Initial commit

.gitignore

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+.venv/
+__pycache__/
+*.py[cod]
+
+# Local ChEMBL download (about 738 MB); obtain it separately before running.
+chembl_*.txt
+
+# Files generated by molmesh.py
+*_similarities.csv
+test_results.csv
+test_structures/

README.md

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+# MolMesh
+
+Минимальный локальный поиск молекул ChEMBL, похожих на гефитиниб (`CHEMBL939`).
+
+Скрипт находит `CHEMBL939` в TSV и использует его `canonical_smiles` как эталон. Затем во втором потоковом проходе по файлу строит Morgan fingerprints (`radius=2`, `fpSize=2048`) и ранжирует остальные валидные SMILES по Tanimoto similarity. Невалидные SMILES и сам `CHEMBL939` пропускаются. В памяти остаются только 20 лучших результатов (или значение `--top`).
+
+## Установка
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+RDKit также можно установить через conda-forge:
+
+```bash
+conda install -c conda-forge rdkit
+```
+
+## Запуск
+
+```bash
+python molmesh.py chembl_37_chemreps.txt -o gefitinib_similarities.csv
+```
+
+По умолчанию создаётся CSV с колонками `rank,chembl_id,canonical_smiles,similarity` и выводится тот же top-20 в терминал. Для другого размера выборки:
+
+```bash
+python molmesh.py chembl_37_chemreps.txt --top 50 -o top_50.csv
+```
+
+Во время поиска статус выводится в `stderr` каждые 100 000 строк: количество обработанных строк, текущая и средняя скорость, прошедшее время и число пропущенных невалидных SMILES. Период можно изменить или отключить:
+
+```bash
+python molmesh.py chembl_37_chemreps.txt --progress-every 500000
+python molmesh.py chembl_37_chemreps.txt --progress-every 0
+```
+
+## Быстрый тест на части базы
+
+Опция `--max-rows` ограничивает второй проход первыми `N` строками TSV. Сам `CHEMBL939` перед этим всё равно находится в отдельном потоковом проходе, поэтому эталон остаётся тем же. Получившийся CSV — это top‑20 только по обработанной части, а не по полной базе.
+
+```bash
+python molmesh.py chembl_37_chemreps.txt --max-rows 10000 -o test_results.csv
+```
+
+## Изображения структур
+
+Передайте каталог в `--images-dir`, чтобы создать `CHEMBL939_gefitinib.png` с гефитинибом и `top_candidates.png` с сеткой top‑кандидатов. На изображениях кандидатов указаны ранг, ChEMBL ID и Tanimoto similarity.
+
+```bash
+python molmesh.py chembl_37_chemreps.txt --images-dir structures
+```
+
+Опция `--image-columns` задаёт число структур в строке сетки (по умолчанию `4`).

molmesh.py

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+#!/usr/bin/env python3
+"""Потоковый поиск молекул ChEMBL, похожих на гефитиниб (CHEMBL939)."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import heapq
+import sys
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterator
+
+from rdkit import Chem, DataStructs, RDLogger
+from rdkit.Chem import Draw, rdFingerprintGenerator
+
+
+REFERENCE_CHEMBL_ID = "CHEMBL939"
+FP_RADIUS = 2
+FP_SIZE = 2048
+
+# Invalid records are intentionally skipped, so do not emit one RDKit error per row.
+RDLogger.DisableLog("rdApp.error")
+
+
+@dataclass
+class SearchStats:
+    """Counters collected while scanning the candidate records."""
+
+    scanned: int = 0
+    valid: int = 0
+    invalid: int = 0
+    stopped_early: bool = False
+
+
+def rows(tsv_path: Path) -> Iterator[dict[str, str]]:
+    """Read ChEMBL records one at a time without loading the file into memory."""
+    with tsv_path.open("r", encoding="utf-8", newline="") as source:
+        yield from csv.DictReader(source, delimiter="\t")
+
+
+def molecule(smiles: str | None) -> Chem.Mol | None:
+    """Return a parsed molecule, or None when a SMILES is empty or invalid."""
+    if not smiles:
+        return None
+    return Chem.MolFromSmiles(smiles)
+
+
+def find_reference_smiles(tsv_path: Path) -> str:
+    """Locate CHEMBL939 and return its canonical_smiles."""
+    for row in rows(tsv_path):
+        if row.get("chembl_id") == REFERENCE_CHEMBL_ID:
+            smiles = row.get("canonical_smiles", "")
+            if molecule(smiles) is None:
+                raise ValueError(f"{REFERENCE_CHEMBL_ID} has an invalid canonical_smiles")
+            return smiles
+    raise ValueError(f"{REFERENCE_CHEMBL_ID} was not found in {tsv_path}")
+
+
+def find_similar(
+    tsv_path: Path,
+    reference_smiles: str,
+    limit: int,
+    progress_every: int,
+    max_rows: int | None,
+) -> tuple[list[tuple[float, str, str]], SearchStats]:
+    """Calculate top similarities, retaining only ``limit`` rows in memory."""
+    generator = rdFingerprintGenerator.GetMorganGenerator(
+        radius=FP_RADIUS, fpSize=FP_SIZE
+    )
+    reference_fp = generator.GetFingerprint(molecule(reference_smiles))
+    best: list[tuple[float, str, str]] = []
+    stats = SearchStats()
+    started_at = time.perf_counter()
+    last_report_at = started_at
+    last_report_rows = 0
+
+    print("Searching candidates...", file=sys.stderr)
+
+    for row in rows(tsv_path):
+        if max_rows is not None and stats.scanned >= max_rows:
+            stats.stopped_early = True
+            break
+        stats.scanned += 1
+        chembl_id = row.get("chembl_id", "")
+        smiles = row.get("canonical_smiles", "")
+        if chembl_id != REFERENCE_CHEMBL_ID:
+            mol = molecule(smiles)
+            if mol is None:
+                stats.invalid += 1
+            else:
+                stats.valid += 1
+                similarity = DataStructs.TanimotoSimilarity(
+                    reference_fp, generator.GetFingerprint(mol)
+                )
+                candidate = (similarity, chembl_id, smiles)
+                if len(best) < limit:
+                    heapq.heappush(best, candidate)
+                elif candidate > best[0]:
+                    heapq.heapreplace(best, candidate)
+
+        if progress_every and stats.scanned % progress_every == 0:
+            now = time.perf_counter()
+            interval_seconds = now - last_report_at
+            total_seconds = now - started_at
+            interval_rows = stats.scanned - last_report_rows
+            current_rate = interval_rows / interval_seconds if interval_seconds else 0.0
+            average_rate = stats.scanned / total_seconds if total_seconds else 0.0
+            print(
+                "Processed "
+                f"{stats.scanned:,} rows | {current_rate:,.0f} rows/s current | "
+                f"{average_rate:,.0f} rows/s average | {total_seconds:.1f}s elapsed | "
+                f"{stats.valid:,} valid | {stats.invalid:,} invalid | top {len(best)}",
+                file=sys.stderr,
+            )
+            last_report_at = now
+            last_report_rows = stats.scanned
+
+    return sorted(best, reverse=True), stats
+
+
+def write_results(output_path: Path, matches: list[tuple[float, str, str]]) -> None:
+    """Write ranked matching records as CSV."""
+    with output_path.open("w", encoding="utf-8", newline="") as destination:
+        writer = csv.DictWriter(
+            destination,
+            fieldnames=["rank", "chembl_id", "canonical_smiles", "similarity"],
+        )
+        writer.writeheader()
+        for rank, (similarity, chembl_id, smiles) in enumerate(matches, start=1):
+            writer.writerow(
+                {
+                    "rank": rank,
+                    "chembl_id": chembl_id,
+                    "canonical_smiles": smiles,
+                    "similarity": f"{similarity:.6f}",
+                }
+            )
+
+
+def write_images(
+    output_dir: Path,
+    reference_smiles: str,
+    matches: list[tuple[float, str, str]],
+    columns: int,
+) -> tuple[Path, Path]:
+    """Create PNG depictions for the reference molecule and ranked matches."""
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    reference_path = output_dir / "CHEMBL939_gefitinib.png"
+    reference_image = Draw.MolToImage(
+        molecule(reference_smiles), size=(600, 400), legend="CHEMBL939 (gefitinib)"
+    )
+    reference_image.save(reference_path)
+
+    candidate_path = output_dir / "top_candidates.png"
+    candidate_molecules = [molecule(smiles) for _, _, smiles in matches]
+    legends = [
+        f"#{rank} {chembl_id}\nTanimoto: {similarity:.4f}"
+        for rank, (similarity, chembl_id, _) in enumerate(matches, start=1)
+    ]
+    candidate_image = Draw.MolsToGridImage(
+        candidate_molecules,
+        molsPerRow=columns,
+        subImgSize=(350, 250),
+        legends=legends,
+    )
+    candidate_image.save(candidate_path)
+    return reference_path, candidate_path
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Find ChEMBL molecules similar to gefitinib (CHEMBL939)."
+    )
+    parser.add_argument("input", type=Path, help="Path to ChEMBL TSV file")
+    parser.add_argument(
+        "-o",
+        "--output",
+        type=Path,
+        default=Path("gefitinib_similarities.csv"),
+        help="Output CSV path (default: gefitinib_similarities.csv)",
+    )
+    parser.add_argument(
+        "--top",
+        type=int,
+        default=20,
+        help="Number of matches to retain (default: 20)",
+    )
+    parser.add_argument(
+        "--progress-every",
+        type=int,
+        default=100_000,
+        help="Print progress after this many rows; 0 disables it (default: 100000)",
+    )
+    parser.add_argument(
+        "--max-rows",
+        type=int,
+        help="For testing, scan only the first N TSV rows after locating CHEMBL939",
+    )
+    parser.add_argument(
+        "--images-dir",
+        type=Path,
+        help="Create reference and top-candidate PNG images in this directory",
+    )
+    parser.add_argument(
+        "--image-columns",
+        type=int,
+        default=4,
+        help="Number of molecules per row in the candidate image (default: 4)",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    if args.top < 1:
+        print("--top must be a positive integer", file=sys.stderr)
+        return 2
+    if args.progress_every < 0:
+        print("--progress-every cannot be negative", file=sys.stderr)
+        return 2
+    if args.max_rows is not None and args.max_rows < 1:
+        print("--max-rows must be a positive integer", file=sys.stderr)
+        return 2
+    if args.image_columns < 1:
+        print("--image-columns must be a positive integer", file=sys.stderr)
+        return 2
+    if not args.input.is_file():
+        print(f"Input file not found: {args.input}", file=sys.stderr)
+        return 2
+
+    try:
+        reference_smiles = find_reference_smiles(args.input)
+        matches, stats = find_similar(
+            args.input,
+            reference_smiles,
+            args.top,
+            args.progress_every,
+            args.max_rows,
+        )
+    except ValueError as error:
+        print(f"Error: {error}", file=sys.stderr)
+        return 1
+
+    write_results(args.output, matches)
+    image_paths: tuple[Path, Path] | None = None
+    if args.images_dir:
+        image_paths = write_images(
+            args.images_dir, reference_smiles, matches, args.image_columns
+        )
+    print(f"Reference {REFERENCE_CHEMBL_ID}: {reference_smiles}")
+    print(
+        f"Scanned {stats.scanned:,} rows: {stats.valid:,} valid, "
+        f"{stats.invalid:,} invalid SMILES."
+    )
+    if stats.stopped_early:
+        print("Stopped early because of --max-rows; results cover only that subset.")
+    print(f"Saved {len(matches)} matches to {args.output}")
+    if image_paths:
+        print(f"Saved reference image to {image_paths[0]}")
+        print(f"Saved candidate image to {image_paths[1]}")
+    for rank, (similarity, chembl_id, smiles) in enumerate(matches, start=1):
+        print(f"{rank:>2}. {chembl_id}\t{similarity:.6f}\t{smiles}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

requirements.txt

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+rdkit>=2024.3