][C@Hexpl][Ring1][Branch2_2][C].[C][C][C][Branch2_1][Ring1][Ring1][N][C][Branch1_1][=C][N][C][=C][C][=C][Branch1_1][Ring1][O][C][C][=C][Ring1][Branch2_1][=S][C][C][C][Ring2][Ring1][C],1,Greedy,1,0,False,,,,,
+Molecules: Polymer,Generate,C1(=O)O[C@@H](C)C(=O)O[C@H]1C.C2CC(NC(NC1=CC=C(OC)C=C1)=S)CCC2,10,Sample,1.3,50,True,0.5,"<pdi>:3.490, <conv>:0.567, <molwt>:3.567",,,C1(=O)O[C@@H](C)C(=O)O[C@H]1C

requirements.txt

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+-f https://download.pytorch.org/whl/cpu/torch_stable.html
+-f https://data.pyg.org/whl/torch-1.12.1+cpu.html
+# pip==20.2.4
+torch==1.12.1
+torch-scatter
+torch-spline-conv
+torch-sparse
+torch-geometric
+torchvision==0.13.1
+torchaudio==0.12.1
+gt4sd>=1.0.6
+molgx>=0.22.0a1
+molecule_generation
+nglview
+PyTDC==0.3.7
+gradio>=3.9
+markdown-it-py>=2.1.0
+mols2grid>=0.2.0
+pandas>=1.0.0
+terminator @ git+https://github.com/IBM/regression-transformer@gt4sd
+guacamol_baselines @ git+https://github.com/GT4SD/[email protected]
+moses @ git+https://github.com/GT4SD/[email protected]
+paccmann_chemistry @ git+https://github.com/PaccMann/[email protected]
+paccmann_generator @ git+https://github.com/PaccMann/[email protected]
+paccmann_gp @ git+https://github.com/PaccMann/[email protected]
+paccmann_omics @ git+https://github.com/PaccMann/[email protected]
+paccmann_predictor @ git+https://github.com/PaccMann/paccmann_predictor@sarscov2
+reinvent_models @ git+https://github.com/GT4SD/[email protected]

utils.py

@@ -0,0 +1,172 @@
+import json
+import logging
+import os
+from collections import defaultdict
+from typing import Dict, List, Tuple
+
+import mols2grid
+import pandas as pd
+from gt4sd.algorithms import (
+    RegressionTransformerMolecules,
+    RegressionTransformerProteins,
+)
+from gt4sd.algorithms.core import AlgorithmConfiguration
+from rdkit import Chem
+from terminator.selfies import decoder
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+
+def get_application(application: str) -> AlgorithmConfiguration:
+    """
+    Convert application name to AlgorithmConfiguration.
+
+    Args:
+        application: Molecules or Proteins
+
+    Returns:
+        The corresponding AlgorithmConfiguration
+    """
+    if application == "Molecules":
+        application = RegressionTransformerMolecules
+    elif application == "Proteins":
+        application = RegressionTransformerProteins
+    else:
+        raise ValueError(
+            "Currently only models for molecules and proteins are supported"
+        )
+    return application
+
+
+def get_inference_dict(
+    application: AlgorithmConfiguration, algorithm_version: str
+) -> Dict:
+    """
+    Get inference dictionary for a given application and algorithm version.
+
+    Args:
+        application: algorithm application (Molecules or Proteins)
+        algorithm_version: algorithm version (e.g. qed)
+
+    Returns:
+        A dictionary with the inference parameters.
+    """
+    config = application(algorithm_version=algorithm_version)
+    with open(os.path.join(config.ensure_artifacts(), "inference.json"), "r") as f:
+        data = json.load(f)
+    return data
+
+
+def get_rt_name(x: Dict) -> str:
+    """
+    Get the UI display name of the regression transformer.
+
+    Args:
+        x: dictionary with the inference parameters
+
+    Returns:
+        The display name
+    """
+    return (
+        x["algorithm_application"].split("Transformer")[-1]
+        + ": "
+        + x["algorithm_version"].capitalize()
+    )
+
+
+def draw_grid_predict(prediction: str, target: str, domain: str) -> str:
+    """
+    Uses mols2grid to draw a HTML grid for the prediction
+
+    Args:
+        prediction: Predicted sequence.
+        target: Target molecule
+        domain: Domain of the prediction (molecules or proteins)
+
+    Returns:
+        HTML to display
+    """
+
+    if domain not in ["Molecules", "Proteins"]:
+        raise ValueError(f"Unsupported domain {domain}")
+
+    seq = target.split("|")[-1]
+    converter = (
+        decoder
+        if domain == "Molecules"
+        else lambda x: Chem.MolToSmiles(Chem.MolFromFASTA(x))
+    )
+    try:
+        seq = converter(seq)
+    except Exception:
+        logger.warning(f"Could not draw sequence {seq}")
+
+    result = {"SMILES": [seq], "Name": ["Target"]}
+    # Add properties
+    for prop in prediction.split("<")[1:]:
+        result[
+            prop.split(">")[0]
+        ] = f"{prop.split('>')[0].capitalize()} = {prop.split('>')[1]}"
+    result_df = pd.DataFrame(result)
+    obj = mols2grid.display(
+        result_df,
+        tooltip=list(result.keys()),
+        height=900,
+        n_cols=1,
+        name="Results",
+        size=(600, 700),
+    )
+    return obj.data
+
+
+def draw_grid_generate(
+    samples: List[Tuple[str]], domain: str, n_cols: int = 5, size=(140, 200)
+) -> str:
+    """
+    Uses mols2grid to draw a HTML grid for the generated molecules
+
+    Args:
+        samples: The generated samples (with properties)
+        domain: Domain of the prediction (molecules or proteins)
+        n_cols: Number of columns in grid. Defaults to 5.
+        size: Size of molecule in grid. Defaults to (140, 200).
+
+    Returns:
+        HTML to display
+    """
+
+    if domain not in ["Molecules", "Proteins"]:
+        raise ValueError(f"Unsupported domain {domain}")
+
+    if domain == "Proteins":
+        try:
+            smis = list(
+                map(lambda x: Chem.MolToSmiles(Chem.MolFromFASTA(x[0])), samples)
+            )
+        except Exception:
+            logger.warning(f"Could not convert some sequences {samples}")
+    else:
+        smis = [s[0] for s in samples]
+
+    result = defaultdict(list)
+    result.update({"SMILES": smis, "Name": [f"sample_{i}" for i in range(len(smis))]})
+
+    # Create properties
+    properties = [s.split("<")[1] for s in samples[0][1].split(">")[:-1]]
+    # Fill properties
+    for sample in samples:
+        for prop in properties:
+            value = float(sample[1].split(prop)[-1][1:].split("<")[0])
+            result[prop].append(f"{prop} = {value}")
+
+    result_df = pd.DataFrame(result)
+    obj = mols2grid.display(
+        result_df,
+        tooltip=list(result.keys()),
+        height=1100,
+        n_cols=n_cols,
+        name="Results",
+        size=size,
+    )
+    return obj.data