app.py

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.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


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, return_loss=False)
    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 show_input(input_file):
    if input_file is None:
        return ''

    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)

    try:
        molecule = read_molecule_content(path)
    except Exception as e:
        return f'Could not read the molecule: {e}'

    html = output.HTML_TEMPLATE.format(molecule=molecule, fmt=extension)
    return output.IFRAME_TEMPLATE.format(html=html)


def generate(input_file):
    if input_file is None:
        return ''

    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)

    try:
        molecule = read_molecule(path)
        name = '.'.join(path.split('/')[-1].split('.')[:-1])
        inp_sdf = f'results/{name}_input.sdf'
        inp_xyz = f'results/{name}_input.xyz'
        out_sdf = f'results/{name}_output.sdf'
        out_xyz = f'results/{name}_output.xyz'
    except Exception as e:
        return f'Could not read the molecule: {e}'

    if molecule.GetNumAtoms() > 50:
        return f'Too large molecule: upper limit is 50 heavy atoms'

    with Chem.SDWriter(inp_sdf) as w:
        w.write(molecule)
    Chem.MolToXYZFile(molecule, inp_xyz)

    positions, one_hot, charges = parse_molecule(molecule, is_geom=True)
    anchors = np.zeros_like(charges)
    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),
    }]
    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='output')
        print('Saved XYZ file')
        subprocess.run(f'obabel {out_xyz} -O {out_sdf}', shell=True)
        print('Converted to SDF')
        break

    generated_molecule = read_molecule_content(out_sdf)
    html = output.HTML_TEMPLATE.format(molecule=generated_molecule, fmt='sdf')
    return [
        output.IFRAME_TEMPLATE.format(html=html),
        [inp_sdf, inp_xyz, out_sdf, out_xyz],
    ]


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('')
                gr.Markdown('## Output Files')
                gr.Markdown('Download files with the generated molecules here:')
                output_files = gr.File(file_count='multiple', label='Output Files')
            with gr.Column():
                visualization = gr.HTML()

    input_file.change(
        fn=show_input,
        inputs=[input_file],
        outputs=[visualization],
    )
    button.click(
        fn=generate,
        inputs=[input_file],
        outputs=[visualization, output_files],
    )

demo.launch()