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diffdock

Running on T4

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import gradio as gr
import os

import copy
import os
import torch

import time
from argparse import ArgumentParser, Namespace, FileType
from rdkit.Chem import RemoveHs
from functools import partial
import numpy as np
import pandas as pd
from rdkit import RDLogger
from rdkit.Chem import MolFromSmiles, AddHs
from torch_geometric.loader import DataLoader
import yaml

print(os.getcwd())
print(os.listdir("datasets"))
from datasets.process_mols import (
    read_molecule,
    generate_conformer,
    write_mol_with_coords,
)
from datasets.pdbbind import PDBBind
from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl, get_t_schedule
from utils.sampling import randomize_position, sampling
from utils.utils import get_model
from utils.visualise import PDBFile
from tqdm import tqdm
from datasets.esm_embedding_preparation import esm_embedding_prep
import subprocess

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

with open(f"workdir/paper_score_model/model_parameters.yml") as f:
    score_model_args = Namespace(**yaml.full_load(f))

with open(f"workdir/paper_confidence_model/model_parameters.yml") as f:
    confidence_args = Namespace(**yaml.full_load(f))

t_to_sigma = partial(t_to_sigma_compl, args=score_model_args)

model = get_model(score_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True)
state_dict = torch.load(
    f"workdir/paper_score_model/best_ema_inference_epoch_model.pt",
    map_location=torch.device("cpu"),
)
model.load_state_dict(state_dict, strict=True)
model = model.to(device)
model.eval()

confidence_model = get_model(
    confidence_args,
    device,
    t_to_sigma=t_to_sigma,
    no_parallel=True,
    confidence_mode=True,
)
state_dict = torch.load(
    f"workdir/paper_confidence_model/best_model_epoch75.pt",
    map_location=torch.device("cpu"),
)
confidence_model.load_state_dict(state_dict, strict=True)
confidence_model = confidence_model.to(device)
confidence_model.eval()
tr_schedule = get_t_schedule(inference_steps=10)
rot_schedule = tr_schedule
tor_schedule = tr_schedule
print("common t schedule", tr_schedule)
failures, skipped, confidences_list, names_list, run_times, min_self_distances_list = (
    0,
    0,
    [],
    [],
    [],
    [],
)
N = 10


def get_pdb(pdb_code="", filepath=""):
    if pdb_code is None or pdb_code == "":
        try:
            return filepath.name
        except AttributeError as e:
            return None
    else:
        os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb")
        return f"{pdb_code}.pdb"


def get_ligand(smiles="", filepath=""):
    if smiles is None or smiles == "":
        try:
            return filepath.name
        except AttributeError as e:
            return None
    else:
        return smiles


def read_mol(molpath):
    with open(molpath, "r") as fp:
        lines = fp.readlines()
    mol = ""
    for l in lines:
        mol += l
    return mol


def molecule(input_pdb, ligand_pdb):

    structure = read_mol(input_pdb)
    mol = read_mol(ligand_pdb)

    x = (
        """<!DOCTYPE html>
        <html>
        <head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <style>
    body{
        font-family:sans-serif
    }
    .mol-container {
    width: 600px;
    height: 600px;
    position: relative;
    mx-auto:0
    }
    .mol-container select{
        background-image:None;
    }
    </style>
    <script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
    </head>
    <body>  
     <button id="startanimation">Replay diffusion process</button>
    <div id="container" class="mol-container"></div>
  
            <script>
               let ligand = `"""
        + mol
        + """`  
        let structure = `"""
        + structure
        + """`  
      
             let viewer = null;
      
             $(document).ready(function () {
                let element = $("#container");
                let config = { backgroundColor: "white" };
                viewer = $3Dmol.createViewer(element, config);
                viewer.addModel( structure, "pdb" );
                viewer.setStyle({}, {cartoon: {color: "gray"}});
                viewer.zoomTo();
                viewer.zoom(0.7);
                viewer.addModelsAsFrames(ligand, "pdb");
                viewer.animate({loop: "forward",reps: 1});
                
                viewer.getModel(1).setStyle({stick:{colorscheme:"magentaCarbon"}});
                viewer.render();
                
              })

              $("#startanimation").click(function() {
                viewer.animate({loop: "forward",reps: 1});
              });
        </script>
        </body></html>"""
    )

    return f"""<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='{x}'></iframe>"""


def esm(protein_path, out_file):
    esm_embedding_prep(out_file, protein_path)
    # create args object with defaults
    os.environ["HOME"] = "esm/model_weights"

    subprocess.call(
        f"python esm/scripts/extract.py esm2_t33_650M_UR50D {out_file} data/esm2_output --repr_layers 33 --include per_tok",
        shell=True,
    )


def update(inp, file, ligand_inp, ligand_file):
    pdb_path = get_pdb(inp, file)
    ligand_path = get_ligand(ligand_inp, ligand_file)

    esm(
        pdb_path,
        f"data/{os.path.basename(pdb_path)}_prepared_for_esm.fasta",
    )

    protein_path_list = [pdb_path]
    ligand_descriptions = [ligand_path]
    no_random = False
    ode = False
    no_final_step_noise = False
    out_dir = "results/test"
    test_dataset = PDBBind(
        transform=None,
        root="",
        protein_path_list=protein_path_list,
        ligand_descriptions=ligand_descriptions,
        receptor_radius=score_model_args.receptor_radius,
        cache_path="data/cache",
        remove_hs=score_model_args.remove_hs,
        max_lig_size=None,
        c_alpha_max_neighbors=score_model_args.c_alpha_max_neighbors,
        matching=False,
        keep_original=False,
        popsize=score_model_args.matching_popsize,
        maxiter=score_model_args.matching_maxiter,
        all_atoms=score_model_args.all_atoms,
        atom_radius=score_model_args.atom_radius,
        atom_max_neighbors=score_model_args.atom_max_neighbors,
        esm_embeddings_path="data/esm2_output",
        require_ligand=True,
        num_workers=1,
        keep_local_structures=False,
    )
    test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False)
    confidence_test_dataset = PDBBind(
        transform=None,
        root="",
        protein_path_list=protein_path_list,
        ligand_descriptions=ligand_descriptions,
        receptor_radius=confidence_args.receptor_radius,
        cache_path="data/cache",
        remove_hs=confidence_args.remove_hs,
        max_lig_size=None,
        c_alpha_max_neighbors=confidence_args.c_alpha_max_neighbors,
        matching=False,
        keep_original=False,
        popsize=confidence_args.matching_popsize,
        maxiter=confidence_args.matching_maxiter,
        all_atoms=confidence_args.all_atoms,
        atom_radius=confidence_args.atom_radius,
        atom_max_neighbors=confidence_args.atom_max_neighbors,
        esm_embeddings_path="data/esm2_output",
        require_ligand=True,
        num_workers=1,
    )
    confidence_complex_dict = {d.name: d for d in confidence_test_dataset}
    for idx, orig_complex_graph in tqdm(enumerate(test_loader)):
        if (
            confidence_model is not None
            and not (
                confidence_args.use_original_model_cache
                or confidence_args.transfer_weights
            )
            and orig_complex_graph.name[0] not in confidence_complex_dict.keys()
        ):
            skipped += 1
            print(
                f"HAPPENING | The confidence dataset did not contain {orig_complex_graph.name[0]}. We are skipping this complex."
            )
            continue
        try:
            data_list = [copy.deepcopy(orig_complex_graph) for _ in range(N)]
            randomize_position(
                data_list,
                score_model_args.no_torsion,
                no_random,
                score_model_args.tr_sigma_max,
            )
            pdb = None
            lig = orig_complex_graph.mol[0]
            visualization_list = []
            for graph in data_list:
                pdb = PDBFile(lig)
                pdb.add(lig, 0, 0)
                pdb.add(
                    (
                        orig_complex_graph["ligand"].pos
                        + orig_complex_graph.original_center
                    )
                    .detach()
                    .cpu(),
                    1,
                    0,
                )
                pdb.add(
                    (graph["ligand"].pos + graph.original_center).detach().cpu(),
                    part=1,
                    order=1,
                )
                visualization_list.append(pdb)

            start_time = time.time()
            if confidence_model is not None and not (
                confidence_args.use_original_model_cache
                or confidence_args.transfer_weights
            ):
                confidence_data_list = [
                    copy.deepcopy(confidence_complex_dict[orig_complex_graph.name[0]])
                    for _ in range(N)
                ]
            else:
                confidence_data_list = None

            data_list, confidence = sampling(
                data_list=data_list,
                model=model,
                inference_steps=10,
                tr_schedule=tr_schedule,
                rot_schedule=rot_schedule,
                tor_schedule=tor_schedule,
                device=device,
                t_to_sigma=t_to_sigma,
                model_args=score_model_args,
                no_random=no_random,
                ode=ode,
                visualization_list=visualization_list,
                confidence_model=confidence_model,
                confidence_data_list=confidence_data_list,
                confidence_model_args=confidence_args,
                batch_size=1,
                no_final_step_noise=no_final_step_noise,
            )
            ligand_pos = np.asarray(
                [
                    complex_graph["ligand"].pos.cpu().numpy()
                    + orig_complex_graph.original_center.cpu().numpy()
                    for complex_graph in data_list
                ]
            )
            run_times.append(time.time() - start_time)

            if confidence is not None and isinstance(
                confidence_args.rmsd_classification_cutoff, list
            ):
                confidence = confidence[:, 0]
            if confidence is not None:
                confidence = confidence.cpu().numpy()
                re_order = np.argsort(confidence)[::-1]
                confidence = confidence[re_order]
                confidences_list.append(confidence)
                ligand_pos = ligand_pos[re_order]
            write_dir = (
                f'{out_dir}/index{idx}_{data_list[0]["name"][0].replace("/","-")}'
            )
            os.makedirs(write_dir, exist_ok=True)
            for rank, pos in enumerate(ligand_pos):
                mol_pred = copy.deepcopy(lig)
                if score_model_args.remove_hs:
                    mol_pred = RemoveHs(mol_pred)
                if rank == 0:
                    write_mol_with_coords(
                        mol_pred, pos, os.path.join(write_dir, f"rank{rank+1}.sdf")
                    )
                write_mol_with_coords(
                    mol_pred,
                    pos,
                    os.path.join(
                        write_dir, f"rank{rank+1}_confidence{confidence[rank]:.2f}.sdf"
                    ),
                )
            self_distances = np.linalg.norm(
                ligand_pos[:, :, None, :] - ligand_pos[:, None, :, :], axis=-1
            )
            self_distances = np.where(
                np.eye(self_distances.shape[2]), np.inf, self_distances
            )
            min_self_distances_list.append(np.min(self_distances, axis=(1, 2)))

            filenames = []
            if confidence is not None:
                for rank, batch_idx in enumerate(re_order):
                    visualization_list[batch_idx].write(
                        os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb")
                    )
                    filenames.append(
                        os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb")
                    )
            else:
                for rank, batch_idx in enumerate(ligand_pos):
                    visualization_list[batch_idx].write(
                        os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb")
                    )
                    filenames.append(
                        os.path.join(write_dir, f"rank{rank+1}_reverseprocess.pdb")
                    )
            names_list.append(orig_complex_graph.name[0])
        except Exception as e:
            print("Failed on", orig_complex_graph["name"], e)
            failures += 1
            return None

    labels = [f"rank {i+1}" for i in range(len(filenames))]
    return (
        molecule(pdb_path, filenames[0]),
        gr.Dropdown.update(choices=labels, value="rank 1"),
        filenames,
        pdb_path,
    )


def updateView(out, filenames, pdb):
    i = int(out.replace("rank", ""))
    return molecule(pdb, filenames[i])


demo = gr.Blocks()

with demo:
    gr.Markdown("# DiffDock")
    gr.Markdown(
        ">**DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking**, Corso, Gabriele and Stärk, Hannes and Jing, Bowen and Barzilay, Regina and Jaakkola, Tommi, arXiv:2210.01776  [GitHub](https://github.com/gcorso/diffdock)"
    )
    gr.Markdown("Runs the diffusion model `10` times with `10` inference steps")
    with gr.Box():
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Protein")
                inp = gr.Textbox(
                    placeholder="PDB Code or upload file below", label="Input structure"
                )
                file = gr.File(file_count="single", label="Input PDB")
            with gr.Column():
                gr.Markdown("## Ligand")
                ligand_inp = gr.Textbox(
                    placeholder="Provide SMILES input or upload mol2/sdf file below",
                    label="SMILES string",
                )
                ligand_file = gr.File(file_count="single", label="Input Ligand")

    btn = gr.Button("Run predictions")

    gr.Markdown("## Output")
    pdb = gr.Variable()
    filenames = gr.Variable()
    out = gr.Dropdown(interactive=True, label="Ranked samples")
    mol = gr.HTML()
    gr.Examples(
        [
            [
                None,
                "examples/1a46_protein_processed.pdb",
                None,
                "examples/1a46_ligand.sdf",
            ]
        ],
        [inp, file, ligand_inp, ligand_file],
        [mol, out],
        # cache_examples=True,
    )
    btn.click(
        fn=update,
        inputs=[inp, file, ligand_inp, ligand_file],
        outputs=[mol, out, filenames, pdb],
    )
    out.change(fn=updateView, inputs=[out, filenames, pdb], outputs=mol)
demo.launch()