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0613483 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | """Plot FuzzyScore distributions for D/L-peptide designs.
Mirrors ``plot_dl_synthesis_distribution`` but swaps the x-axis to the
``bo.scoring.FuzzyScore`` geometric-mean desirability score. Per the README
spec, FuzzyScore combines per-property desirability functions (here:
ProteinMPNN NLL and SWI solubility) into a unified score in (0, 1].
Desirability control points are anchored to the combined D+L 10/90
percentiles of each property (per target); weights are equal (importance=1,
i.e. pure desirability in the geometric mean). Same targets (PDL1, MDM2,
IL23R), same buried-pose filter, same D/L down-sampling as the ProteinMPNN
and synthesis figures, so all three are row-for-row comparable.
Tune ``DESIR_WEIGHT`` or the percentile anchors if a different trade-off is
desired.
"""
from pathlib import Path
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
SCRIPT_DIR = Path(__file__).resolve().parent
if str(SCRIPT_DIR) not in sys.path:
sys.path.insert(0, str(SCRIPT_DIR))
from plot_ablation_dl_figures import ( # noqa: E402
PALETTE,
TARGETS,
_load_filtered_dl_df,
apply_nature_style,
save_pub_fig,
)
from bo.scoring import FuzzyScore, swi # noqa: E402
DESIR_WEIGHT = 1.0 # importance per property (1 = pure desirability)
PCT_LOW, PCT_HIGH = 10, 90 # percentile anchors for the control points
def _desirability_controls(combined):
"""Build MPNN-NLL and SWI desirability control points from combined D+L."""
mp_low, mp_high = np.percentile(combined["score"], [PCT_LOW, PCT_HIGH])
sw_low, sw_high = np.percentile(combined["SWI"], [PCT_LOW, PCT_HIGH])
# NLL: lower is better -> high desirability at the low end
mpnn_points = [{"x": float(mp_low), "y": 1.0}, {"x": float(mp_high), "y": 0.0}]
# SWI: higher is better -> high desirability at the high end
swi_points = [{"x": float(sw_low), "y": 0.0}, {"x": float(sw_high), "y": 1.0}]
return {"score": mpnn_points, "SWI": swi_points}
def _add_fuzzyscore(df, controls):
"""Attach the bo FuzzyScore to a per-design DataFrame."""
df = df.copy()
df["SWI"] = df["sequence"].map(swi)
fs = FuzzyScore(df)
out = fs.fuzzy_score(
score=(controls["score"], DESIR_WEIGHT),
SWI=(controls["SWI"], DESIR_WEIGHT),
)
return out
def plot_dl_fuzzyscore_distribution():
apply_nature_style(font_size=8)
from scipy import stats as sp_stats
fig, axes = plt.subplots(1, len(TARGETS), figsize=(9.2, 2.8))
rng = np.random.default_rng(42)
for idx, target in enumerate(TARGETS):
ax = axes[idx]
d_df = _load_filtered_dl_df(target, "D_peptide")
l_df = _load_filtered_dl_df(target, "L_peptide")
d_df["SWI"] = d_df["sequence"].map(swi)
l_df["SWI"] = l_df["sequence"].map(swi)
# Match D count, identical sampling seed to the other D/L figures
n_d = len(d_df)
n_l = len(l_df)
if n_l > n_d:
l_df = l_df.sample(n=n_d, random_state=rng).copy()
controls = _desirability_controls(pd.concat([d_df, l_df]))
d_df = _add_fuzzyscore(d_df, controls)
l_df = _add_fuzzyscore(l_df, controls)
d_scores = d_df["FuzzyScore"].to_numpy()
l_scores = l_df["FuzzyScore"].to_numpy()
bins = np.linspace(
min(d_scores.min(), l_scores.min()),
max(np.percentile(d_scores, 99), np.percentile(l_scores, 99)),
50,
)
ax.hist(
d_scores, bins=bins, density=True, alpha=0.55,
color=PALETTE["blue_main"], label="D-peptide",
)
ax.hist(
l_scores, bins=bins, density=True, alpha=0.55,
color=PALETTE["orange"], label="L-peptide",
)
ks_stat, ks_p = sp_stats.ks_2samp(d_scores, l_scores)
ax.axvline(np.median(d_scores), color=PALETTE["blue_main"], ls="--", lw=0.8)
ax.axvline(np.median(l_scores), color=PALETTE["orange"], ls="--", lw=0.8)
ax.set_xlabel(f"{target} — FuzzyScore")
if idx == 0:
ax.set_ylabel("Density")
ax.text(
0.97, 0.95,
f"$n$ = {n_d}\nKS = {ks_stat:.3f}\np < {ks_p:.1e}" if ks_p < 0.001
else f"$n$ = {n_d}\nKS = {ks_stat:.3f}\np = {ks_p:.3f}",
transform=ax.transAxes, ha="right", va="top",
fontsize=7, color=PALETTE["neutral_dark"],
bbox=dict(boxstyle="round,pad=0.3", fc="white",
ec=PALETTE["neutral_light"], lw=0.5),
)
if idx == 0:
ax.legend(loc="upper left", fontsize=7)
save_pub_fig(fig, "fig_dl_fuzzyscore_distribution")
def main():
plot_dl_fuzzyscore_distribution()
if __name__ == "__main__":
main()
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