From In-Silico to Wet-Lab: Evaluating AI Protein Design Performance

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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 …

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