screen script

scripts/admet_screen.py

@@ -0,0 +1,105 @@
+import pandas as pd
+import numpy as np
+import os, sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# === 配置 ===
+csv_path = "data/trpe_preds.csv"
+props_with_thresholds = {
+    "AMES": 0.5,
+    "Carcinogens_Lagunin": 0.5,
+    "ClinTox": 0.5,
+    "DILI": 0.5,
+    "hERG": 0.5,
+    "Bioavailability_Ma": 0.5,
+    "HIA_Hou": 0.5,
+}
+
+# 判定符号：含等号可切换成 <= / >=
+toxic_op_le = "<"   # 或 "<="
+adme_op_ge  = ">"   # 或 ">="
+
+df = pd.read_csv(csv_path)
+total = len(df)
+
+toxic_cols = ["AMES","Carcinogens_Lagunin","ClinTox","DILI","hERG"]
+adme_cols  = ["Bioavailability_Ma","HIA_Hou"]
+
+# 统一转数值（非数值转为 NaN）
+for col in toxic_cols + adme_cols:
+    if col in df.columns:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+
+results = []
+os.makedirs("failed_by_rule", exist_ok=True)
+
+def apply_rule(series, thr, op):
+    if op == "<":  return series < thr
+    if op == "<=": return series <= thr
+    if op == ">":  return series > thr
+    if op == ">=": return series >= thr
+    raise ValueError("Unsupported op")
+
+for col, thr in props_with_thresholds.items():
+    if col not in df.columns:
+        continue
+
+    # 打印该属性的范围，方便 sanity check
+    col_min = float(np.nanmin(df[col].values)) if df[col].notna().any() else np.nan
+    col_max = float(np.nanmax(df[col].values)) if df[col].notna().any() else np.nan
+
+    if col in toxic_cols:
+        mask = apply_rule(df[col], thr, toxic_op_le)
+        rule = f"{col} {toxic_op_le} {thr}"
+    else:
+        mask = apply_rule(df[col], thr, adme_op_ge)
+        rule = f"{col} {adme_op_ge} {thr}"
+
+    passed = int(mask.sum())
+    failed = int(total - passed)
+
+    # 导出未通过的分子（含该列原值）
+    failed_df = df.loc[~mask, [col]].copy()
+    failed_df.to_csv(f"failed_by_rule/{col}_failed.csv", index=False)
+
+    results.append({
+        "属性": col,
+        "阈值": thr,
+        "规则": rule,
+        "最小值": col_min,
+        "最大值": col_max,
+        "通过数": passed,
+        "淘汰数": failed,
+        "是否全部淘汰": failed == total
+    })
+
+res_df = pd.DataFrame(results).sort_values("淘汰数", ascending=False)
+print(res_df)
+res_df.to_csv("trpe_filter_diagnostics.csv", index=False)
+
+# === 最终整体过滤（交集） ===
+mask_all = pd.Series(True, index=df.index)
+
+for col, thr in props_with_thresholds.items():
+    if col not in df.columns:
+        continue
+    if col in toxic_cols:
+        mask_all &= df[col] < thr   # 毒理类必须 < 阈值
+    else:
+        mask_all &= df[col] > thr   # 吸收类必须 > 阈值
+
+final_candidates = df[mask_all].copy()
+
+print("\n=== 最终筛选结果 ===")
+print(f"总数: {total}")
+print(f"符合所有标准的分子数: {len(final_candidates)}")
+
+if len(final_candidates) > 0:
+    print("符合条件的分子索引（行号）:")
+    print(final_candidates.index.tolist())
+    final_candidates.to_csv("trpe_final_candidates.csv", index=False)
+
+    print("✅ 已保存到 trpe_final_candidates.csv")
+else:
+    print("⚠️ 没有分子同时满足所有条件。")

scripts/admet_screen2.py

@@ -0,0 +1,105 @@
+import pandas as pd
+import numpy as np
+import os, sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# === 配置 ===
+csv_path = "data/trpe_preds.csv"
+props_with_thresholds = {
+    "AMES": 0.5,
+    "Carcinogens_Lagunin": 0.5,
+    "ClinTox": 0.5,
+    "DILI": 0.5,
+    "hERG": 0.5,
+    "Bioavailability_Ma": 0,
+    "HIA_Hou": 0,
+}
+
+# 判定符号：含等号可切换成 <= / >=
+toxic_op_le = "<"   # 或 "<="
+adme_op_ge  = ">"   # 或 ">="
+
+df = pd.read_csv(csv_path)
+total = len(df)
+
+toxic_cols = ["AMES","Carcinogens_Lagunin","ClinTox","DILI","hERG"]
+adme_cols  = ["Bioavailability_Ma","HIA_Hou"]
+
+# 统一转数值（非数值转为 NaN）
+for col in toxic_cols + adme_cols:
+    if col in df.columns:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+
+results = []
+os.makedirs("failed_by_rule", exist_ok=True)
+
+def apply_rule(series, thr, op):
+    if op == "<":  return series < thr
+    if op == "<=": return series <= thr
+    if op == ">":  return series > thr
+    if op == ">=": return series >= thr
+    raise ValueError("Unsupported op")
+
+for col, thr in props_with_thresholds.items():
+    if col not in df.columns:
+        continue
+
+    # 打印该属性的范围，方便 sanity check
+    col_min = float(np.nanmin(df[col].values)) if df[col].notna().any() else np.nan
+    col_max = float(np.nanmax(df[col].values)) if df[col].notna().any() else np.nan
+
+    if col in toxic_cols:
+        mask = apply_rule(df[col], thr, toxic_op_le)
+        rule = f"{col} {toxic_op_le} {thr}"
+    else:
+        mask = apply_rule(df[col], thr, adme_op_ge)
+        rule = f"{col} {adme_op_ge} {thr}"
+
+    passed = int(mask.sum())
+    failed = int(total - passed)
+
+    # 导出未通过的分子（含该列原值）
+    failed_df = df.loc[~mask, [col]].copy()
+    failed_df.to_csv(f"failed_by_rule/{col}_failed.csv", index=False)
+
+    results.append({
+        "属性": col,
+        "阈值": thr,
+        "规则": rule,
+        "最小值": col_min,
+        "最大值": col_max,
+        "通过数": passed,
+        "淘汰数": failed,
+        "是否全部淘汰": failed == total
+    })
+
+res_df = pd.DataFrame(results).sort_values("淘汰数", ascending=False)
+print(res_df)
+res_df.to_csv("trpe_filter_diagnostics.csv", index=False)
+
+# === 最终整体过滤（交集） ===
+mask_all = pd.Series(True, index=df.index)
+
+for col, thr in props_with_thresholds.items():
+    if col not in df.columns:
+        continue
+    if col in toxic_cols:
+        mask_all &= df[col] < thr   # 毒理类必须 < 阈值
+    else:
+        mask_all &= df[col] > thr   # 吸收类必须 > 阈值
+
+final_candidates = df[mask_all].copy()
+
+print("\n=== 最终筛选结果 ===")
+print(f"总数: {total}")
+print(f"符合所有标准的分子数: {len(final_candidates)}")
+
+if len(final_candidates) > 0:
+    print("符合条件的分子索引（行号）:")
+    print(final_candidates.index.tolist())
+    final_candidates.to_csv("trpe_final_candidates.csv", index=False)
+
+    print("✅ 已保存到 trpe_final_candidates.csv")
+else:
+    print("⚠️ 没有分子同时满足所有条件。")