import argparse import gradio as gr import numpy as np import os import torch import subprocess import output from rdkit import Chem from src import const from src.datasets import get_dataloader, collate_with_fragment_edges, parse_molecule from src.lightning import DDPM from src.linker_size_lightning import SizeClassifier from src.generation import N_SAMPLES, generate_linkers, try_to_convert_to_sdf MODELS_METADATA = { 'geom_difflinker': { 'link': 'https://zenodo.org/record/7121300/files/geom_difflinker.ckpt?download=1', 'path': 'models/geom_difflinker.ckpt', }, 'geom_difflinker_given_anchors': { 'link': 'https://zenodo.org/record/7775568/files/geom_difflinker_given_anchors.ckpt?download=1', 'path': 'models/geom_difflinker_given_anchors.ckpt', }, 'pockets_difflinker': { 'link': 'https://zenodo.org/record/7775568/files/pockets_difflinker_full_no_anchors.ckpt?download=1', 'path': 'models/pockets_difflinker.ckpt', }, 'pockets_difflinker_given_anchors': { 'link': 'https://zenodo.org/record/7775568/files/pockets_difflinker_full.ckpt?download=1', 'path': 'models/pockets_difflinker_given_anchors.ckpt', }, } parser = argparse.ArgumentParser() parser.add_argument('--ip', type=str, default=None) args = parser.parse_args() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f'Device: {device}') os.makedirs("results", exist_ok=True) os.makedirs("models", exist_ok=True) size_gnn_path = 'models/geom_size_gnn.ckpt' if not os.path.exists(size_gnn_path): print('Downloading SizeGNN model...') link = 'https://zenodo.org/record/7121300/files/geom_size_gnn.ckpt?download=1' subprocess.run(f'wget {link} -O {size_gnn_path}', shell=True) size_nn = SizeClassifier.load_from_checkpoint('models/geom_size_gnn.ckpt', map_location=device).eval().to(device) print('Loaded SizeGNN model') diffusion_models = {} for model_name, metadata in MODELS_METADATA.items(): link = metadata['link'] diffusion_path = metadata['path'] if not os.path.exists(diffusion_path): print(f'Downloading {model_name}...') subprocess.run(f'wget {link} -O {diffusion_path}', shell=True) diffusion_models[model_name] = DDPM.load_from_checkpoint(diffusion_path, map_location=device).eval().to(device) print(f'Loaded model {model_name}') print(os.curdir) print(os.path.abspath(os.curdir)) print(os.listdir(os.curdir)) 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 show_input(input_file): if input_file is None: return ['', gr.Radio.update(visible=False, value='Sample 1'), None] if isinstance(input_file, str): path = input_file else: path = input_file.name extension = path.split('.')[-1] if extension not in ['sdf', 'pdb', 'mol', 'mol2']: msg = output.INVALID_FORMAT_MSG.format(extension=extension) return [ output.IFRAME_TEMPLATE.format(html=msg), gr.Radio.update(visible=False), None, ] try: molecule = read_molecule_content(path) except Exception as e: return [ f'Could not read the molecule: {e}', gr.Radio.update(visible=False), None, ] html = output.INITIAL_RENDERING_TEMPLATE.format(molecule=molecule, fmt=extension) return [ output.IFRAME_TEMPLATE.format(html=html), gr.Radio.update(visible=False), None, ] def draw_sample(idx, out_files): if isinstance(idx, str): idx = int(idx.strip().split(' ')[-1]) - 1 in_file = out_files[0] in_sdf = in_file if isinstance(in_file, str) else in_file.name out_file = out_files[idx + 1] out_sdf = out_file if isinstance(out_file, str) else out_file.name input_fragments_content = read_molecule_content(in_sdf) generated_molecule_content = read_molecule_content(out_sdf) fragments_fmt = in_sdf.split('.')[-1] molecule_fmt = out_sdf.split('.')[-1] html = output.SAMPLES_RENDERING_TEMPLATE.format( fragments=input_fragments_content, fragments_fmt=fragments_fmt, molecule=generated_molecule_content, molecule_fmt=molecule_fmt, ) return output.IFRAME_TEMPLATE.format(html=html) def generate(input_file, n_steps, n_atoms, radio_samples, selected_atoms): # Parsing selected atoms (javascript output) selected_atoms = selected_atoms.strip() if selected_atoms == '': selected_atoms = [] else: selected_atoms = list(map(int, selected_atoms.split(','))) # Selecting model if len(selected_atoms) == 0: selected_model_name = 'geom_difflinker' else: selected_model_name = 'geom_difflinker_given_anchors' if input_file is None: return [None, None, None, None] print(f'Start generating with model {selected_model_name}, selected_atoms:', selected_atoms) ddpm = diffusion_models[selected_model_name] path = input_file.name extension = path.split('.')[-1] if extension not in ['sdf', 'pdb', 'mol', 'mol2']: msg = output.INVALID_FORMAT_MSG.format(extension=extension) return [output.IFRAME_TEMPLATE.format(html=msg), None, None, None] try: molecule = read_molecule(path) molecule = Chem.RemoveAllHs(molecule) name = '.'.join(path.split('/')[-1].split('.')[:-1]) inp_sdf = f'results/input_{name}.sdf' except Exception as e: error = f'Could not read the molecule: {e}' msg = output.ERROR_FORMAT_MSG.format(message=error) return [output.IFRAME_TEMPLATE.format(html=msg), None, None, None] if molecule.GetNumAtoms() > 50: error = f'Too large molecule: upper limit is 50 heavy atoms' msg = output.ERROR_FORMAT_MSG.format(message=error) return [output.IFRAME_TEMPLATE.format(html=msg), None, None, None] with Chem.SDWriter(inp_sdf) as w: w.write(molecule) positions, one_hot, charges = parse_molecule(molecule, is_geom=True) anchors = np.zeros_like(charges) anchors[selected_atoms] = 1 fragment_mask = np.ones_like(charges) linker_mask = np.zeros_like(charges) 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.tensor(anchors, dtype=const.TORCH_FLOAT, device=device), 'fragment_mask': torch.tensor(fragment_mask, dtype=const.TORCH_FLOAT, device=device), 'linker_mask': torch.tensor(linker_mask, dtype=const.TORCH_FLOAT, device=device), 'num_atoms': len(positions), }] * N_SAMPLES dataloader = get_dataloader(dataset, batch_size=N_SAMPLES, collate_fn=collate_with_fragment_edges) print('Created dataloader') ddpm.edm.T = n_steps if n_atoms == 0: def sample_fn(_data): out, _ = size_nn.forward(_data, return_loss=False) probabilities = torch.softmax(out, 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 else: def sample_fn(_data): return torch.ones(_data['positions'].shape[0], device=device, dtype=torch.long) * n_atoms for data in dataloader: try: generate_linkers(ddpm=ddpm, data=data, sample_fn=sample_fn, name=name) except Exception as e: error = f'Caught exception while generating linkers: {e}' msg = output.ERROR_FORMAT_MSG.format(message=error) return [output.IFRAME_TEMPLATE.format(html=msg), None, None, None] out_files = try_to_convert_to_sdf(name) out_files = [inp_sdf] + out_files return [ draw_sample(radio_samples, out_files), out_files, gr.Radio.update(visible=True), None ] demo = gr.Blocks() with demo: gr.Markdown('# DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design') gr.Markdown( 'Given a set of disconnected fragments in 3D, ' 'DiffLinker places missing atoms in between and designs a molecule incorporating all the initial fragments. ' 'Our method can link an arbitrary number of fragments, requires no information on the attachment atoms ' 'and linker size, and can be conditioned on the protein pockets.' ) gr.Markdown( '[**[Paper]**](https://arxiv.org/abs/2210.05274) ' '[**[Code]**](https://github.com/igashov/DiffLinker)' ) with gr.Box(): with gr.Row(): hidden = gr.Textbox(visible=False) 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') n_steps = gr.Slider(minimum=10, maximum=500, label="Number of Denoising Steps", step=10) n_atoms = gr.Slider( minimum=0, maximum=20, label="Linker Size: DiffLinker will predict it if set to 0", step=1 ) examples = gr.Dataset( components=[gr.File(visible=False)], samples=[['examples/example_1.sdf'], ['examples/example_2.sdf']], type='index', ) button = gr.Button('Generate Linker!') gr.Markdown('') gr.Markdown('## Output Files') gr.Markdown('Download files with the generated molecules here:') output_files = gr.File(file_count='multiple', label='Output Files', interactive=False) with gr.Column(): gr.Markdown('## Visualization') gr.Markdown('**Hint:** click on atoms to select anchor points (optionally)') samples = gr.Radio( choices=['Sample 1', 'Sample 2', 'Sample 3', 'Sample 4', 'Sample 5'], value='Sample 1', type='value', show_label=False, visible=False, interactive=True, ) visualization = gr.HTML() input_file.change( fn=show_input, inputs=[input_file], outputs=[visualization, samples, hidden], ) input_file.clear( fn=lambda: [None, '', gr.Radio.update(visible=False), None], inputs=[], outputs=[input_file, visualization, samples, hidden], ) examples.click( fn=lambda idx: [f'examples/example_{idx+1}.sdf', 10, 0] + show_input(f'examples/example_{idx+1}.sdf'), inputs=[examples], outputs=[input_file, n_steps, n_atoms, visualization, samples, hidden] ) button.click( fn=generate, inputs=[input_file, n_steps, n_atoms, samples, hidden], outputs=[visualization, output_files, samples, hidden], _js=output.RETURN_SELECTION_JS, ) samples.change( fn=draw_sample, inputs=[samples, output_files], outputs=[visualization], ) demo.load(_js=output.STARTUP_JS) demo.launch(server_name=args.ip)