update

.gitignore

@@ -0,0 +1,2 @@
+__pycache__/
+*.DS_Store

README.md

@@ -1,5 +1,5 @@
 ---
-title: Crystal properties (CGCNN)
+title: Crystal properties
 emoji: 💡
 colorFrom: green
 colorTo: blue

app.py

@@ -1,169 +1,107 @@
 import logging
+import os
 import pathlib
+import shutil
+import tempfile
+from pathlib import Path
 
 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,
-)
+from gt4sd.properties.crystals import CRYSTALS_PROPERTY_PREDICTOR_FACTORY
 
 logger = logging.getLogger(__name__)
 logger.addHandler(logging.NullHandler())
 
+suffix_dict = {"metal_nonmetal_classifier": ".csv"}
 
-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 = {}
+
+def create_temp_file(path: str) -> str:
+    temp_dir = tempfile.gettempdir()
+    temp_folder = os.path.join(temp_dir, "gt4sd_crystal")
+    os.makedirs(temp_folder, exist_ok=True)
+    # Clean up directory
+    for i in os.listdir(temp_folder):
+        print("Removing", i)
+        os.remove(os.path.join(temp_folder, i))
+
+    temp_path = os.path.join(temp_folder, path.split("/")[-1])
+    shutil.copy2(path, temp_path)
+    return temp_path
+
+
+def main(property: str, data_file: str):
+
+    print(data_file, data_file.orig_name, data_file.name)
+
+    if data_file is None:
+        raise TypeError("You have to pass either an input file for the crystal model")
+
+    # Copy file into a UNIQUE temporary directory
+    file_path = Path(create_temp_file(data_file.name))
+    folder = file_path.parent
+    print(file_path)
+    print(folder)
+    if file_path.suffix == ".cif":
+        input_path = folder
+    elif file_path.suffix == ".csv":
+        input_path = file_path
+    elif file_path.suffix == ".zip":
+        # Unzip zip
+        shutil.unpack_archive(file_path, file_path.parent)
+        if len(list(filter(lambda x: x.endswith(".cif"), os.listdir(folder)))) == 0:
+            raise ValueError("No `.cif` files were found inside the `.zip`.")
+        input_path = folder
     else:
-        substructures_to_mask = (
-            []
-            if substructures_to_mask == ""
-            else substructures_to_mask.replace(" ", "").split(",")
+        raise TypeError(
+            "You have to pass a `.csv` (for `metal_nonmetal_classifier`),"
+            " a `.cif` (for all other properties) or a `.zip` with multiple"
+            f" `.cif` files. Not {type(data_file)}."
         )
-        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])
+
+    prop_name = property.replace(" ", "_").lower()
+    algo, config = CRYSTALS_PROPERTY_PREDICTOR_FACTORY[prop_name]
+    # Pass hyperparameters if applicable
+    kwargs = {"algorithm_version": "v0"}
+    model = algo(config(**kwargs))
+
+    result = model(input=input_path)
+    return pd.DataFrame(result)
 
 
 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(CRYSTALS_PROPERTY_PREDICTOR_FACTORY.keys())[::-1]
+    properties = list(map(lambda x: x.replace("_", " ").title(), 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 = [
+        ["Formation Energy", metadata_root.joinpath("7206075.cif")],
+        ["Bulk moduli", metadata_root.joinpath("crystals.zip")],
+        ["Metal Nonmetal Classifier", metadata_root.joinpath("metal.csv")],
+        ["Bulk moduli", metadata_root.joinpath("9000046.cif")],
+    ]
 
-    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="Crystal properties",
         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
+            gr.Dropdown(properties, label="Property", value="Instability"),
+            gr.File(
+                file_types=[".cif", ".csv", ".zip"],
+                label="Input file for crystal model",
             ),
         ],
-        outputs=gr.HTML(label="Output"),
+        outputs=gr.DataFrame(label="Output"),
         article=article,
         description=description,
-        examples=examples.values.tolist(),
+        examples=examples,
     )
     demo.launch(debug=True, show_error=True)

model_cards/7206075.cif

@@ -0,0 +1,119 @@
+#------------------------------------------------------------------------------
+#$Date: 2016-03-26 17:23:40 +0200 (Sat, 26 Mar 2016) $
+#$Revision: 180391 $
+#$URL: svn://www.crystallography.net/cod/cif/7/20/60/7206075.cif $
+#------------------------------------------------------------------------------
+#
+# This file is available in the Crystallography Open Database (COD),
+# http://www.crystallography.net/
+#
+# All data on this site have been placed in the public domain by the
+# contributors.
+#
+data_7206075
+loop_
+_publ_author_name
+'Rezaee, Masih'
+'Mousavi Khoie, Seyyed Mohammad'
+'Liu, Kun Hua'
+_publ_section_title
+;
+ The role of brookite in mechanical activation of anatase-to-rutile
+ transformation of nanocrystalline TiO2: An XRD and Raman spectroscopy
+ investigation
+;
+_journal_issue                   16
+_journal_name_full               CrystEngComm
+_journal_page_first              5055
+_journal_paper_doi               10.1039/c1ce05185g
+_journal_volume                  13
+_journal_year                    2011
+_chemical_formula_structural     'Ti O2'
+_chemical_formula_sum            'O2 Ti'
+_chemical_name_mineral           Anatase
+_chemical_name_systematic        'Titanium oxide'
+_space_group_IT_number           141
+_symmetry_cell_setting           tetragonal
+_symmetry_Int_Tables_number      141
+_symmetry_space_group_name_Hall  'I 4bw 2bw -1bw'
+_symmetry_space_group_name_H-M   'I 41/a m d :1'
+_audit_update_record
+;
+2011-02-06 # Formatted by publCIF
+;
+_cell_angle_alpha                90
+_cell_angle_beta                 90
+_cell_angle_gamma                90
+_cell_formula_units_Z            4
+_cell_length_a                   3.7850
+_cell_length_b                   3.7850
+_cell_length_c                   9.5196
+_cell_measurement_temperature    298
+_cell_volume                     136.380
+_computing_cell_refinement       MAUD
+_computing_data_collection       X'Pert
+_computing_data_reduction        MAUD
+_computing_publication_material  publCIF
+_computing_structure_refinement  MAUD
+_computing_structure_solution    MAUD
+_diffrn_measurement_device_type  'GBC MMA X-ray diffractometer'
+_diffrn_radiation_source         'Cu K\a'
+_diffrn_radiation_type           'Cu K\a'
+_diffrn_radiation_wavelength     1.541874
+_diffrn_reflns_theta_max         50
+_cod_data_source_file            400a_anatase_.txt
+_cod_data_source_block           11A
+_cod_original_sg_symbol_H-M      'I 41/a m d S'
+_cod_database_code               7206075
+loop_
+_symmetry_equiv_pos_as_xyz
+x,y,z
+-x,-y,z
+x,1/2+y,1/4-z
+-x,1/2-y,1/4-z
+-x,y,z
+x,-y,z
+-x,1/2+y,1/4-z
+x,1/2-y,1/4-z
+y,x,-z
+-y,-x,-z
+y,1/2+x,1/4+z
+-y,1/2-x,1/4+z
+-y,x,-z
+y,-x,-z
+-y,1/2+x,1/4+z
+y,1/2-x,1/4+z
+1/2+x,1/2+y,1/2+z
+1/2-x,1/2-y,1/2+z
+1/2+x,y,3/4-z
+1/2-x,-y,3/4-z
+1/2-x,1/2+y,1/2+z
+1/2+x,1/2-y,1/2+z
+1/2-x,y,3/4-z
+1/2+x,-y,3/4-z
+1/2+y,1/2+x,1/2-z
+1/2-y,1/2-x,1/2-z
+1/2+y,x,3/4+z
+1/2-y,-x,3/4+z
+1/2-y,1/2+x,1/2-z
+1/2+y,1/2-x,1/2-z
+1/2-y,x,3/4+z
+1/2+y,-x,3/4+z
+loop_
+_atom_site_label
+_atom_site_type_symbol
+_atom_site_symmetry_multiplicity
+_atom_site_Wyckoff_symbol
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+_atom_site_occupancy
+_atom_site_attached_hydrogens
+_atom_site_calc_flag
+Ti1 Ti4+ 4 a 0. 0. 0. 1. 0 d
+O1 O2- 8 e 0. 0. 0.21017 1. 0 d
+loop_
+_atom_type_symbol
+_atom_type_oxidation_number
+Ti4+ 4.000
+O2- -2.000

model_cards/9000046.cif

@@ -0,0 +1,84 @@
+#------------------------------------------------------------------------------
+#$Date: 2013-05-05 17:21:46 +0300 (Sun, 05 May 2013) $
+#$Revision: 85285 $
+#$URL: svn://www.crystallography.net/cod/cif/9/00/00/9000046.cif $
+#------------------------------------------------------------------------------
+#
+# This file is available in the Crystallography Open Database (COD),
+# http://www.crystallography.net/. The original data for this entry
+# were provided the American Mineralogist Crystal Structure Database,
+# http://rruff.geo.arizona.edu/AMS/amcsd.php
+#
+# The file may be used within the scientific community so long as
+# proper attribution is given to the journal article from which the
+# data were obtained.
+#
+data_9000046
+loop_
+_publ_author_name
+'Kukesh, J. S.'
+'Pauling, L.'
+_publ_section_title
+;
+ The problem of the graphite structure
+;
+_journal_name_full               'American Mineralogist'
+_journal_page_first              125
+_journal_page_last               125
+_journal_volume                  35
+_journal_year                    1950
+_chemical_formula_sum            C
+_chemical_name_common            Graphite
+_chemical_name_mineral           Graphite
+_space_group_IT_number           69
+_symmetry_space_group_name_Hall  '-F 2 2'
+_symmetry_space_group_name_H-M   'F m m m'
+_cell_angle_alpha                90
+_cell_angle_beta                 90
+_cell_angle_gamma                90
+_cell_length_a                   2.456
+_cell_length_b                   4.254
+_cell_length_c                   6.696
+_cell_volume                     69.959
+_exptl_crystal_density_diffrn    2.281
+_cod_database_code               9000046
+loop_
+_symmetry_equiv_pos_as_xyz
+x,y,z
+x,1/2+y,1/2+z
+1/2+x,y,1/2+z
+1/2+x,1/2+y,z
+x,-y,z
+x,1/2-y,1/2+z
+1/2+x,-y,1/2+z
+1/2+x,1/2-y,z
+-x,y,-z
+-x,1/2+y,1/2-z
+1/2-x,y,1/2-z
+1/2-x,1/2+y,-z
+-x,y,z
+-x,1/2+y,1/2+z
+1/2-x,y,1/2+z
+1/2-x,1/2+y,z
+x,-y,-z
+x,1/2-y,1/2-z
+1/2+x,-y,1/2-z
+1/2+x,1/2-y,-z
+x,y,-z
+x,1/2+y,1/2-z
+1/2+x,y,1/2-z
+1/2+x,1/2+y,-z
+-x,-y,z
+-x,1/2-y,1/2+z
+1/2-x,-y,1/2+z
+1/2-x,1/2-y,z
+-x,-y,-z
+-x,1/2-y,1/2-z
+1/2-x,-y,1/2-z
+1/2-x,1/2-y,-z
+loop_
+_atom_site_label
+_atom_site_fract_x
+_atom_site_fract_y
+_atom_site_fract_z
+C 0.00000 0.16667 0.00000

model_cards/description.md

@@ -0,0 +1,8 @@
+
+
+<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
+
+### Crystal property prediction
+
+This is the GT4SD web-app for prediction of various crystal properties. For **examples** and **documentation** of the supported properties, please see below. 
+Enjoy :) 

model_cards/metal.csv

@@ -0,0 +1,50 @@
+KPSO2,orthorhombic
+Zr2Ga(PO4)3,trigonal
+Te4Mo(WSe)2,trigonal
+Mo3W(SeS3)2,trigonal
+Te2Mo2SeS,trigonal
+Mo3W(Se3S)2,trigonal
+MoWSe3S,trigonal
+Mo3W(SeS)4,trigonal
+Te2Mo3WSe6,trigonal
+TeW2SeS2,trigonal
+Te4MoW3S4,trigonal
+Te6Mo3WS2,trigonal
+KMg6CO8,tetragonal
+Mg14BiBO16,orthorhombic
+KMg14WO16,tetragonal
+Mg14AlCdO16,orthorhombic
+Mg30VCrO32,tetragonal
+Mg30CoSiO32,tetragonal
+YMg30CO32,tetragonal
+KYMg30O32,tetragonal
+CaMg30MnO32,tetragonal
+CaMg30CO32,tetragonal
+LiMg30MnO32,tetragonal
+CaMg30NiO32,tetragonal
+LiMg30AlO32,tetragonal
+Mg30AlFeO32,tetragonal
+RbMg30SbO32,tetragonal
+KNaMg30O3orthorhombic
+La7Sm(Fe2O5)4,triclinic
+SrCa3Mn4O1triclinic
+NbNi3(HC)2,tetragonal
+La2P2AuO,monoclinic
+Li9Mn2Co5O16,monoclinic
+Li9Mn2Co5O16,monoclinic
+Li9Mn2Co5O16,monoclinic
+Li9Mn2Co5O16,monoclinic
+Li9Mn2Co5O16,monoclinic
+LiCrP4O13,triclinic
+LiCr4P7O24,triclinic
+ZnGe(OF)6,trigonal
+Cs2Mo(SO)2,monoclinic
+NaMgSO7,monoclinic
+K2NaNdCl6,cubic
+K2NaBiCl6,cubic
+Na2EuCuCl6,cubic
+NaLi2CoF6,cubic
+K2NaTiF6,cubic
+K2AgRhF6,cubic
+K2CeAgCl6,cubic
+K2ErCuCl6,cubic

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>=1.1.4
 molgx>=0.22.0a1
 molecule_generation
 nglview

utils.py

@@ -1,16 +1,7 @@
-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
 
@@ -18,63 +9,6 @@ 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
@@ -118,55 +52,3 @@ def draw_grid_predict(prediction: str, target: str, domain: str) -> str:
         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