update

README.md

@@ -1,5 +1,5 @@
 ---
-title: Regression Transformer
+title: Molecular properties
 emoji: 💡
 colorFrom: green
 colorTo: blue

app.py

@@ -2,168 +2,98 @@ import logging
 import pathlib
 
 import gradio as gr
+import numpy as np
 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,
-)
+from gt4sd.properties.molecules import MOLECULE_PROPERTY_PREDICTOR_FACTORY
+
+from utils import draw_grid_predict
 
 logger = logging.getLogger(__name__)
 logger.addHandler(logging.NullHandler())
 
+REMOVE = ["docking", "docking_tdc", "molecule_one", "askcos", "plogp"]
+REMOVE.extend(["similarity_seed", "activity_against_target", "organtox"])
 
-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_PROP_DESCRIPTION = {
+    "Tox21": "NR-AR, NR-AR-LBD, NR-AhR, NR-Aromatase, NR-ER, NR-ER-LBD, NR-PPAR-gamma, SR-ARE, SR-ATAD5, SR-HSE, SR-MMP, SR-p53",
+    "Sider": "Hepatobiliary disorders,Metabolism and nutrition disorders,Product issues,Eye disorders,Investigations,Musculoskeletal disorders,Gastrointestinal disorders,Social circumstances,Immune system disorders,Reproductive system and breast disorders,Bening & malignant,General disorders,Endocrine disorders,Surgical & medical procedures,Vascular disorders,Blood & lymphatic disorders,Skin & subcutaneous disorders,Congenital & genetic disorders,Infections,Respiratory & thoracic disorders,Psychiatric disorders,Renal & urinary disorders,Pregnancy conditions,Ear disorders,Cardiac disorders,Nervous system disorders,Injury & procedural complications",
+    "Clintox": "FDA approval, Clinical trial failure",
+}
+
+
+def main(property: str, smiles: str, smiles_file: str):
+
+    algo, config = MOLECULE_PROPERTY_PREDICTOR_FACTORY[property.lower()]
+    kwargs = (
+        {"algorithm_version": "v0"} if property in MODEL_PROP_DESCRIPTION.keys() else {}
     )
-    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])
+    model = algo(config(**kwargs))
+    if smiles is not None and smiles_file is not None:
+        raise ValueError("Pass either smiles or smiles_file, not both.")
+    elif smiles is not None:
+        smiles = [smiles]
+    elif smiles_file is not None:
+        smiles = pd.read_csv(smiles_file.name, header=None, sep="\t")[0].tolist()
+    props = np.array(list(map(model, smiles))).round(2)
+
+    # Expand to 2D array if needed
+    if len(props.shape) == 1:
+        props = np.expand_dims(np.array(props), -1)
+
+    if property in MODEL_PROP_DESCRIPTION.keys():
+        property_names = MODEL_PROP_DESCRIPTION[property].split(",")
     else:
-        return draw_grid_generate(samples, domain=algorithm.split(":")[0])
+        property_names = [property]
+
+    return draw_grid_predict(
+        smiles, props, property_names=property_names, domain="Molecules"
+    )
 
 
 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
+    properties = list(MOLECULE_PROPERTY_PREDICTOR_FACTORY.keys())[::-1]
+    for prop in REMOVE:
+        prop_to_idx = dict(zip(properties, range(len(properties))))
+        properties.pop(prop_to_idx[prop])
+    properties = list(map(lambda x: x.capitalize(), 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("")
+    examples = [
+        ["Qed", None, metadata_root.joinpath("examples.smi")],
+        [
+            "Esol",
+            "CN1CCN(CCCOc2ccc(N3C(=O)C(=Cc4ccc(Oc5ccc([N+](=O)[O-])cc5)cc4)SC3=S)cc2)CC1",
+            None,
+        ],
+    ]
 
-    with open(metadata_root.joinpath("regression_transformer_article.md"), "r") as f:
+    with open(metadata_root.joinpath("article.md"), "r") as f:
         article = f.read()
-    with open(
-        metadata_root.joinpath("regression_transformer_description.md"), "r"
-    ) as f:
+    with open(metadata_root.joinpath("description.md"), "r") as f:
         description = f.read()
 
     demo = gr.Interface(
-        fn=regression_transformer,
-        title="Regression Transformer",
+        fn=main,
+        title="Molecular properties",
         inputs=[
-            gr.Dropdown(rt_names, label="Algorithm version", value="Molecules: Qed"),
-            gr.Radio(choices=["Predict", "Generate"], label="Task", value="Generate"),
+            gr.Dropdown(properties, label="Property", value="qed"),
             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
+                label="Single SMILES",
+                placeholder="CC(C#C)N(C)C(=O)NC1=CC=C(Cl)C=C1",
+                lines=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
+            gr.File(
+                file_types=[".smi"],
+                label="Multiple SMILES (tab-separated, `.smi` file)",
             ),
         ],
         outputs=gr.HTML(label="Output"),
         article=article,
         description=description,
-        examples=examples.values.tolist(),
+        examples=examples,
     )
     demo.launch(debug=True, show_error=True)

model_cards/article.md

@@ -0,0 +1,68 @@
+# Supported molecular properties
+
+
+### ClinTox
+A [ToxSmi model](https://github.com/PaccMann/toxsmi) trained on [ClinTox](https://moleculenet.org/datasets-1) dataset which has two endpoints: Probability of FDA approval and Probability of failure in clinical trials. When using this model, please cite [*Born et al. (2023)](#toxsmi-citation).
+
+### SIDER
+A [ToxSmi model](https://github.com/PaccMann/toxsmi) trained on the [SIDER](https://moleculenet.org/datasets-1) dataset for 27 different types of side effects of drugs. When using this model, please cite [*Born et al. (2023)](#toxsmi-citation).
+
+### Tox21
+A [ToxSmi model](https://github.com/PaccMann/toxsmi) trained on the [Tox21](https://tripod.nih.gov/tox/) dataset with 12 different types of environmental toxicities. When using this model, please cite [*Born et al. (2023)](#toxsmi-citation).
+
+### SCScore
+Predict the synthetic complexity score (SCScore) as presented in [Coley et al. (*J. Chem. Inf. Model.*; 2018)](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00622).
+
+### SAS
+Estimate the synthetic accessibility score (SAS) as presented in [Ertl et al. (*Journal of Chemoinformatics*; 2009)](https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8).
+
+### Lipinski
+Measure whether a molecule confirms to the Lipinski-rule-of-five as presented in [Lipinski et al. (*Advanced Drug Delivery Reviews*; 2001)](https://www.sciencedirect.com/science/article/abs/pii/S0169409X00001290?via%3Dihub).
+
+### Penalized logP
+Measure the penalized logP (partition coefficient) score as presented in [Gomez-Bombarelli et al. (*ACS Central Science*; 2018)](https://arxiv.org/abs/1610.02415v1). This is the logP minus the number of rings with > 6 atoms minus the SAS.
+
+### QED
+Measure the drug-likeness as presented in [Bickerton et al. (*Nature Chemistry*; 2012)](https://www.nature.com/articles/nchem.1243).
+
+### LogP
+Measure the logP (partition coefficient) of a molecule as presented in [Wildman et al. (*J. Chem. Inf. Comput. Sci.*; 1999)](https://pubs.acs.org/doi/full/10.1021/ci990307l).
+
+### Bertz
+Calculate the total polar surface area of a molecule as presented in [Ertl et al. (*Journal of Medicinal Chemistry*; 2000)](https://pubs.acs.org/doi/full/10.1021/jm000942e).
+
+### TPSA
+Calculate the first general index of molecular complexity [Bertz (*Journal of the American Chemical Society*; 1981)](https://pubs.acs.org/doi/pdf/10.1021/ja00402a071).
+
+### Is-Scaffold
+Whether the molecule is identical to its [Murcko scaffold](https://rdkit.org/docs/source/rdkit.Chem.Scaffolds.MurckoScaffold.html).
+
+### Number-Of-X
+Calculated with [RDKit](https://www.rdkit.org/docs/source/rdkit.Chem.rdchem.html).
+
+### Molecular Weight
+Calculated with [RDKit](https://www.rdkit.org/docs/source/rdkit.Chem.rdchem.html).
+
+
+### ToxSmi citation
+```bib
+@article{born2023chemical,
+  title={Chemical representation learning for toxicity prediction},
+  author={Born, Jannis and Markert, Greta and Janakarajan, Nikita and Kimber, Talia B. and Volkamer, Andrea and Rodriguez Martinez, Maria and Manica, Matteo},
+  journal={Under review at Digital Discovery},
+  year={2023}
+}
+```
+
+
+### Unsupported properties
+The following molecular properties are available via the GT4SD API but not in this UI:
+- [MoleculeOne](https://tdcommons.ai/functions/oracles/#moleculeone) endpoint for retrosynthesis
+- [ASKCOS](https://tdcommons.ai/functions/oracles/#askcos) endpoint for retrosynthesis
+- [TDC-Docking](https://tdcommons.ai/functions/oracles/#docking-scores) endpoint for docking against a user-provided target
+- [TDC-Docking](https://tdcommons.ai/functions/oracles/#docking-scores) endpoint for docking against *3pbl*.
+- [Protein-ligand binding](https://tdcommons.ai/functions/oracles/#dopamine-receptor-d2-drd2) against one of the targets *drd2*, *gsk3b*, *jnk3*, *fpscores*, *cyp3a4_veith*, *drd2_current*, *gsk3b_current* or *jnk3_current*.
+- [Tanimoto similarity](https://tdcommons.ai/functions/oracles/#similaritydissimilarity) to a seed molecule.
+
+
+Moreover, GT4SD also includes properties on other entities such as [proteins](https://gt4sd.github.io/gt4sd-core/api/gt4sd.properties.proteins.html) and [crystals](https://gt4sd.github.io/gt4sd-core/api/gt4sd.properties.crystals.html).

model_cards/description.md

@@ -0,0 +1,7 @@
+
+
+<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
+
+### Molecular property prediction
+
+This is the GT4SD web-app for prediction of various molecular properties. For **examples** and **documentation** of the supported properties, please see below. Please note that this API does not expose **all** properties that are supported in GT4SD (a list of the non-supported ones can be found at the bottom).

model_cards/examples.smi

@@ -0,0 +1,13 @@
+Cc1cc2c(c3oc(CCCC#N)cc13)C(=O)c1c(O)cccc1C2=O
+C=CCN1C(=O)C(=NNC(=S)NC2OC(COC(C)=O)C(OC(C)=O)C(OC(C)=O)C2OC(C)=O)c2ccccc21
+O=C1C(=Cc2ccc(F)cc2)CCOc2c1ccc1ccccc21
+CC(C)CNc1cc(NCC(C)C)nc(NCC(C)C)n1
+CN1CCN(CCCOc2ccc(N3C(=O)C(=Cc4ccc(Oc5ccc([N+](=O)[O-])cc5)cc4)SC3=S)cc2)CC1
+COc1ccc2ccccc2c1C1CC1NC(C)=O
+Cc1ccc(-n2c(=O)[nH]cc(C(=O)Nc3ccc4c(c3)OCCO4)c2=O)cc1
+Cc1ccc(NCc2nnc(SCC(=O)NCCc3ccccc3)n2C)cc1
+CCCNC(=O)c1ccc2c(c1)N=C(C)c1c(C)ccc(C)c1S2
+COc1ccc(Cn2ccn(CC(=O)Nc3cc(C)ccc3C)c(=O)c2=O)cc1
+Cn1nccc1C(=O)NN=Cc1c(O)ccc2ccccc12
+CCOC(=O)Nc1cc(N)c2c(n1)NC(C)C(c1ccccc1)=N2
+Cn1nc(N)c2ncc(C(Cl)(Cl)Cl)nc21

model_cards/regression_transformer_article.md

@@ -1,113 +0,0 @@
-# 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

@@ -1,13 +0,0 @@
-
-
-<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

@@ -1,9 +0,0 @@
-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

@@ -8,7 +8,7 @@ torch-sparse
 torch-geometric
 torchvision==0.13.1
 torchaudio==0.12.1
-gt4sd>=1.0.6
+gt4sd>=1.1.1
 molgx>=0.22.0a1
 molecule_generation
 nglview

utils.py

@@ -1,136 +1,25 @@
-import json
 import logging
-import os
-from collections import defaultdict
-from typing import Dict, List, Tuple
-
+from typing import List
+import numpy as np
 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)
+def draw_grid_predict(
+    sequences: List[str], properties: np.array, property_names: List[str], domain: str
 ) -> str:
     """
-    Uses mols2grid to draw a HTML grid for the generated molecules
+    Uses mols2grid to draw a HTML grid for the prediction
 
     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).
+        sequences: Sequences for which properties are predicted.
+        properties: Predicted properties. Array of shape (n_samples, n_properties).
+        names: List of property names
+        domain: Domain of the prediction (molecules or proteins).
 
     Returns:
         HTML to display
@@ -140,29 +29,25 @@ def draw_grid_generate(
         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}")
+        converter = lambda x: Chem.MolToSmiles(Chem.MolFromFASTA(x))
     else:
-        smis = [s[0] for s in samples]
+        converter = lambda x: x
 
-    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}")
+    smiles = []
+    for sequence in sequences:
+        try:
+            seq = converter(sequence)
+            smiles.append(seq)
+        except Exception:
+            logger.warning(f"Could not draw sequence {seq}")
 
-    result_df = pd.DataFrame(result)
+    result = pd.DataFrame({"SMILES": smiles})
+    for i, name in enumerate(property_names):
+        result[name] = properties[:, i]
+    n_cols = min(3, len(result))
+    size = (140, 200) if len(result) > 3 else (600, 700)
     obj = mols2grid.display(
-        result_df,
+        result,
         tooltip=list(result.keys()),
         height=1100,
         n_cols=n_cols,