SciMesh

SciMesh is a small local framework for scientific workloads on molecular datasets. It currently provides exact molecular similarity search and exact sparse similarity-graph construction. It runs in one local Python process: there is no network service, multiprocessing, coordinator, database, or dense similarity matrix.

The ChEMBL TSV database is intentionally not included in this repository. Download it separately and pass its path to the commands below. The expected columns are chembl_id and canonical_smiles.

Installation

SciMesh requires Python 3.10+ and RDKit.

python -m venv .venv
source .venv/bin/activate
pip install -e .

RDKit can alternatively be installed from conda-forge:

conda install -c conda-forge rdkit
pip install -e .

Similarity search

similarity-search finds the top-k molecules most similar to a query. The query is supplied either by ChEMBL ID or by SMILES. It uses Morgan fingerprints with radius=2 and fpSize=2048, Tanimoto similarity, streaming TSV reads, and a bounded heap. Invalid SMILES and the query molecule are skipped.

scimesh similarity-search chembl_37_chemreps.txt \
  --query-id CHEMBL939 \
  --top-k 20 \
  --output results.csv

Use a SMILES query when it is not identified by ChEMBL ID:

scimesh similarity-search chembl_37_chemreps.txt \
  --query-smiles 'COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1' \
  --top-k 20 \
  --output results.csv

The output CSV contains rank,chembl_id,canonical_smiles,similarity. Search progress and valid/invalid-SMILES statistics are written to the terminal. --max-rows limits the candidate scan for small tests, while --progress-every 0 disables progress reports.

To render the query and retained candidates:

scimesh similarity-search chembl_37_chemreps.txt \
  --query-id CHEMBL939 \
  --images-dir structures

This writes query.png and top_candidates.png into structures.

Similarity graph

similarity-graph constructs an exact sparse undirected graph. Every valid molecule is a vertex; an edge is emitted only when Tanimoto similarity is at least --threshold. Each fingerprint is calculated once. Comparisons are processed block by block, each pair is tested once (i < j), and no dense N×N matrix is created or stored.

scimesh similarity-graph chembl_37_chemreps.txt \
  --max-rows 10000 \
  --threshold 0.7 \
  --block-size 1000 \
  --output similarity_graph.csv

The deterministic edge-list CSV has source_id,target_id,similarity columns. The command reports valid molecules, checked pairs, emitted edges, rate, and elapsed time. --block-size changes only how comparisons are grouped, not the result.

Development

pip install -e '.[dev]'
pytest

The package separates common dataset parsing and fingerprints from independent workloads. Add future workloads through the workload registry without changing the main CLI.