Output Reformat

app.py

@@ -7,8 +7,13 @@ import gradio as gr
 from illuminate import Illuminate
 import matplotlib.pyplot as plt
 
+from rdkit import Chem
+from rdkit.Chem import Draw
 
-
+def MolsMatrixToGridImage(mols, legends, filename):
+    img = Draw.MolsToGridImage(mols, molsPerRow=5, subImgSize=(400,400), legends=legends)
+    img.save(filename)
+    return img
 
 
 def launch_illumination(target, representation, surrogate, acquisition, ranges, generations_max, function_calls_max, structural_filters):
@@ -62,20 +67,27 @@ def launch_illumination(target, representation, surrogate, acquisition, ranges,
     stats_file = pd.read_csv("statistics.csv")
     molecules_file = pd.read_csv("molecules.csv")
 
-    files_in_directory = os.listdir('.')
+    # files_in_directory = os.listdir('.')
+
+    # pattern = re.compile(r'archive_(\d+)\.csv')
+    # archive_files = [f for f in files_in_directory if pattern.match(f)]
+    # archive_numbers = [int(pattern.search(f).group(1)) for f in archive_files]
+    # archive_file = pd.read_csv(f'archive_{max(archive_numbers)}.csv')
 
-    pattern = re.compile(r'archive_(\d+)\.csv')
-    archive_files = [f for f in files_in_directory if pattern.match(f)]
-    archive_numbers = [int(pattern.search(f).group(1)) for f in archive_files]
-    archive_file = pd.read_csv(f'archive_{max(archive_numbers)}.csv')
+    # csv_files = [file for file in files_in_directory if file.endswith('.csv')]
 
-    csv_files = [file for file in files_in_directory if file.endswith('.csv')]
+    # for csv_file in csv_files:
+    #     if os.path.isfile(csv_file):
+    #         os.remove(csv_file)
 
-    for csv_file in csv_files:
-        if os.path.isfile(csv_file):
-            os.remove(csv_file)
+    top_molecules = molecules_file.nlargest(10, 'fitness')
+    top_smiles = top_molecules['smiles'].tolist()
+    top_fitness = top_molecules['fitness'].tolist()
+    top_mols = [Chem.MolFromSmiles(smile) for smile in top_smiles]
+    top_legends = [f'Similarity: {score:.5f}' for score in top_fitness]
+    image = MolsMatrixToGridImage(mols=top_mols, legends=top_legends, filename='top_molecules_grid.png')
 
-    return stats_file, molecules_file #, archive_file
+    return image, stats_file, molecules_file
 
 def validate_and_process(target, representation, surrogate, acquisition, exact_mol_wt_min, exact_mol_wt_max, mol_log_p_min, mol_log_p_max, tpsa_min, tpsa_max, mol_mr_min, mol_mr_max, generations_max, function_calls_max, structural_filters):
     # Ensure min is less than max for each range
@@ -91,9 +103,8 @@ def validate_and_process(target, representation, surrogate, acquisition, exact_m
         mol_mr_range
     ]
 
-    stats_file, molecules_file = launch_illumination(target, representation, surrogate, acquisition, ranges, generations_max, function_calls_max, structural_filters)
-    return stats_file, molecules_file
-
+    image, stats_file, molecules_file = launch_illumination(target, representation, surrogate, acquisition, ranges, generations_max, function_calls_max, structural_filters)
+    return image
 
 def gradio_interface():
     with gr.Blocks() as demo:
@@ -128,16 +139,11 @@ def gradio_interface():
                 mol_mr_min = gr.Slider(minimum=0, maximum=250, value=40, step=1, label="Minimum Molecular Refractivity")
                 mol_mr_max = gr.Slider(minimum=0, maximum=250, value=130, step=1, label="Maximum Molecular Refractivity")
 
-        def plot_csv(file_path):
-            df = pd.read_csv(file_path)
-            fig, ax = plt.subplots()
-            df.plot(ax=ax)
-            return fig
-
         submit_btn = gr.Button("Submit")
 
-        output_df_1 = gr.Dataframe(label="Optimisation History")
-        output_df_2 = gr.Dataframe(label="Output Molecules")
+        output_image = gr.Image(label="Top Molecules")
+        gr.DownloadButton(label=f"Download Optimisation History", value="./statistics.csv", visible=True)
+        gr.DownloadButton(label=f"Download Output Molecules", value="./molecules.csv", visible=True)
 
         submit_btn.click(
             validate_and_process,
@@ -158,7 +164,7 @@ def gradio_interface():
                 function_calls_max,
                 structural_filters,
             ],
-            outputs=[output_df_1, output_df_2]
+            outputs=[output_image]
             )
         demo.launch()
 

illumination/app.py

@@ -1,163 +0,0 @@
-import os
-import re
-import logging
-import pandas as pd
-from omegaconf import OmegaConf
-import gradio as gr
-from illuminate import Illuminate
-import matplotlib.pyplot as plt
-
-def launch_illumination(target, representation, surrogate, acquisition, ranges, generations_max, function_calls_max, structural_filters):
-    config = {
-        'controller': {
-            'max_generations': generations_max,
-            'max_fitness_calls': function_calls_max
-        },
-        'archive': {
-            'name': 'Troglitazone',
-            'size': 150,
-            'accuracy': 25000
-        },
-        'descriptor': {
-            'properties': [
-                'Descriptors.ExactMolWt',
-                'Descriptors.MolLogP',
-                'Descriptors.TPSA',
-                'Crippen.MolMR'
-            ],
-            'ranges': ranges
-        },
-        'fitness': {
-            'type': 'Fingerprint',
-            'target': target,
-            'representation': representation
-        },
-        'arbiter': {
-            'rules': [rule_set for rule_set in structural_filters]
-        },
-        'generator': {
-            'batch_size': 40,
-            'initial_size': 40,
-            'mutation_data': 'data/smarts/mutation_collection.tsv',
-            'initial_data': 'data/smiles/guacamol_intitial_rediscovery_troglitazone.smi'
-        },
-        'surrogate': {
-            'type': "Fingerprint",
-            'representation': surrogate,
-        },
-        'acquisition': {
-            'type': acquisition,
-            'beta': 0.3
-        }
-    }
-    log = logging.getLogger(__name__)
-    log.info(OmegaConf.to_yaml(config))
-    current_instance = Illuminate(OmegaConf.create(config))
-    current_instance()
-
-    stats_file = pd.read_csv("statistics.csv")
-    molecules_file = pd.read_csv("molecules.csv")
-
-    files_in_directory = os.listdir('.')
-
-    pattern = re.compile(r'archive_(\d+)\.csv')
-    archive_files = [f for f in files_in_directory if pattern.match(f)]
-    archive_numbers = [int(pattern.search(f).group(1)) for f in archive_files]
-    archive_file = pd.read_csv(f'archive_{max(archive_numbers)}.csv')
-
-    csv_files = [file for file in files_in_directory if file.endswith('.csv')]
-
-    for csv_file in csv_files:
-        if os.path.isfile(csv_file):
-            os.remove(csv_file)
-
-    return stats_file, molecules_file #, archive_file
-
-def validate_and_process(target, representation, surrogate, acquisition, exact_mol_wt_min, exact_mol_wt_max, mol_log_p_min, mol_log_p_max, tpsa_min, tpsa_max, mol_mr_min, mol_mr_max, generations_max, function_calls_max, structural_filters):
-    # Ensure min is less than max for each range
-    exact_mol_wt_range = sorted([exact_mol_wt_min, exact_mol_wt_max])
-    mol_log_p_range = sorted([mol_log_p_min, mol_log_p_max])
-    tpsa_range = sorted([tpsa_min, tpsa_max])
-    mol_mr_range = sorted([mol_mr_min, mol_mr_max])
-
-    ranges = [
-        exact_mol_wt_range,
-        mol_log_p_range,
-        tpsa_range,
-        mol_mr_range
-    ]
-
-    stats_file, molecules_file = launch_illumination(target, representation, surrogate, acquisition, ranges, generations_max, function_calls_max, structural_filters)
-    return stats_file, molecules_file
-
-def gradio_interface():
-    with gr.Blocks() as demo:
-
-        representation_options = ["ECFP4", "ECFP6", "FCFP4", "FCFP6"]
-        surrogate_options = ["ECFP4", "ECFP6", "FCFP4", "FCFP6", "RDFP", "APFP", "TTFP"]
-        acquisition_options = ["Mean", "UCB", "EI", "logEI"]
-
-        target = gr.Textbox(label="Target (SMILES)", value="O=C1NC(=O)SC1Cc4ccc(OCC3(Oc2c(c(c(O)c(c2CC3)C)C)C)C)cc4")
-
-        with gr.Row():
-            generations_max = gr.Slider(minimum=0, maximum=150, value=1, step=1, label="Generations")
-            function_calls_max = gr.Slider(minimum=0, maximum=15000, value=5000, step=100, label="Function Calls")
-
-        structural_filters = gr.CheckboxGroup(["BMS", "Dundee", "Glaxo", "Inpharmatica", "LINT", "MLSMR", "PAINS", "SureChEMBL"], label="Structural Filters")
-
-        with gr.Row():
-            representation = gr.Dropdown(choices=representation_options, value="ECFP4", label="Fitness Representation")
-            surrogate = gr.Dropdown(choices=surrogate_options, value="ECFP4", label="Surrogate Representation")
-            acquisition = gr.Dropdown(choices=acquisition_options, value="Mean", label="Acquisition Function")
-
-        with gr.Accordion("Physicochemical Descriptors", open=False):
-            with gr.Row():
-                exact_mol_wt_min = gr.Slider(minimum=0, maximum=885, value=225, step=1, label="Minimum Molecular Weight")
-                exact_mol_wt_max = gr.Slider(minimum=0, maximum=885, value=555, step=1, label="Maximum Molecular Weight")
-            with gr.Row():
-                mol_log_p_min = gr.Slider(minimum=-4, maximum=8, value=-0.5, step=0.1, label="Minimum Log(P)")
-                mol_log_p_max = gr.Slider(minimum=-4, maximum=8, value=5.5, step=0.1, label="Maximum Log(P)")
-            with gr.Row():
-                tpsa_min = gr.Slider(minimum=0, maximum=250, value=0, step=1, label="Minimum TPSA")
-                tpsa_max = gr.Slider(minimum=0, maximum=250, value=140, step=1, label="Maximum TPSA")
-            with gr.Row():
-                mol_mr_min = gr.Slider(minimum=0, maximum=250, value=40, step=1, label="Minimum Molecular Refractivity")
-                mol_mr_max = gr.Slider(minimum=0, maximum=250, value=130, step=1, label="Maximum Molecular Refractivity")
-
-        def plot_csv(file_path):
-            df = pd.read_csv(file_path)
-            fig, ax = plt.subplots()
-            df.plot(ax=ax)
-            return fig
-
-        submit_btn = gr.Button("Submit")
-
-        output_df_1 = gr.Dataframe(label="Optimisation History")
-        output_df_2 = gr.Dataframe(label="Output Molecules")
-
-        submit_btn.click(
-            validate_and_process,
-            inputs=[
-                target,
-                representation,
-                surrogate,
-                acquisition,
-                exact_mol_wt_min,
-                exact_mol_wt_max,
-                mol_log_p_min,
-                mol_log_p_max,
-                tpsa_min,
-                tpsa_max,
-                mol_mr_min,
-                mol_mr_max,
-                generations_max,
-                function_calls_max,
-                structural_filters,
-            ],
-            outputs=[output_df_1, output_df_2] #, plot2, plot3]
-        )
-
-        demo.launch()
-
-if __name__ == "__main__":
-    gradio_interface()