In this tutorial, we use Anthropic’s claude-protein-binder-design dataset, which contains 1,440 AI-designed miniprotein binders tested against 16 targets. Because the release includes both computational predictions and real wet-lab results from two independent labs, we can go beyond simply studying the designs. We evaluate how well structure predictors identify successful binders, whether combining predictions improves performance, how rankings translate into practical testing budgets, and how much disagreement comes from the assays themselves. Also, we train a target-aware classifier to test whether these signals can reliably predict experimental success.
Copy CodeCopiedUse a different Browserimport subprocess, sys, warnings, itertools, math
warnings.filterwarnings(“ignore”)
import importlib.util
_needed = {“huggingface_hub”: “huggingface_hub>=0.24”, “pyarrow”: “pyarrow”,
“pandas”: “pandas”, “sklearn”: “scikit-learn”,
“matplotlib”: “matplotlib”, “scipy”: “scipy”}
_missing = [pkg for mod, pkg in _needed.items() if importlib.util.find_spec(mod) is None]
if _missing:
print(“installing:”, “, “.join(_missing))
subprocess.run([sys.executable, “-m”, “pip”, “install”, “-q”, *_missing], check=False)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
from huggingface_hub import HfApi, hf_hub_download
from sklearn.metrics import roc_auc_score, cohen_kappa_score, average_precision_score
from sklearn.model_selection import GroupKFold, StratifiedKFold
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.inspection import permutation_importance
SEED = 0
rng_global = np.random.default_rng(SEED)
pd.set_option(“display.width”, 200)
pd.set_option(“display.max_columns”, 100)
plt.rcParams.update({“figure.dpi”: 110, “font.size”: 9, “axes.grid”: True,
“grid.alpha”: 0.25, “axes.spines.top”: False, “axes.spines.right”: False})
REPO = “Anthropic/claude-protein-binder-design”
BAR = “=” * 78
def head(n, title):
prefix = f”{n}. ” if str(n) else “”
print(f”n{BAR}n {prefix}{title}n{BAR}”)
head(1, “TABLE DISCOVERY”)
api = HfApi()
repo_files = api.list_repo_files(REPO, repo_type=”dataset”)
TABLES = {}
for f in repo_files:
if f.startswith(“data/tables/”) and f.endswith(“.parquet”):
key = f[len(“data/tables/”): -len(“.parquet”)].replace(“/”, “_”)
TABLES[key] = f
print(f”Found {len(TABLES)} Parquet tables:”)
for k in sorted(TABLES):
print(f” – {k:38s} {TABLES[k]}”)
def load_table(name: str) -> pd.DataFrame:
“””Load a subset by its viewer name, with a datasets-library fallback.”””
if name in TABLES:
return pd.read_parquet(hf_hub_download(REPO, TABLES[name], repo_type=”dataset”))
from datasets import load_dataset
return load_dataset(REPO, name, split=”full”).to_pandas()
ds = load_table(“design_summary”)
print(f”ndesign_summary: {ds.shape[0]:,} rows x {ds.shape[1]} columns”)
We start by installing only what the runtime is actually missing, then enumerate the repository once and build a {subset to path} map instead of hard-coding file locations. This matters because the naming is not uniform; the subset wetlab_summary lives at data/tables/wetlab/summary.parquet, and a guessed path would fail silently. With the map in place we pull design_summary, one row per design, 1,440 rows wide enough to carry every join we need downstream.
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CALLS = {“binder”, “non_binder”}
tested = ds[“adaptyv_binding”].isin(CALLS) | ds[“twist_binding”].isin(CALLS)
ev = ds[tested].copy()
ev[“y”] = ev[“binder_final”].astype(int)
print(f”All designs : {len(ds):,}”)
print(f”Evaluable (>=1 vendor call): {len(ev):,}”)
print(f”Confirmed binders : {int(ev[‘y’].sum()):,} ”
f”({100 * ev[‘y’].mean():.1f}% base rate)”)
print(f”Never measured : {len(ds) – len(ev):,}”)
print(“nCategorical levels:”)
for c in [“design_model”, “campaign”, “generator”, “sequence_design_method”, “vendor_agreement”]:
vals = ds[c].astype(str).value_counts()
print(f” {c:24s} ({len(vals)}): {‘, ‘.join(vals.index[:6])}”
+ (” …” if len(vals) > 6 else “”))
print(f”nTargets ({ds[‘target’].nunique()}): {‘, ‘.join(sorted(ds[‘target’].unique()))}”)
print(f”Binder length: {ds.binder_length.min()}-{ds.binder_length.max()} aa ”
f”(median {ds.binder_length.median():.0f})”)
head(3, “HIT-RATE LANDSCAPE”)
def wilson(k, n, z=1.96):
if n == 0:
return (np.nan, np.nan, np.nan)
p = k / n
d = 1 + z**2 / n
c = (p + z**2 / (2 * n)) / d
h = z * math.sqrt(p * (1 – p) / n + z**2 / (4 * n**2)) / d
return p, max(0.0, c – h), min(1.0, c + h)
def rate_table(df, by):
rows = []
for key, g in df.groupby(by, dropna=False):
p, lo, hi = wilson(int(g.y.sum()), len(g))
rows.append({by: key, “n”: len(g), “hits”: int(g.y.sum()),
“rate”: p, “lo”: lo, “hi”: hi})
return pd.DataFrame(rows).sort_values(“rate”, ascending=False).reset_index(drop=True)
for dim in [“design_model”, “campaign”, “generator”, “sequence_design_method”]:
t = rate_table(ev, dim)
print(f”n— hit rate by {dim} —“)
print(t.to_string(index=False,
formatters={“rate”: “{:.3f}”.format, “lo”: “{:.3f}”.format, “hi”: “{:.3f}”.format}))
tt = rate_table(ev, “target”)
fig, ax = plt.subplots(figsize=(9, 4.2))
ax.bar(tt.target, tt.rate, color=”#4C72B0″)
ax.errorbar(tt.target, tt.rate,
yerr=[(tt.rate – tt.lo).clip(lower=0), (tt.hi – tt.rate).clip(lower=0)],
fmt=”none”, ecolor=”0.25″, capsize=3, lw=1)
ax.axhline(ev.y.mean(), ls=”–“, c=”crimson”, lw=1, label=f”pooled {ev.y.mean():.2f}”)
ax.set_ylabel(“experimental hit rate”); ax.set_title(“Hit rate by target (Wilson 95% CI)”)
ax.tick_params(axis=”x”, rotation=55); ax.legend(); plt.tight_layout(); plt.show()
print(“nRead this plot as the dominant effect size in the dataset: target choice ”
“swamps generator choice. Any model comparison that does not stratify by ”
“target is mostly measuring which targets that model was pointed at.”)
We define the evaluable set by filtering on actual vendor calls rather than on binder_final, because that column is a bool and so records the 120 never-measured designs as False rather than missing. From there we compute hit rates by model, campaign, generator, and target, wrapping each in a Wilson interval since several subgroups sit in the small-n regime where the normal approximation misbehaves. The target plot is the one to read first: it shows antigen choice swamping every other factor we compare.
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PREDICTORS = sorted({c[len(“ipsae_min_”):] for c in ds.columns if c.startswith(“ipsae_min_”)})
print(f”Predictors ({len(PREDICTORS)}): {‘, ‘.join(PREDICTORS)}”)
def auc_ci(y, s, n_boot=300, seed=SEED):
s = np.asarray(s, dtype=float); y = np.asarray(y, dtype=int)
m = ~np.isnan(s)
y, s = y[m], s[m]
if len(y) < 30 or len(np.unique(y)) < 2:
return dict(auc=np.nan, lo=np.nan, hi=np.nan, n=len(y), ap=np.nan)
base = roc_auc_score(y, s)
ap = average_precision_score(y, s)
rng = np.random.default_rng(seed)
idx, boots = np.arange(len(y)), []
for _ in range(n_boot):
b = rng.choice(idx, len(idx), replace=True)
if len(np.unique(y[b])) > 1:
boots.append(roc_auc_score(y[b], s[b]))
lo, hi = (np.percentile(boots, [2.5, 97.5]) if boots else (np.nan, np.nan))
return dict(auc=base, lo=lo, hi=hi, n=len(y), ap=ap)
rows = []
for p in PREDICTORS:
for metric in [“ipsae_min”, “sc_dockq”]:
col = f”{metric}_{p}”
if col in ev.columns:
r = auc_ci(ev.y, ev[col])
rows.append({“predictor”: p, “metric”: metric, **r})
perf = pd.DataFrame(rows)
piv = perf.pivot(index=”predictor”, columns=”metric”, values=”auc”).sort_values(“ipsae_min”, ascending=False)
print(“nAUC vs experimental binder_final:”)
print(perf.sort_values(“auc”, ascending=False).to_string(
index=False, formatters={c: “{:.3f}”.format for c in [“auc”, “lo”, “hi”, “ap”]}))
fig, ax = plt.subplots(figsize=(9, 4.2))
x = np.arange(len(piv)); w = 0.38
for i, (metric, colr) in enumerate([(“ipsae_min”, “#4C72B0”), (“sc_dockq”, “#DD8452”)]):
sub = perf[perf.metric == metric].set_index(“predictor”).reindex(piv.index)
lo_err = (sub.auc – sub.lo).clip(lower=0).fillna(0)
hi_err = (sub.hi – sub.auc).clip(lower=0).fillna(0)
ax.bar(x + (i – 0.5) * w, sub.auc, w, label=metric, color=colr)
ax.errorbar(x + (i – 0.5) * w, sub.auc,
yerr=[lo_err, hi_err], fmt=”none”, ecolor=”0.3″, capsize=2, lw=0.9)
ax.axhline(0.5, ls=”–“, c=”crimson”, lw=1)
ax.set_xticks(x); ax.set_xticklabels(piv.index, rotation=45, ha=”right”)
ax.set_ylabel(“AUC”); ax.set_ylim(0.35, None)
ax.set_title(“In-silico score vs wet-lab binding, by structure predictor”)
ax.legend(); plt.tight_layout(); plt.show()
print(“Interpretation: AUCs land well above chance but far below the ~0.9 you ”
“would need to trust a single filter. That gap is the entire practical ”
“reason this dataset exists.”)
head(5, “CONSENSUS SCORING”)
ips_cols = [f”ipsae_min_{p}” for p in PREDICTORS if f”ipsae_min_{p}” in ev.columns]
dq_cols = [f”sc_dockq_{p}” for p in PREDICTORS if f”sc_dockq_{p}” in ev.columns]
def pct_rank(df, cols):
return df[cols].rank(pct=True, na_option=”keep”)
R_ips, R_dq = pct_rank(ev, ips_cols), pct_rank(ev, dq_cols)
ev[“cons_ipsae”] = R_ips.mean(axis=1)
ev[“cons_dockq”] = R_dq.mean(axis=1)
ev[“cons_all”] = pd.concat([R_ips, R_dq], axis=1).mean(axis=1)
ev[“cons_median”] = pd.concat([R_ips, R_dq], axis=1).median(axis=1)
ev[“cons_min”] = pd.concat([R_ips, R_dq], axis=1).min(axis=1)
ev[“cons_disagree”] = pd.concat([R_ips, R_dq], axis=1).std(axis=1)
best_single = perf.loc[perf.auc.idxmax()]
print(f”Best single column: {best_single.metric}_{best_single.predictor} AUC={best_single.auc:.3f}”)
print()
for name in [“cons_ipsae”, “cons_dockq”, “cons_all”, “cons_median”, “cons_min”, “cons_disagree”]:
r = auc_ci(ev.y, ev[name])
print(f” {name:16s} AUC={r[‘auc’]:.3f} [{r[‘lo’]:.3f}, {r[‘hi’]:.3f}] AP={r[‘ap’]:.3f}”)
corr = ev[ips_cols].corr(method=”spearman”)
fig, ax = plt.subplots(figsize=(6.2, 5.2))
im = ax.imshow(corr.values, cmap=”viridis”, vmin=0, vmax=1)
lbl = [c.replace(“ipsae_min_”, “”) for c in ips_cols]
ax.set_xticks(range(len(lbl))); ax.set_xticklabels(lbl, rotation=90)
ax.set_yticks(range(len(lbl))); ax.set_yticklabels(lbl)
ax.set_title(“Spearman correlation between predictors (ipSAE)”)
ax.grid(False); fig.colorbar(im, shrink=0.8); plt.tight_layout(); plt.show()
print(“nIf every off-diagonal cell were ~1.0 there would be no ensemble gain to ”
“harvest. The moderate correlations are why cons_all typically edges out ”
“the best single predictor — and why disagreement itself carries signal.”)
We score all ten predictors against the wet-lab label, on both ipSAE and self-consistency DockQ, with bootstrapped confidence intervals so we can see which differences are real. We then rank-normalize each column to percentiles and aggregate them, which keeps the comparison scale-free across metrics that live on different ranges and pile up differently at zero. The Spearman heatmap explains why the ensemble helps at all; if the predictors agreed perfectly there would be nothing left to harvest.
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def budget_curve(df, score_col, max_n=400):
d = df[[score_col, “y”]].dropna().sort_values(score_col, ascending=False)
hits = d.y.values.cumsum()
n = np.arange(1, len(d) + 1)
k = min(max_n, len(d))
return n[:k], (hits / n)[:k]
fig, ax = plt.subplots(figsize=(8, 4.4))
best_col = f”{best_single.metric}_{best_single.predictor}”
for col, lab, style in [(best_col, f”best single ({best_col})”, “-“),
(“cons_all”, “consensus (rank-avg, all)”, “-“),
(“cons_min”, “consensus (unanimity/min)”, “–“)]:
n, prec = budget_curve(ev, col)
ax.plot(n, prec, style, lw=1.8, label=lab)
ax.axhline(ev.y.mean(), ls=”:”, c=”crimson”, lw=1.4, label=f”random baseline ({ev.y.mean():.2f})”)
ax.set_xlabel(“designs ordered for wet-lab testing (N, best-first)”)
ax.set_ylabel(“hit rate among top N”); ax.set_title(“How much does in-silico triage buy you?”)
ax.legend(); plt.tight_layout(); plt.show()
print(“Enrichment at small budgets:”)
for N in [25, 50, 100, 200]:
line = f” N={N:4d} | random {ev.y.mean():.3f}”
for col, lab in [(best_col, “best-single”), (“cons_all”, “consensus”)]:
n, prec = budget_curve(ev, col, max_n=N)
line += f” | {lab} {prec[-1]:.3f} ({prec[-1] / ev.y.mean():.2f}x)”
print(line)
head(7, “VENDOR CONCORDANCE”)
both = ev[ev.adaptyv_binding.isin(CALLS) & ev.twist_binding.isin(CALLS)]
ct = pd.crosstab(both.adaptyv_binding, both.twist_binding)
print(f”Designs with calls from BOTH vendors: {len(both):,}n”)
print(ct.to_string())
if len(both) > 10:
kappa = cohen_kappa_score(both.adaptyv_binding, both.twist_binding)
agree = (both.adaptyv_binding == both.twist_binding).mean()
print(f”nRaw agreement: {agree:.3f} Cohen’s kappa: {kappa:.3f}”)
print(“Kappa well under 1.0 means part of the ‘unpredictable’ variance above ”
“is assay disagreement, not model failure.”)
kd = ev[[“adaptyv_kd_nM”, “twist_kd_nM”]].dropna()
kd = kd[(kd > 0).all(axis=1)]
if len(kd) > 10:
rho, pv = stats.spearmanr(kd.adaptyv_kd_nM, kd.twist_kd_nM)
fig, ax = plt.subplots(figsize=(4.8, 4.6))
ax.scatter(kd.adaptyv_kd_nM, kd.twist_kd_nM, s=16, alpha=0.6, c=”#4C72B0″, edgecolor=”none”)
lims = [min(kd.min()) * 0.5, max(kd.max()) * 2]
ax.plot(lims, lims, “k–“, lw=1)
ax.set_xscale(“log”); ax.set_yscale(“log”)
ax.set_xlabel(“Adaptyv KD (nM)”); ax.set_ylabel(“Twist KD (nM)”)
ax.set_title(f”Cross-vendor KD, n={len(kd)}, Spearman rho={rho:.2f}”)
plt.tight_layout(); plt.show()
med_ratio = np.median(kd.twist_kd_nM / kd.adaptyv_kd_nM)
print(f”Median KD ratio (Twist/Adaptyv): {med_ratio:.2f}x -> systematic format offset, ”
“so treat absolute KD across vendors as ordinal, not interchangeable.”)
We convert ranking performance into precision@N, because no lab orders 1,300 constructs and AUC quietly hides how a score behaves at the top of the list. The enrichment table then tells us what triage actually buys at budgets of 25, 50, 100, and 200. We follow it with Cohen’s κ and a log-log KD comparison between vendors, which sets the ceiling: label noise bounds how high any AUC above can honestly climb.
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if “twist_expression_mg_per_mL” in ev.columns:
g = ev.dropna(subset=[“twist_expression_mg_per_mL”])
a = g.loc[g.y == 1, “twist_expression_mg_per_mL”]
b = g.loc[g.y == 0, “twist_expression_mg_per_mL”]
if len(a) > 5 and len(b) > 5:
u, pv = stats.mannwhitneyu(a, b)
print(f”Titer (mg/mL) binders median {a.median():.2f} (n={len(a)}) | ”
f”non-binders {b.median():.2f} (n={len(b)}) Mann-Whitney p={pv:.2e}”)
r = auc_ci(g.y, g.twist_expression_mg_per_mL)
print(f”AUC of raw expression titer alone as a ‘binder’ predictor: {r[‘auc’]:.3f}”)
fig, axes = plt.subplots(1, 2, figsize=(9, 3.6))
axes[0].hist([b, a], bins=25, label=[“non-binder”, “binder”],
color=[“#BBBBBB”, “#4C72B0”], density=True)
axes[0].set_xlabel(“Twist titer (mg/mL)”); axes[0].set_ylabel(“density”); axes[0].legend()
axes[0].set_title(“Expression by outcome”)
if “adaptyv_expression” in ev.columns:
ex = ev.groupby(ev.adaptyv_expression.astype(str)).y.agg([“mean”, “size”])
ex = ex[ex[“size”] >= 10].sort_values(“mean”)
axes[1].barh(ex.index, ex[“mean”], color=”#DD8452″)
axes[1].set_xlabel(“hit rate”); axes[1].set_title(“Hit rate by Adaptyv expression class”)
plt.tight_layout(); plt.show()
print(“nTakeaway: if expression alone scores meaningfully above 0.5, then part of ”
“every AUC in section 4 is a solubility signal riding along. To isolate ”
“interface quality, re-run section 4 restricted to designs that expressed.”)
expressed = ev[ev.adaptyv_expression.astype(str).isin([“medium”, “high”])] if “adaptyv_expression” in ev.columns else ev
if len(expressed) > 100:
r_all = auc_ci(ev.y, ev.cons_all)
r_exp = auc_ci(expressed.y, expressed.cons_all)
print(f” consensus AUC, all evaluable : {r_all[‘auc’]:.3f} (n={r_all[‘n’]})”)
print(f” consensus AUC, expressed only : {r_exp[‘auc’]:.3f} (n={r_exp[‘n’]})”)
head(9, “EPITOPE CONVERGENCE”)
def parse_epitope(s):
if not isinstance(s, str) or not s.strip():
return frozenset()
out = set()
for tok in s.split(“;”):
tok = tok.strip()
if not tok:
continue
out.add(tok.split(“:”)[-1])
return frozenset(out)
ev[“epi”] = ev[“epitope_residues”].apply(parse_epitope)
def mean_pairwise_jaccard(sets, max_pairs=4000, seed=SEED):
sets = [s for s in sets if len(s) > 0]
if len(sets) < 2:
return np.nan
pairs = list(itertools.combinations(range(len(sets)), 2))
rng = np.random.default_rng(seed)
if len(pairs) > max_pairs:
pairs = [pairs[i] for i in rng.choice(len(pairs), max_pairs, replace=False)]
vals = []
for i, j in pairs:
u = len(sets[i] | sets[j])
vals.append(len(sets[i] & sets[j]) / u if u else 0.0)
return float(np.mean(vals))
rows = []
for tgt, g in ev.groupby(“target”):
B = g.loc[g.y == 1, “epi”].tolist()
N = g.loc[g.y == 0, “epi”].tolist()
if len(B) >= 3 and len(N) >= 3:
rows.append({“target”: tgt, “n_bind”: len(B), “n_non”: len(N),
“J_binders”: mean_pairwise_jaccard(B),
“J_nonbinders”: mean_pairwise_jaccard(N)})
epi = pd.DataFrame(rows)
if len(epi):
epi[“delta”] = epi.J_binders – epi.J_nonbinders
print(epi.sort_values(“delta”, ascending=False).to_string(
index=False, formatters={c: “{:.3f}”.format for c in [“J_binders”, “J_nonbinders”, “delta”]}))
w = stats.wilcoxon(epi.J_binders, epi.J_nonbinders) if len(epi) >= 6 else None
if w:
print(f”nPaired Wilcoxon across targets: p={w.pvalue:.4f} ”
f”(binders more epitope-convergent than failures?)”)
tgt = epi.sort_values(“n_bind”, ascending=False).target.iloc[0]
sub = ev[ev.target == tgt]
freq_b = pd.Series([r for s in sub[sub.y == 1].epi for r in s]).value_counts()
freq_n = pd.Series([r for s in sub[sub.y == 0].epi for r in s]).value_counts()
top = freq_b.head(18).index
fig, ax = plt.subplots(figsize=(9, 3.8))
xx = np.arange(len(top))
ax.bar(xx – 0.2, (freq_b.reindex(top).fillna(0) / max(1, (sub.y == 1).sum())), 0.4,
label=”binders”, color=”#4C72B0″)
ax.bar(xx + 0.2, (freq_n.reindex(top).fillna(0) / max(1, (sub.y == 0).sum())), 0.4,
label=”non-binders”, color=”#BBBBBB”)
ax.set_xticks(xx); ax.set_xticklabels(top, rotation=70, ha=”right”)
ax.set_ylabel(“fraction of designs contacting”); ax.set_title(f”Epitope usage on {tgt}”)
ax.legend(); plt.tight_layout(); plt.show()
We test whether expression titer alone discriminates binders, and if it does, we know part of every score from above is solubility riding along under another name. Re-running consensus on expressed-only designs isolates interface quality from biophysics. We then parse the epitope contact lists into residue sets and ask, per target and paired across targets, whether confirmed binders converge on a shared patch more than the failures do.
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AAS = “ACDEFGHIKLMNPQRSTVWY”
KD_HYDRO = dict(zip(AAS, [1.8, 2.5, -3.5, -3.5, 2.8, -0.4, -3.2, 4.5, -3.9, 3.8,
1.9, -3.5, -1.6, -3.5, -4.5, -0.8, -0.7, 4.2, -0.9, -1.3]))
CHARGE = {“K”: 1, “R”: 1, “H”: 0.1, “D”: -1, “E”: -1}
def seq_features(seq):
seq = “”.join(ch for ch in str(seq).upper() if ch in AAS)
L = max(1, len(seq))
counts = {a: seq.count(a) / L for a in AAS}
f = {f”aa_{a}”: counts[a] for a in AAS}
f[“length”] = len(seq)
f[“net_charge”] = sum(CHARGE.get(c, 0) for c in seq)
f[“charge_density”] = f[“net_charge”] / L
f[“gravy”] = float(np.mean([KD_HYDRO[c] for c in seq])) if seq else 0.0
f[“aromatic”] = sum(counts[a] for a in “FWY”)
f[“helix_prone”] = sum(counts[a] for a in “AELM”)
f[“beta_prone”] = sum(counts[a] for a in “VIYFT”)
f[“gly_pro”] = counts[“G”] + counts[“P”]
p = np.array([counts[a] for a in AAS]); p = p[p > 0]
f[“entropy”] = float(-(p * np.log2(p)).sum())
run, best = 0, 0
for c in seq:
run = run + 1 if KD_HYDRO[c] > 1.5 else 0
best = max(best, run)
f[“max_hydrophobic_run”] = best
return f
SF = pd.DataFrame([seq_features(s) for s in ev.sequence], index=ev.index)
seq_cols = list(SF.columns)
sil_cols = [c for c in ev.columns if c.startswith((“ipsae_min_”, “sc_dockq_”))] +
[“cons_all”, “cons_min”, “cons_disagree”]
meta_cols = [c for c in [“rank”, “n_optimization_rounds”, “epitope_n_residues”] if c in ev.columns]
X_all = pd.concat([ev[sil_cols + meta_cols], SF], axis=1)
y = ev.y.values
groups = ev.target.values
FEATURE_SETS = {
“in-silico only”: sil_cols + meta_cols,
“sequence only”: seq_cols,
“in-silico + sequence”: sil_cols + meta_cols + seq_cols,
}
def cv_auc(X, y, splitter, groups=None):
aucs = []
it = splitter.split(X, y, groups) if groups is not None else splitter.split(X, y)
for tr, te in it:
if len(np.unique(y[te])) < 2:
continue
m = HistGradientBoostingClassifier(max_depth=4, max_iter=250,
learning_rate=0.06, random_state=SEED)
m.fit(X.iloc[tr], y[tr])
aucs.append(roc_auc_score(y[te], m.predict_proba(X.iloc[te])[:, 1]))
return float(np.mean(aucs)), float(np.std(aucs)), len(aucs)
print(f”{‘feature set’:24s} {‘random 5-fold’:>18s} {‘grouped-by-target’:>20s}”)
print(“-” * 66)
results = {}
for name, cols in FEATURE_SETS.items():
X = X_all[cols]
r_mean, r_sd, _ = cv_auc(X, y, StratifiedKFold(5, shuffle=True, random_state=SEED))
g_mean, g_sd, nf = cv_auc(X, y, GroupKFold(n_splits=5), groups=groups)
results[name] = (r_mean, g_mean)
print(f”{name:24s} {r_mean:.3f} +/- {r_sd:.3f} {g_mean:.3f} +/- {g_sd:.3f}”)
gap = results[“in-silico + sequence”][0] – results[“in-silico + sequence”][1]
print(f”nRandom-CV minus grouped-CV for the full feature set: {gap:+.3f}”)
print(“That gap is leakage: features that encode target identity (epitope size, ”
“length priors, generator habits) let a randomly-split model recover the ”
“per-target base rate instead of learning what makes a binder. Report the ”
“grouped number; the random one is what a target-blind reviewer will catch.”)
Xt = pd.get_dummies(pd.Series(groups, index=ev.index), prefix=”tgt”)
r_mean, _, _ = cv_auc(Xt, y, StratifiedKFold(5, shuffle=True, random_state=SEED))
print(f”nControl – target one-hot ONLY, random CV: AUC={r_mean:.3f} ”
“(pure base-rate memorisation, zero design signal).”)
gkf = GroupKFold(n_splits=5)
tr, te = next(iter(gkf.split(X_all, y, groups)))
model = HistGradientBoostingClassifier(max_depth=4, max_iter=250,
learning_rate=0.06, random_state=SEED).fit(
X_all[FEATURE_SETS[“in-silico + sequence”]].iloc[tr], y[tr])
imp = permutation_importance(model, X_all[FEATURE_SETS[“in-silico + sequence”]].iloc[te],
y[te], n_repeats=12, random_state=SEED, scoring=”roc_auc”)
order = np.argsort(imp.importances_mean)[-18:]
names = np.array(FEATURE_SETS[“in-silico + sequence”])[order]
fig, ax = plt.subplots(figsize=(7, 5))
ax.barh(names, imp.importances_mean[order],
xerr=imp.importances_std[order], color=”#55A868″)
ax.set_xlabel(“drop in AUC when permuted”)
ax.set_title(“Permutation importance (held-out target block)”)
plt.tight_layout(); plt.show()
head(“”, “SUMMARY”)
print(f”””
Evaluable designs : {len(ev):,} base hit rate {ev.y.mean():.3f}
Best single in-silico : {best_col} AUC {best_single.auc:.3f}
Rank-average consensus : AUC {auc_ci(ev.y, ev.cons_all)[‘auc’]:.3f}
Honest ML (grouped CV) : AUC {results[‘in-silico + sequence’][1]:.3f} <- the one to report
Same model, random CV : AUC {results[‘in-silico + sequence’][0]:.3f} (gap = {gap:+.3f} leakage)
Five things this dataset teaches that a design paper usually cannot:
1. Target identity dominates every other factor; always stratify.
2. Structure-predictor confidence is real but weak signal (AUC ~0.6-0.75),
nowhere near a standalone go/no-go filter.
3. Ensembling across predictors is a cheap, reliable few-points-of-AUC win.
4. Cross-vendor label noise caps how high any AUC here can honestly go.
5. Expression failure masquerades as binding failure. Condition on it.
Extensions worth trying:
– load_table(‘insilico_cofold_predictions’) for all 5 seeds/predictor, and
test whether seed VARIANCE beats seed-best as a confidence signal
– load_table(‘adaptyv_fit_curves’) to refit kinetics yourself and flag
designs whose reported KD rests on a poorly-conditioned fit
– load_table(‘insilico_provenance_steps’) to relate optimisation-round count
to eventual success
– snapshot_download(…, allow_patterns=’data/designs/EGFR/<name>/*’) for
mmCIF structures + PAE matrices on a single design
“””)
We featurize sequences by composition, charge, hydropathy, entropy, and hydrophobic run length, then fit gradient boosting under two schemes: random folds and target-grouped folds. The gap between them is the leakage, since designs nest inside targets with very different base rates and a random split lets the model memorize which antigens are easy. The one-hot control makes that explicit, and permutation importance on a held-out target block shows what survives when we remove the shortcut.
In conclusion, in-silico scoring was helpful, but it did not tell the whole story. The target strongly influenced the results, so comparing models without accounting for it could easily give us a misleading picture. The structure predictors showed useful signals, and combining them gave a modest improvement, but they were still not reliable enough to use on their own. We also found that differences between experiments and protein expression could make a design look like a binding failure even when the real issue was poor expression. Overall, we learned that careful evaluation mattered more than chasing impressive individual metrics. By grouping our cross-validation by target, we got a more realistic view of how well the models could generalize to new targets.
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