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import logging
import os
import time
from typing import TypedDict
import numpy as np
import pandas as pd
import redis
import torch
from chem_mrl.molecular_fingerprinter import MorganFingerprinter
from dotenv import load_dotenv
from rdkit import Chem, RDLogger
from redis.commands.search.field import TextField, VectorField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
from constants import (
EMBEDDING_DIMENSION,
HNSW_K,
HNSW_PARAMETERS,
MODEL_NAME,
SUPPORTED_EMBEDDING_DIMENSIONS,
USE_HALF_PRECISION,
)
from data import ISOMER_DESIGN_DATASET
def setup_logger(clear_handler=False):
if clear_handler:
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler) # issue with sentence-transformer's logging handler
RDLogger.DisableLog("rdApp.*") # type: ignore - DisableLog is an exported function
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
logger = logging.getLogger(__name__)
return logger
load_dotenv("../.env")
logger = setup_logger(clear_handler=True)
class SimilarMolecule(TypedDict):
smiles: str
name: str
properties: str
score: float
class MolecularEmbeddingService:
def __init__(self):
self.model_name = MODEL_NAME
self.index_name = "molecule_embeddings"
self.model_embed_dim = EMBEDDING_DIMENSION
self.model = self._initialize_model()
self.redis_client = self._initialize_redis()
self._initialize_datastore()
def _initialize_model(self):
"""Initialize the Hugging Face transformers model"""
try:
model = SentenceTransformer(
self.model_name,
model_kwargs={
"torch_dtype": torch.float16 if USE_HALF_PRECISION else torch.float32,
},
)
model.eval()
return model
except Exception as e:
logger.error(f"Failed to load model: {e}")
raise
def _initialize_redis(self):
"""Initialize Redis connection"""
try:
redis_host = os.getenv("REDIS_HOST", "localhost")
redis_port = int(os.getenv("REDIS_PORT", 6379))
redis_password = os.getenv("REDIS_PASSWORD", None)
logger.info(
f"Connecting to Redis at {redis_host}:{redis_port} with password: {'***' if redis_password else 'None'}"
)
redis_client = redis.Redis(
host=redis_host,
port=redis_port,
password=redis_password,
decode_responses=True,
)
except Exception as e:
logger.error(f"Failed to connect to Redis: {e}")
raise
while True:
try:
redis_client.ping()
break
except redis.exceptions.BusyLoadingError:
time_out = 5
logger.warning(f"Redis is loading the dataset in memory. Retrying in {time_out} seconds...")
time.sleep(time_out)
return redis_client
def _initialize_datastore(self):
self.__create_hnsw_index()
self.__populate_sample_data(ISOMER_DESIGN_DATASET)
def __create_hnsw_index(self):
"""Create HNSW index for molecular embeddings"""
try:
self.redis_client.ft(self.index_name).info()
logger.info(f"Index {self.index_name} already exists")
return
except redis.exceptions.ResponseError:
pass
try:
schema: list[TextField | VectorField] = [
VectorField(
self.embedding_field_name(dim),
"HNSW",
{
**HNSW_PARAMETERS,
"DIM": dim,
},
)
for dim in SUPPORTED_EMBEDDING_DIMENSIONS
]
schema.insert(0, TextField("smiles"))
self.redis_client.ft(self.index_name).create_index(
schema,
definition=IndexDefinition(prefix=[self.molecule_index_prefix("")], index_type=IndexType.HASH),
)
logger.info(f"Created HNSW index: {self.index_name}")
except Exception as e:
logger.error(f"Failed to create HNSW index: {e}")
raise
def __populate_sample_data(self, df: pd.DataFrame):
"""Populate Redis with sample molecular data"""
logger.info("Populating Redis with sample molecular data...")
for _, row in df.iterrows():
try:
key = self.molecule_index_prefix(row["smiles"])
if self.redis_client.exists(key):
continue
embedding_cache: np.ndarray = self.get_molecular_embedding(row["smiles"], EMBEDDING_DIMENSION)
mapping: dict[str, bytes | str] = {
self.embedding_field_name(embed_dim): self._truncate_and_normalize_embedding(
embedding_cache.copy(), embed_dim
).tobytes()
for embed_dim in SUPPORTED_EMBEDDING_DIMENSIONS
}
mapping = {**mapping, **row.to_dict()}
self.redis_client.hset(
key,
mapping=mapping, # type: ignore
)
except Exception as e:
logger.error(f"Failed to process molecule {row}: {e}")
continue
logger.info(f"Populated {len(df)} sample molecules")
def get_molecular_embedding(self, smiles: str, embed_dim: int) -> np.ndarray:
"""Generate molecular embedding using ChemMRL"""
try:
if embed_dim <= 0:
raise ValueError("embed_dim must be positive")
# Preprocess smiles similarly as training data for optimal performance
smiles = MorganFingerprinter.canonicalize_smiles(smiles) or smiles
embedding: np.ndarray = self.model.encode(
[smiles],
show_progress_bar=False,
convert_to_numpy=True,
)[0]
return self._truncate_and_normalize_embedding(embedding, embed_dim)
except Exception as e:
logger.error(f"Failed to generate embedding for {smiles}: {e}")
raise
def _truncate_and_normalize_embedding(self, embedding: np.ndarray, embed_dim: int) -> np.ndarray:
"""Truncate and normalize embedding"""
if embed_dim < len(embedding):
embedding = embedding[:embed_dim]
norms = np.linalg.norm(embedding, ord=2, keepdims=True)
return embedding / np.where(norms == 0, 1, norms)
def find_similar_molecules(
self, query_embedding: np.ndarray, embed_dim: int, k: int = HNSW_K
) -> list[SimilarMolecule]:
"""Find k most similar molecules using HNSW"""
try:
query_vector = query_embedding.tobytes()
query = (
Query(f"*=>[KNN {k} @{self.embedding_field_name(embed_dim)} $vec AS score]")
.sort_by("score")
.return_fields("smiles", "name", "properties", "score")
.dialect(2)
)
results = self.redis_client.ft(self.index_name).search(
query,
query_params={
"vec": query_vector, # type: ignore
},
)
neighbors: list[SimilarMolecule] = [
{"smiles": doc.smiles, "name": doc.name, "properties": doc.properties, "score": float(doc.score)}
for doc in results.docs
]
return neighbors
except Exception as e:
logger.error(f"Failed to find similar molecules: {e}")
return []
@staticmethod
def get_canonical_smiles(smiles: str | None) -> str:
"""Convert SMILES to canonical SMILES representation"""
if not smiles or smiles.strip() == "":
return ""
canonical = MorganFingerprinter.canonicalize_smiles(smiles.strip())
if canonical is None:
return smiles.strip()
return canonical
@staticmethod
def get_smiles_from_mol_file(mol_file: str) -> str:
"""Convert SMILES to canonical SMILES representation"""
if not mol_file or mol_file.strip() == "":
return ""
mol = Chem.rdmolfiles.MolFromMolBlock(mol_file)
if mol is None:
return ""
return Chem.MolToSmiles(mol, canonical=True)
@staticmethod
def embedding_field_name(dim: int) -> str:
return f"embedding_{dim}"
@staticmethod
def molecule_index_prefix(smiles: str) -> str:
return f"mol:{smiles}"
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