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…nd components Co-authored-by: Cursor <cursoragent@cursor.com>
…-ligand pipeline Co-authored-by: Cursor <cursoragent@cursor.com>
…-ligand training - Add max_cluster_replicates parameter to StructureLightningDataModule to cap upsampling of small datasets in balanced training mode - Add data configs: structure_ligand_all (7-dataset combined), PLINDER baseline, distillation, and intermediate configs for protein-ligand training - Fix elif→if in gen_ume protein-ligand model to allow simultaneous IF/FF eval - Fix PDB loading edge cases in latent_generator io - Add structure transforms for protein-ligand data handling
…line eval - Add compute_protein_ligand_contacts and compute_aligned_ligand_rmsd to generation utils as reusable standalone functions - Add contact-based ligand_in_pocket metric to forward folding evaluator: checks if predicted ligand contacts GT pocket residues (replaces centroid-based) - Add ligand_contacts_protein metric (any protein-ligand contact at 6A) - Allow skipping ESMFold in conditioned generation (plm_fold=None) - Add best-of-N display and ligand placement stats to FF cmdline output - Add LigandMPNN inverse folding baseline evaluator and cmdline script - Update inverse folding evaluator with pocket-aware metrics - Update conditioned gen cmdline with additional generation parameters
- Update forward folding and inverse folding callbacks with ligand support - Update hydra callback configs with protein-ligand evaluation parameters - Add save_structures and minimize_ligand options to callback configs
…ct-based ligand placement - Add good_fold_and_in_pocket_fraction (TM > 0.5 AND ligand in correct pocket) to FF evaluator summary and cmdline output - Update merge_cofold_results.py to use contact-based ligand_in_pocket (CA within 6A of GT pocket residues) instead of centroid distance - Add cofold_ligand_contacts_protein and cofold_n_pocket_contacts metrics - Report good_fold_and_in_pocket in merge summary
- Restructure run_full_eval.sh: Phase 2 supports rf3, boltz, or both backends with configurable task selection (COFOLD_TASKS=if,ff,cg,lmpnn) - RF3 co-folding runs in parallel chunks across multiple GPUs - Boltz2 co-folding uses SLURM array jobs (one per sample) - Phase 3 merges co-fold results from either backend - Add benchmark_conditioned_gen.py for Gen-UME vs Proteina-Complexa comparison with ESMFold pre-filtering and per-design timing - Add run_rf3_ff_baseline.py for RF3 co-folding on designed sequences - Add submit_cofold_batch.py and run_cofold_local.py for batch co-folding
… docs - Add ligand-conditioned generation and LigandMPNN baseline sections - Document evaluation pipeline (Phase 1-3) with RF3/Boltz2 co-folding - Document contact-based ligand placement metrics and good_fold_and_in_pocket - Add training data configs and training commands - Document benchmark script for Gen-UME vs Proteina-Complexa - Add best-of-N forward folding and aligned ligand RMSD - Update PoseBusters benchmark description
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