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import logging
from typing import List
import numpy as np
import mols2grid
import pandas as pd
from rdkit import Chem
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
def draw_grid_predict(
sequences: List[str], properties: np.array, property_names: List[str], domain: str
) -> str:
"""
Uses mols2grid to draw a HTML grid for the prediction
Args:
sequences: Sequences for which properties are predicted.
properties: Predicted properties. Array of shape (n_samples, n_properties).
names: List of property names
domain: Domain of the prediction (molecules or proteins).
Returns:
HTML to display
"""
if domain not in ["Molecules", "Proteins"]:
raise ValueError(f"Unsupported domain {domain}")
if domain == "Proteins":
converter = lambda x: Chem.MolToSmiles(Chem.MolFromFASTA(x))
else:
converter = lambda x: x
smiles = []
for sequence in sequences:
try:
seq = converter(sequence)
smiles.append(seq)
except Exception:
logger.warning(f"Could not draw sequence {seq}")
result = pd.DataFrame({"SMILES": smiles})
if domain == "Proteins":
result["Seqs"] = sequences
for i, name in enumerate(property_names):
result[name] = properties[:, i]
n_cols = min(3, len(result))
size = (140, 200) if len(result) > 3 else (600, 700)
obj = mols2grid.display(
result,
tooltip=list(result.keys()),
height=1100,
n_cols=n_cols,
name="Results",
size=size,
)
return obj.data
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