MOSAIC-UCSD/MOSAIC_conditional_motif

MOSAIC: Conditional Motif Generation

Conditional generation samples + metrics for the three MOSAIC tokenizers (SENT, HDT-MC, HDTC) prompted with each of 6 shared ring/aromatic motifs, across MOSES, GuacaMol, and COCONUT.

The conditioning prompt for each model is the standalone-motif tokenization, with closing tokens stripped so the model continues from "the molecule starts with this motif." For HDT-MC, the outer ENTER block is left open so the model can add additional communities; for HDTC, the trailing super-graph block is stripped so the model can add more typed communities.

Layout

• —{dataset}__AVG.png — per-dataset metric table averaged over the 6 motifs
• —{dataset}__AVG__grid.png — per-dataset 6×5 visual grid (one row per motif, one column per model, plus reference + motif primer)
• —{dataset}/_reference/{motif}.txt — k motif-containing reference SMILES drawn from training (used as the comparison set for SNN / Frag / motif-distribution MMDs)
• —{dataset}/{motif}/{dataset}__{motif}.png — per-cell metric table
• —{dataset}/{motif}/{dataset}__{motif}__grid.png — per-cell 5-column visual grid (Reference | Motif Primer | SENT | HDT-MC | HDTC)
• —{dataset}/{motif}/{model}/generated_smiles.txt — k=100 conditional generations
• —{dataset}/{motif}/{model}/generated_metadata.json — gen-time stats (prompt token sequence, decode rate, motif retention, sampling params, elapsed)
• —{dataset}/{motif}/{model}/metrics.json — full computed metrics

Motifs

Datasets

• —moses — MOSES drug-like (training set ~1.6M)
• —guacamol — GuacaMol drug-like (test split fallback, ~941 reference SMILES)
• —coconut — COCONUT natural products (training set ~10K)

Models

Listed in the column order used by the rendered tables (flat → unsupervised hierarchies → supervised hierarchy → typed hierarchy):

• —sent — flat random-walk tokenizer (SENT)
• —hdt_lou — HDT with Louvain community coarsening (HDT-Lou)
• —hdt_hac — HDT with HAC-avg community coarsening (HDT-HAC)
• —hdt — HDT with motif-community coarsening (HDT-MC)
• —hdtc — HDT-Compositional, typed two-level (RING / FUNC / SINGLETON)

All models are gpt2xs (~11M params) trained with next-token prediction. Per-cell sample budget: k=100 decodable molecules per (dataset, model, motif), obtained via rejection sampling capped at 800 attempts.

Pipeline

Generation/metric/render code lives in the MOSAIC repo at scripts/conditional_motif/.