File size: 3,700 Bytes
d646213 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | from __future__ import annotations
from typing import Iterable, Sequence
import numpy as np
import py3Dmol
from IPython.display import HTML, display
from rdkit import Chem
from rdkit.Chem import Mol
def visualize_conformers_grid(
mol: Mol,
weights: Sequence[float],
key_conformers: Iterable[int],
top_n: int = 5,
style: str = "stick",
n_cols: int = 4,
width: int = 250,
height: int = 250,
show_all: bool = False,
sort_by_weight: bool = True,
) -> None:
"""Render a molecule's conformers as a grid of 3D viewers in a notebook.
Highlights the true key conformers and the top predicted ones by color,
so you can eyeball how well predicted weights line up with the known key
instances. Displays the grid directly via IPython; nothing is returned.
Args:
mol (rdkit.Chem.Mol): Molecule with one embedded conformer per entry
in ``weights``.
weights (Sequence[float]): Predicted weight for each conformer, in
conformer-index order.
key_conformers (Iterable[int]): Indices of the true key conformers,
highlighted in red.
top_n (int): Number of highest-weighted conformers to highlight in
blue as predictions.
style (str): py3Dmol rendering style (e.g. ``"stick"``).
n_cols (int): Number of viewers per grid row.
width (int): Width in pixels of each viewer.
height (int): Height in pixels of each viewer.
show_all (bool): If True, show every conformer instead of only the
key and top-predicted ones.
sort_by_weight (bool): If True, order the shown conformers by
descending predicted weight.
"""
num_confs = mol.GetNumConformers()
if num_confs != len(weights):
raise ValueError("Number of weights must equal number of conformers")
# top-N predicted indices
top_indices = set(np.argsort(weights)[-top_n:][::-1])
key_conformers = set(key_conformers)
if show_all:
conf_indices = list(range(num_confs))
else:
conf_indices = sorted(key_conformers.union(top_indices))
# sort conformers by weight if requested
if sort_by_weight:
conf_indices = sorted(conf_indices, key=lambda i: weights[i], reverse=True)
viewers_html = []
for i in conf_indices:
conf = mol.GetConformer(int(i))
block = Chem.MolToMolBlock(mol, confId=conf.GetId())
color = "0xAAAAAA" # default grey
label = f"Conf {i} (w={weights[i]:.2f})"
if i in key_conformers:
color = "0xFF0000" # red
label += " [TRUE]"
elif i in top_indices:
color = "0x0000FF" # blue
label += " [PRED]"
viewer = py3Dmol.view(width=width, height=height)
viewer.addModel(block, "sdf")
viewer.setStyle({style: {"color": color}})
viewer.zoomTo()
html = viewer._make_html()
viewers_html.append(f"<div style='display:inline-block; text-align:center;'>{html}<br>{label}</div>")
# arrange into grid
rows = []
for i in range(0, len(viewers_html), n_cols):
row_html = "".join(viewers_html[i : i + n_cols])
rows.append(f"<div style='margin-bottom:20px'>{row_html}</div>")
# add legend
legend_html = """
<div style='margin:10px 0;'>
<b>Legend:</b>
<span style='color:red;'>[TRUE]=Ground truth</span> |
<span style='color:blue;'>[PRED]=Top predicted</span> |
<span style='color:gray;'>Others</span>
</div>
"""
display(HTML(legend_html + "".join(rows)))
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