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"source": [
"### Explore AI4LifeScience Tools in OpenBioMed\n",
"\n",
"OpenBioMed implements a suite of AIs tools for accelerating life science research including:\n",
"- molecular property prediction\n",
"- molecule editing\n",
"- text-based denovo molecule generation\n",
"- protein function prediction\n",
"- protein folding\n",
"- denovo protein generation\n",
"- protein mutation explanation & engineering\n",
"- protein-molecule docking\n",
"- structure-based drug design\n",
"\n",
"Feel free to [download](https://cloud.tsinghua.edu.cn/d/5d08f4bc502848dc83bd/) our trained models, put them under `checkpoints/server`, and explore their applications with your own data!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/AIRvePFS/dair/luoyz-data/projects/OpenBioMed/OpenBioMed_arch\n"
]
}
],
"source": [
"# Change working directory\n",
"import os\n",
"import sys\n",
"parent = os.path.dirname(os.path.abspath(''))\n",
"print(parent)\n",
"sys.path.append(parent)\n",
"os.chdir(parent)\n",
"\n",
"import logging\n",
"logging.basicConfig(level=logging.ERROR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In OpenBioMed, we provide a unified interface for deploying ML-based models and performing prediction through `InferencePipeline`. To construct a pipeline, you just need to configure the task, model, path to the trained checkpoint, and which device to deploy the model. \n",
"\n",
"You can use the pipeline.print_usage() function to identify the inputs and the outputs of the model. To construct appropriate inputs for molecule and protein inputs of the model, please refer to [manipulating_molecules](./manipulating_molecules.ipynb). \n",
"\n",
"Then, you can pass the inputs to pipeline.run() method to perform prediction. It accepts either single input or multiple inputs. The return value is a tuple, where the first element is a list of the original model outputs, and the second element is a list of metadata for building workflows (which you can simply ignore).\n",
"\n",
"Here we provide examples on two tasks. You can also modify model inputs [here](../open_biomed/scripts/inference.py) and run `python open_biomed/scripts/inference.py --task [TASK_NAME]` to test any task you are interested in.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Molecular property prediction.\n",
"Inputs: {\"molecule\": a small molecule}\n",
"Outputs: A float number in [0, 1] indicating the likeness of the molecule to exhibit certain properties.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Inference Steps: 100%|██████████| 1/1 [00:00<00:00, 174.02it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.582], [0.8478]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"from open_biomed.core.pipeline import InferencePipeline\n",
"from open_biomed.data.molecule import Molecule\n",
"\n",
"# Predict if a molecule can penetrate the blood-brain barrier (https://arxiv.org/abs/1703.00564) with a fine-tuned GraphMVP (https://arxiv.org/abs/2110.07728) model\n",
"pipeline = InferencePipeline(\n",
" task=\"molecule_property_prediction\",\n",
" model=\"graphmvp\",\n",
" model_ckpt=\"./checkpoints/demo/graphmvp-BBBP.ckpt\",\n",
" additional_config=\"./configs/dataset/bbbp.yaml\",\n",
" device=\"cpu\"\n",
")\n",
"print(pipeline.print_usage())\n",
"\n",
"# Construct molecules via SMILES strings\n",
"molecule1 = Molecule.from_smiles(\"Nc1[nH]c(C(=O)c2ccccc2)c(-c2ccccn2)c1C(=O)c1c[nH]c2ccc(Br)cc12\")\n",
"molecule2 = Molecule.from_smiles(\"CN1CCC[C@H]1COC2=NC3=C(CCN(C3)C4=CC=CC5=C4C(=CC=C5)Cl)C(=N2)N6CCN([C@H](C6)CC#N)C(=O)C(=C)F\")\n",
"\n",
"# The tool can handle multiple inputs simutaneously\n",
"outputs = pipeline.run(\n",
" molecule=[molecule1, molecule2]\n",
")[0]\n",
"print(outputs)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of EsmForProteinFolding were not initialized from the model checkpoint at /AIRvePFS/dair/users/ailin/.cache/huggingface/hub/esmfold_v1 and are newly initialized: ['esm.contact_head.regression.bias', 'esm.contact_head.regression.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Protein folding prediction.\n",
"Inputs: {\"protein\": a protein sequence}\n",
"Outputs: A protein object with 3D structure available.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Inference Steps: 100%|██████████| 1/1 [00:05<00:00, 5.26s/it]\n"
]
},
{
"data": {
"text/plain": [
"'./tmp/folded_protein.pdb'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from open_biomed.core.pipeline import InferencePipeline\n",
"from open_biomed.data.protein import Protein\n",
"\n",
"# Predict the 3D structure of the protein based on its amino acid sequence using EsmFold (https://www.science.org/doi/10.1126/science.ade2574)\n",
"# REMARK: It is recommended to use a GPU with at least 16GB memory to speed up inference. If you don't have a NVIDIA GPU, change the `device` argument to `cpu`.\n",
"pipeline = InferencePipeline(\n",
" task=\"protein_folding\",\n",
" model=\"esmfold\",\n",
" model_ckpt=\"./checkpoints/demo/esmfold.ckpt\",\n",
" device=\"cuda:0\" \n",
")\n",
"print(pipeline.print_usage())\n",
"\n",
"# Initialize a protein with an amino acid sequence\n",
"protein = Protein.from_fasta(\"MASDAAAEPSSGVTHPPRYVIGYALAPKKQQSFIQPSLVAQAASRGMDLVPVDASQPLAEQGPFHLLIHALYGDDWRAQLVAFAARHPAVPIVDPPHAIDRLHNRISMLQVVSELDHAADQDSTFGIPSQVVVYDAAALADFGLLAALRFPLIAKPLVADGTAKSHKMSLVYHREGLGKLRPPLVLQEFVNHGGVIFKVYVVGGHVTCVKRRSLPDVSPEDDASAQGSVSFSQVSNLPTERTAEEYYGEKSLEDAVVPPAAFINQIAGGLRRALGLQLFNFDMIRDVRAGDRYLVIDINYFPGYAKMPGYETVLTDFFWEMVHKDGVGNQQEEKGANHVVVK\")\n",
"outputs = pipeline.run(\n",
" protein=protein,\n",
")\n",
"# The output is still a Protein object, but its 3D backbone coordinates are available\n",
"# You can find the pdb file or use our [visualization tools](./visualization.ipynb) to inspect the structure\n",
"outputs[0][0].save_pdb(\"./tmp/folded_protein.pdb\")"
]
}
],
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