foundry/examples/all.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "819e8193",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For now, we still need to manually add atomworks dependency\n",
    "import sys;\n",
    "modelhub_test = \"/home/rohith/modelhub/release\"\n",
    "sys.path.append(f'{modelhub_test}/models/rfd3/src')\n",
    "sys.path.append(f'{modelhub_test}/models/mpnn/src')\n",
    "sys.path.append(f'{modelhub_test}/src')\n",
    "\n",
    "from atomworks.io.utils.visualize import view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d16cb95b-3f4c-4167-952b-278bdb561bf7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Running RFD3\n",
    "from rfd3.engine import RFD3InferenceConfig, RFD3InferenceEngine\n",
    "conf = RFD3InferenceConfig(\n",
    "    ckpt_path='/projects/ml/aa_design/models/rfd3_latest_cleaned.ckpt',\n",
    "    specification={\n",
    "        'length': 10\n",
    "    },\n",
    "    diffusion_batch_size=2,\n",
    ")\n",
    "model = RFD3InferenceEngine(**conf)\n",
    "outputs = model.run(\n",
    "    inputs=None,\n",
    "    out_dir=None,\n",
    "    n_batches=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35253ef3-2ce1-4dca-958a-63b359b70d73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for idx, data in outputs.items():\n",
    "    print(f\"Output type for batch {idx}: {type(data)}[0] = {type(data[0])}\")\n",
    "    print(f\"Output atom_array: {data[0].atom_array}\")\n",
    "    atom_array = data[0].atom_array\n",
    "view(atom_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d07ae413",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running RFD3\n",
    "from mpnn.inference_engines.mpnn import MPNNInferenceEngine\n",
    "\n",
    "#see defaults in mpnn.utils.inference.py : MPNN_GLOBAL_INFERENCE_DEFAULTS\n",
    "conf = {\n",
    "    ## protein_mpnn options\n",
    "    #\"model_type\":\"protein_mpnn\",\n",
    "    #\"checkpoint_path\":\"/databases/mpnn/vanilla_model_weights/v_48_020.pt\",\n",
    "    ## ligand_mpnn options\n",
    "    \"model_type\":\"ligand_mpnn\",\n",
    "    \"checkpoint_path\":\"/databases/mpnn/ligand_mpnn_model_weights/s25_r010_t300_p.pt\",\n",
    "    \"is_legacy_weights\": True,\n",
    "    \"out_directory\": None,\n",
    "    \"write_structures\": False,\n",
    "    \"write_fasta\": False\n",
    "}\n",
    "\n",
    "# defaults in from mpnn.utils.inference import MPNN_PER_INPUT_INFERENCE_DEFAULTS\n",
    "inputs = [\n",
    "        {\n",
    "            \"batch_size\": 10,\n",
    "            \"remove_waters\":True\n",
    "        }\n",
    "    ]\n",
    "\n",
    "model = MPNNInferenceEngine(**conf)\n",
    "\n",
    "## update to use atom_arrays from previous cell\n",
    "outputs = model.run(input_dicts=inputs, atom_arrays=[atom_array])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75f5e558-3323-4094-9cce-c5a19b54f4cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from biotite.structure import get_residue_starts\n",
    "from atomworks.constants import STANDARD_AA\n",
    "\n",
    "for item in outputs:\n",
    "    atom_array = item.atom_array\n",
    "    res_starts = get_residue_starts(atom_array)\n",
    "    protein_seq = []\n",
    "    for res_name in atom_array.res_name[res_starts]:\n",
    "        if res_name in STANDARD_AA:\n",
    "            protein_seq.append(str(res_name))\n",
    "    print(protein_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d85eda46-4dca-4e92-8946-73b69a5536e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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