| |
| """Reconstruct native MolAS evaluation labels from unchanged raw tables. |
| |
| This is deterministic data preparation, not docking or model training. The |
| output is explicitly reconstructed; it is not the missing original server CSV. |
| Acquisition uses a different, continuous target and is never overwritten here. |
| """ |
| import argparse |
| import csv |
| import hashlib |
| import math |
| from pathlib import Path |
|
|
| ALGORITHMS = ['surfdock', 'unimol', 'diffdock', 'diffdock_l', 'gnina', 'autodock', 'smina'] |
|
|
|
|
| def native_score(rmsd, pb_ratio): |
| if rmsd > 5 or rmsd < 0: |
| return 0.0 |
| return (1 + math.exp(-6)) / (1 + math.exp(3 * (rmsd - 2))) * (pb_ratio == 1.0) |
|
|
|
|
| def read(path): |
| with path.open(newline='') as handle: |
| return list(csv.DictReader(handle)) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument('--root', type=Path, default=Path(__file__).resolve().parents[1]) |
| parser.add_argument('--output', type=Path, help='Explicit reconstructed target destination') |
| parser.add_argument('--check-only', action='store_true', help='Compare with the existing reconstructed file without writing') |
| args = parser.parse_args() |
| root = args.root.resolve() |
| output = args.output or root / 'data/targets/moad_score_native_reconstructed.csv' |
| protected = [root / f'data/raw/moad_{name}_trimmed.csv' for name in ['rmsd', 'pb_ratio', 'runtime']] |
| protected += [root / 'data/targets/strict_score_matrix.csv', root / 'data/targets/full_strict_score_matrix.csv'] |
| if output.resolve() in {path.resolve() for path in protected}: |
| parser.error('Refusing to overwrite raw data or an original acquisition target') |
| rmsd = read(root / 'data/raw/moad_rmsd_trimmed.csv') |
| pb = read(root / 'data/raw/moad_pb_ratio_trimmed.csv') |
| ids = [row['PDB_CCD_ID'] for row in rmsd] |
| assert len(ids) == len(set(ids)) == 3179 |
| assert ids == [row['PDB_CCD_ID'] for row in pb] |
| records = [[identifier] + [native_score(float(r[a + '_rmsd']), float(p[a + '_pb_ratio'])) |
| for a in ALGORITHMS] |
| for identifier, r, p in zip(ids, rmsd, pb)] |
| if args.check_only: |
| existing = read(output) |
| assert [row['PDB_CCD_ID'] for row in existing] == ids |
| largest = max(abs(float(row[a]) - record[j + 1]) |
| for row, record in zip(existing, records) for j, a in enumerate(ALGORITHMS)) |
| assert largest <= 1e-12, f'Native target mismatch: {largest}' |
| print(f'Checked 3179 x 7 native labels; maximum absolute formula difference: {largest}') |
| else: |
| output.parent.mkdir(parents=True, exist_ok=True) |
| with output.open('w', newline='') as handle: |
| writer = csv.writer(handle) |
| writer.writerow(['PDB_CCD_ID'] + ALGORITHMS) |
| writer.writerows([row[0]] + [repr(x) for x in row[1:]] for row in records) |
| print(f'Wrote explicitly reconstructed native evaluation target: {output}') |
| print(f'SHA256: {hashlib.sha256(output.read_bytes()).hexdigest()}') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
