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| import gradio as gr | |
| import os | |
| import torch | |
| import subprocess | |
| from rdkit import Chem | |
| from src import const | |
| from src.visualizer import save_xyz_file | |
| from src.datasets import get_dataloader, collate_with_fragment_edges, parse_molecule | |
| from src.lightning import DDPM | |
| from src.linker_size_lightning import SizeClassifier | |
| HTML_TEMPLATE = """<!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta http-equiv="content-type" content="text/html; charset=UTF-8" /> | |
| <style> | |
| .mol-container {{ | |
| width: 600px; | |
| height: 600px; | |
| position: relative; | |
| }} | |
| .mol-container select{{ | |
| background-image:None; | |
| }} | |
| </style> | |
| <script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script> | |
| </head> | |
| <body> | |
| <div id="container" class="mol-container"></div> | |
| <script> | |
| $(document).ready(function() {{ | |
| let element = $("#container"); | |
| let config = {{ backgroundColor: "white" }}; | |
| let viewer = $3Dmol.createViewer( element, config ); | |
| viewer.addModel(`{molecule}`, "{fmt}") | |
| viewer.getModel().setStyle({{ stick: {{ colorscheme:"greenCarbon" }} }}) | |
| viewer.zoomTo(); | |
| viewer.render(); | |
| }}); | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| IFRAME_TEMPLATE = """<iframe style="width: 100%; height: 700px" name="result" allow="midi; geolocation; microphone; camera; | |
| display-capture; encrypted-media;" sandbox="allow-modals allow-forms allow-scripts allow-same-origin allow-popups | |
| allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" | |
| allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>""" | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| os.makedirs("results", exist_ok=True) | |
| os.makedirs("models", exist_ok=True) | |
| subprocess.run( | |
| 'wget https://zenodo.org/record/7121300/files/geom_size_gnn.ckpt?download=1 -O models/geom_size_gnn.ckpt', | |
| shell=True | |
| ) | |
| size_nn = SizeClassifier.load_from_checkpoint('models/geom_size_gnn.ckpt', map_location=device).eval().to(device) | |
| print('Loaded SizeGNN model') | |
| subprocess.run( | |
| 'wget https://zenodo.org/record/7121300/files/geom_difflinker.ckpt?download=1 -O models/geom_difflinker.ckpt', | |
| shell=True | |
| ) | |
| ddpm = DDPM.load_from_checkpoint('models/geom_difflinker.ckpt', map_location=device).eval().to(device) | |
| print('Loaded diffusion model') | |
| def sample_fn(_data): | |
| output, _ = size_nn.forward(_data) | |
| probabilities = torch.softmax(output, dim=1) | |
| distribution = torch.distributions.Categorical(probs=probabilities) | |
| samples = distribution.sample() | |
| sizes = [] | |
| for label in samples.detach().cpu().numpy(): | |
| sizes.append(size_nn.linker_id2size[label]) | |
| sizes = torch.tensor(sizes, device=samples.device, dtype=torch.long) | |
| return sizes | |
| def read_molecule_content(path): | |
| with open(path, "r") as f: | |
| return "".join(f.readlines()) | |
| def read_molecule(path): | |
| if path.endswith('.pdb'): | |
| return Chem.MolFromPDBFile(path, sanitize=False, removeHs=True) | |
| elif path.endswith('.mol'): | |
| return Chem.MolFromMolFile(path, sanitize=False, removeHs=True) | |
| elif path.endswith('.mol2'): | |
| return Chem.MolFromMol2File(path, sanitize=False, removeHs=True) | |
| elif path.endswith('.sdf'): | |
| return Chem.SDMolSupplier(path, sanitize=False, removeHs=True)[0] | |
| raise Exception('Unknown file extension') | |
| def generate(input_file): | |
| try: | |
| path = input_file.name | |
| molecule = read_molecule(path) | |
| name = '.'.join(path.split('/')[-1].split('.')[:-1]) | |
| out_sdf = f'results/{name}_generated.sdf' | |
| print(f'Input path={path}, name={name}') | |
| except Exception as e: | |
| return f'Could not read the molecule: {e}' | |
| positions, one_hot, charges = parse_molecule(molecule, is_geom=True) | |
| positions = torch.tensor(positions, dtype=const.TORCH_FLOAT, device=device) | |
| one_hot = torch.tensor(one_hot, dtype=const.TORCH_FLOAT, device=device) | |
| print('Read and parsed molecule') | |
| dataset = [{ | |
| 'uuid': '0', | |
| 'name': '0', | |
| 'positions': torch.tensor(positions, dtype=const.TORCH_FLOAT, device=device), | |
| 'one_hot': torch.tensor(one_hot, dtype=const.TORCH_FLOAT, device=device), | |
| 'charges': torch.tensor(charges, dtype=const.TORCH_FLOAT, device=device), | |
| 'anchors': torch.zeros_like(charges, dtype=const.TORCH_FLOAT, device=device), | |
| 'fragment_mask': torch.ones_like(charges, dtype=const.TORCH_FLOAT, device=device), | |
| 'linker_mask': torch.zeros_like(charges, dtype=const.TORCH_FLOAT, device=device), | |
| 'num_atoms': len(positions), | |
| }] | |
| dataloader = get_dataloader(dataset, batch_size=1, collate_fn=collate_with_fragment_edges) | |
| print('Created dataloader') | |
| for data in dataloader: | |
| chain, node_mask = ddpm.sample_chain(data, sample_fn=sample_fn, keep_frames=1) | |
| print('Generated linker') | |
| x = chain[0][:, :, :ddpm.n_dims] | |
| h = chain[0][:, :, ddpm.n_dims:] | |
| save_xyz_file('results', h, x, node_mask, names=[name], is_geom=True, suffix='generated') | |
| print('Saved XYZ file') | |
| subprocess.run(f'obabel results/{name}_generated.xyz -O {out_sdf}', shell=True) | |
| print('Converted to SDF') | |
| break | |
| generated_molecule = read_molecule_content(out_sdf) | |
| html = HTML_TEMPLATE.format(molecule=generated_molecule, fmt='sdf') | |
| return IFRAME_TEMPLATE.format(html=html) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown('# DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design') | |
| with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown('## Input Fragments') | |
| gr.Markdown('Upload the file with 3D-coordinates of the input fragments in .pdb, .mol2 or .sdf format') | |
| input_file = gr.File(file_count='single', label='Input fragments') | |
| button = gr.Button('Generate Linker!') | |
| gr.Markdown('') | |
| visualization = gr.HTML() | |
| button.click( | |
| fn=generate, | |
| inputs=[input_file], | |
| outputs=[visualization], | |
| ) | |
| demo.launch() | |