import uuid import gradio as gr import torch import os import pandas as pd from rdkit import Chem from scripts.pla_net_inference import main from utils.args import ArgsInit os.system("nvidia-smi") print("TORCH_CUDA", torch.cuda.is_available()) PROJECT_URL = "https://www.nature.com/articles/s41598-022-12180-x" DEFAULT_PATH_DOCKER = "/home/user/app" ENABLED_MODELS = [ 'aa2ar', 'abl1', 'ace', 'aces', 'ada', 'ada17', 'adrb1', 'adrb2', 'akt1', 'akt2', 'aldr', 'ampc', 'andr', 'aofb', 'bace1', 'braf', 'cah2', 'casp3', 'cdk2', 'comt', 'cp2c9', 'cp3a4', 'csf1r', 'cxcr4', 'def', 'dhi1', 'dpp4', 'drd3', 'dyr', 'egfr', 'esr1', 'esr2', 'fa10', 'fa7', 'fabp4', 'fak1', 'fgfr1', 'fkb1a', 'fnta', 'fpps', 'gcr', 'glcm', 'gria2', 'grik1', 'hdac2', 'hdac8', 'hivint', 'hivpr', 'hivrt', 'hmdh', 'hs90a', 'hxk4', 'igf1r', 'inha', 'ital', 'jak2', 'kif11', 'kit', 'kith', 'kpcb', 'lck', 'lkha4', 'mapk2', 'mcr', 'met', 'mk01', 'mk10', 'mk14', 'mmp13', 'mp2k1', 'nos1', 'nram', 'pa2ga', 'parp1', 'pde5a', 'pgh1', 'pgh2', 'plk1', 'pnph', 'ppara', 'ppard', 'pparg', 'prgr', 'ptn1', 'pur2', 'pygm', 'pyrd', 'reni', 'rock1', 'rxra', 'sahh', 'src', 'tgfr1', 'thb', 'thrb', 'try1', 'tryb1', 'tysy', 'urok', 'vgfr2', 'wee1', 'xiap' ] def load_and_filter_data(protein_id, ligand_smiles): # generate random short id, make short random_id = str(uuid.uuid4())[:8] print("Inference ID: ", random_id) # check that ligand_smiles is not empty if not ligand_smiles or ligand_smiles.strip() == "": error_msg = f"!SMILES string is required 💥" raise gr.Error(error_msg, duration=5) if protein_id not in ENABLED_MODELS: error_msg = f"!Invalid 💥 target protein ID, the available options are: {ENABLED_MODELS}. To do inference other proteins, you can run the model locally an train the model for each target protein." raise gr.Error(error_msg, duration=5) # Split the input SMILES string by ':' to get a list smiles_list = ligand_smiles.split(':') print("Smiles to predict: ", smiles_list) print("Target Protein ID: ", protein_id) # Validate SMILES invalid_smiles = [] for smiles in smiles_list: mol = Chem.MolFromSmiles(smiles.strip()) if mol is None: invalid_smiles.append(smiles.strip()) if invalid_smiles: error_msg = f"!Invalid 💥 SMILES string(s) : {', '.join(invalid_smiles)}" raise gr.Error(error_msg, duration=5) # Create tmp folder os.makedirs(f"{DEFAULT_PATH_DOCKER}/example/tmp", exist_ok=True) # Save SMILES to CSV df = pd.DataFrame({"smiles": [s.strip() for s in smiles_list if s.strip()]}) df.to_csv(f"{DEFAULT_PATH_DOCKER}/example/tmp/{random_id}_input_smiles.csv", index=False) # Run inference args = ArgsInit().args args.nclasses = 2 args.batch_size = 10 args.use_prot = True args.freeze_molecule = True args.conv_encode_edge = True args.learn_t = True args.binary = True args.use_gpu = True args.target = protein_id args.target_list = f"{DEFAULT_PATH_DOCKER}/data/datasets/AD/Targets_Fasta.csv" args.target_checkpoint_path = f"{DEFAULT_PATH_DOCKER}/checkpoints/PLA-Net/BINARY_{protein_id}" args.input_file_smiles = f"{DEFAULT_PATH_DOCKER}/example/tmp/{random_id}_input_smiles.csv" args.output_file = f"{DEFAULT_PATH_DOCKER}/example/tmp/{random_id}_output_predictions.csv" print("Args: ", args) main(args) # Load the CSV file df = pd.read_csv(f'{DEFAULT_PATH_DOCKER}/example/tmp/{random_id}_output_predictions.csv') print("Prediction Results output: ", df) return df def load_description(fp): with open(fp, 'r', encoding='utf-8') as f: content = f.read() return content def run_inference(protein_id, ligand_smile): result_df = load_and_filter_data(protein_id, ligand_smile) return result_df def create_interface(): with gr.Blocks(title="PLA-Net Web Inference") as inference: gr.HTML(load_description("gradio/title.md")) gr.Markdown("### Input") with gr.Row(): with gr.Column(): gr.Markdown("#### Target Protein") protein_id = gr.Dropdown( choices=ENABLED_MODELS, label="Target Protein ID", info="Select the target protein from the dropdown menu.", value="ada" ) gr.Markdown(" Check the available target proteins [here](https://github.com/juliocesar-io/PLA-Net/blob/main/data/targets.md). The corresponding protein sequences are available in [here](https://github.com/juliocesar-io/PLA-Net/blob/main/data/datasets/AD/Targets_Fasta.csv).") with gr.Column(): gr.Markdown("#### Ligand") ligand_smile = gr.Textbox( info="Provide SMILES input (separate multiple SMILES with ':' )", placeholder="SMILES input", label="SMILES string(s)", ) gr.Examples( examples=[ "Cn4c(CCC(=O)Nc3ccc2ccn(CC[C@H](CO)n1cnc(C(N)=O)c1)c2c3)nc5ccccc45", "OCCCCCn1cnc2C(O)CN=CNc12", "Nc4nc(c1ccco1)c3ncn(C(=O)NCCc2ccccc2)c3n4" ], inputs=ligand_smile, label="Example SMILES" ) btn = gr.Button("Run") gr.Markdown("### Output") out = gr.Dataframe( headers=["target", "smiles", "interaction_probability", "interaction_class"], datatype=["str", "str", "number", "number"], label="Prediction Results" ) btn.click(fn=run_inference, inputs=[protein_id, ligand_smile], outputs=out) gr.Markdown(""" PLA-Net model for predicting interactions between small organic molecules and one of the 102 target proteins in the AD dataset. Graph representations of the molecule and a given target protein are generated from SMILES and FASTA sequences and are used as input to the Ligand Module (LM) and Protein Module (PM), respectively. Each module comprises a deep GCN followed by an average pooling layer, which extracts relevant features of their corresponding input graph. Both representations are finally concatenated and combined through a fully connected layer to predict the target– ligand interaction probability. """) gr.Markdown(""" Ruiz Puentes, P., Rueda-Gensini, L., Valderrama, N. et al. Predicting target–ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery. Sci Rep 12, 8434 (2022). [https://doi.org/10.1038/s41598-022-12180-x](https://doi.org/10.1038/s41598-022-12180-x) """) return inference if __name__ == "__main__": interface = create_interface() interface.launch()