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Commands to run (Spec-RAG retrieval + sanity check + oracle)

Paths below use your env: Spec-RAG, SpecBridge, De-SpecBridge, MassSpecGym_test.mgf, and out/retrieval/.

Improving retrieval performance (candidates_A / candidates_B)

  • --ef-search 512 (default): Higher FAISS HNSW ef_search at query time improves ANN recall. Increase to 1024 if you need better recall and can afford slower search.
  • --formula-filter: When MGF has FORMULA/formula, restricts candidates to same-formula molecules (over-fetches from FAISS then reranks). Use this so Recall/Tanimoto improve when the library contains same-formula molecules.
  • --chemberta-model: Must match the model used to build the library (build_library.py --chemberta-model). Default Derify/ChemBERTa_augmented_pubchem_13m. If your SpecBridge checkpoint was trained with a different ChemBERTa, set this and rebuild the library with that model.
  • Formula over-fetch: --formula-min-fetch, --formula-max-fetch, --formula-fetch-multiplier control how many vectors are fetched when using --formula-filter; higher values can improve recall when formula buckets are large.
  • Rebuild FAISS with higher quality: python scripts/build_faiss.py --library-dir ... --ef-search 256 (default is now 256) before retrieving.

1) Build library (once) 

cd /cluster/tufts/liulab/yiwan01/Spec-RAG

python scripts/build_library.py \
  --smiles-path /cluster/tufts/liulab/yiwan01/De-SpecBridge/data/pubchem_clean.smi \
  --out-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge

2) Build FAISS indices 

python scripts/build_faiss.py \
  --library-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library

3) Train mappers (only if using Variant A without --smited-mapper-ckpt) 

python scripts/train_mapper.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_train.mgf \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --out-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/mappers \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge

4) Retrieve (Variants A, B, C)

Variant A (SMI-TED only; pretrained mapper):

python scripts/retrieve_generate.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --library-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --smited-mapper-ckpt /cluster/tufts/liulab/yiwan01/De-SpecBridge/runs/smited_mapper_final/mapper_best.pt \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --variant A \
  --K 100 \
  --ef-search 512 \
  --formula-filter \
  --out-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_A.jsonl

Variant B (ChemBERTa retrieval; no mapper-dir needed):

python scripts/retrieve_generate.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --library-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --variant B \
  --K 100 \
  --ef-search 512 \
  --formula-filter \
  --chemberta-model Derify/ChemBERTa_augmented_pubchem_13m \
  --out-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B.jsonl

Variant B formula-keyed candidate-pool mode (direct exact rerank inside the provided candidate pool; no dependency on library overlap, and no FAISS needed unless a query falls back):

python scripts/retrieve_generate.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --library-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --variant B \
  --K 100 \
  --formula-filter \
  --candidate-json /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_retrieval_candidates_formula.json \
  --candidate-key-field formula \
  --out-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B_benchmark.jsonl

--candidate-key-field smiles_gt is still supported, but it is oracle-only benchmarking because it selects the candidate pool using the ground-truth molecule.

Variant C:

python scripts/retrieve_generate.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --library-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --variant C \
  --K 100 \
  --out-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_C.jsonl

5) Evaluate spectrum-based retrieval (Recall + Tanimoto) 

python scripts/evaluate_massspecgym.py \
  --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_A.jsonl \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_A.json \
  --tanimoto

python scripts/evaluate_massspecgym.py \
  --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B.jsonl \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_B.json \
  --tanimoto

python scripts/evaluate_massspecgym.py \
  --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_C.jsonl \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_C.json \
  --tanimoto

6) Self-retrieval sanity check (Priority 2) — true mol embeddings

Index = test molecules only; query = same true molecule embeddings. Expected Recall@1 ≈ 1.0 for both.

  • If either fails → that embedding/index pipeline is broken.
  • If ChemBERTa passes and SMI-TED fails → SMI-TED embedding issue.
  • If both pass → issue is likely library coverage / eval mismatch.
python scripts/self_retrieval_sanity_check.py \
  --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B.jsonl \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/self_retrieval_report.json

(You can use any of candidates_A.jsonl, candidates_B.jsonl, or candidates_C.jsonl; they share the same smiles_gt per row.)

6b) Mapped-embedding self-retrieval sanity check

Index = spectrum→mapper embeddings of test set (same MGF); query = same mapped embeddings. Expected Recall@1 ≈ 1.0.

  • Tests spectrum→ChemBERTa-mapped (SpecBridge) and spectrum→SMI-TED-mapped (DreamsToSmiTed or M_smi).
  • If either fails → spectrum→mapped-embedding pipeline is broken.
# With De-SpecBridge SMI-TED mapper (recommended)
python scripts/self_retrieval_mapped_sanity_check.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --smited-mapper-ckpt /cluster/tufts/liulab/yiwan01/De-SpecBridge/runs/smited_mapper_final/mapper_best.pt \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/self_retrieval_mapped_report.json

If you use Spec-RAG mappers instead of the pretrained SMI-TED mapper:

python scripts/self_retrieval_mapped_sanity_check.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --mapper-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/mappers \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/self_retrieval_mapped_report.json

6c) True-index + Mapped-query sanity check

Index = true molecule embeddings (ChemBERTa/SMI-TED of test SMILES). Query = mapped embeddings (spectrum → mapper). So we build a tiny index from the test set’s true mol embeddings, then query with the spectrum→mapper embeddings. This tests how well the mapper aligns spectrum to molecule space (Recall@1/10/50).

python scripts/sanity_check_true_index_mapped_query.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --smited-mapper-ckpt /cluster/tufts/liulab/yiwan01/De-SpecBridge/runs/smited_mapper_final/mapper_best.pt \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/true_index_mapped_query_report.json

7) Oracle retrieval (true mol embedding vs library) 

python scripts/evaluate_oracle_retrieval.py \
  --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B.jsonl \
  --library-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --K 100 \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_oracle.json

Minimal “all commands” copy-paste (after library + FAISS exist) 

cd /cluster/tufts/liulab/yiwan01/Spec-RAG

# Self-retrieval sanity check — true mol embeddings (expect Recall@1 ≈ 1.0)
python scripts/self_retrieval_sanity_check.py \
  --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B.jsonl \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/self_retrieval_report.json

# Mapped-embedding self-retrieval (spectrum→mapper; expect Recall@1 ≈ 1.0)
python scripts/self_retrieval_mapped_sanity_check.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --smited-mapper-ckpt /cluster/tufts/liulab/yiwan01/De-SpecBridge/runs/smited_mapper_final/mapper_best.pt \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/self_retrieval_mapped_report.json

# True-index + Mapped-query (index = true mol emb, query = spectrum→mapper)
python scripts/sanity_check_true_index_mapped_query.py \
  --mgf-path /cluster/tufts/liulab/yiwan01/SpecBridge/data/MassSpecGym_test.mgf \
  --specbridge-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/runs/specbridge_align_chemberta_pub_v3g_msgym_mapper_spec/ckpt_001200.pt \
  --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
  --smited-mapper-ckpt /cluster/tufts/liulab/yiwan01/De-SpecBridge/runs/smited_mapper_final/mapper_best.pt \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/true_index_mapped_query_report.json

# Oracle retrieval (true mol embedding vs library)
python scripts/evaluate_oracle_retrieval.py \
  --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B.jsonl \
  --library-dir /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/library \
  --despecbridge-path /cluster/tufts/liulab/yiwan01/De-SpecBridge \
  --K 100 \
  --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_oracle.json

# Spectrum-based eval (if you have candidates_*.jsonl)
python scripts/evaluate_massspecgym.py --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_A.jsonl --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_A.json --tanimoto
python scripts/evaluate_massspecgym.py --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_B.jsonl --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_B.json --tanimoto
python scripts/evaluate_massspecgym.py --pred-jsonl /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/candidates_C.jsonl --report /cluster/tufts/liulab/yiwan01/Spec-RAG/out/retrieval/metrics_C.json --tanimoto