--- license: cc-by-4.0 pretty_name: Cereblon (CRBN) Binders — Ligand–Receptor Complexes task_categories: - graph-ml - other size_categories: - n<1K language: - en tags: - chemistry - drug-discovery - molecular-docking - protein-ligand-complex - CRBN - cereblon - molecular-glue - targeted-protein-degradation - E3-ligase - biology - de-novo-design - generative-design - structure-based-drug-design - synthetic - synthetic-data - ai-generated - computational-chemistry - virtual-screening - cheminformatics - SMILES - E3-ubiquitin-ligase - CRL4-CRBN - thalidomide - lenalidomide - pomalidomide - IMiD - PROTAC - TPD - degrader - neosubstrate - ligase-handle - generative-ai - small-molecule - SDF - genetic-algorithm - GA-II - autodock-vina - TC-43 - technetium --- # Cereblon (CRBN) Binders — Ligand–Receptor Complexes > **Why this target matters.** Cereblon is the most clinically validated E3 ligase handle in medicine — the direct target of thalidomide, lenalidomide and pomalidomide, and the recruiting engine behind most molecular glues and PROTACs now in the clinic. **221** computationally designed small-molecule ligands docked into Cereblon (**CRBN**), each provided as a single-file **protein–ligand complex in PDB format** (221 unique ligand structures). **CRBN** is the substrate-receptor subunit of the CRL4^CRBN E3 ubiquitin ligase and the central recruiting handle for **molecular glues and PROTACs** in targeted protein degradation. Designs occupy the thalidomide-binding domain (the classic tri-tryptophan glutarimide pocket). > **Receptor note:** coordinates correspond to the CRBN thalidomide-binding domain (chain A res 20–123). > *TODO: add the source RCSB PDB accession for the receptor template used to generate these complexes.* ## Dataset summary | | | |---|---| | Complex files | **221** (`*_cmpx.pdb`) | | Unique ligand SMILES | **221** | | Receptor | CRBN thalidomide-binding domain (chain A res 20–123) | | Generator | Technetium GA-II pocket-conditioned generative platform | | Pose scoring | AutoDock Vina | These are de novo, scaffold-constrained generative designs produced by the **Technetium GA-II** pocket-conditioned generative platform. Each design is docked into the target pocket and scored with **AutoDock Vina**; a `REMARK CORE` record preserves the scaffold/attachment context. Each complex file is self-contained — receptor structure, the ligand's 3D docked pose, and a 2D↔3D atom map all travel inside the single PDB. ## Property profile Physicochemical ranges are computed with RDKit over the **221** unique ligand structures; docking energy is from the generation/docking pipeline. | Property | Range | Median | |---|---|---| | Docking energy (AutoDock Vina) | ≤ -8.4 kcal/mol (down to -9.5) | — | | Molecular weight | 216.2 – 299.3 Da | 282.3 | | cLogP | 0.0 – 3.0 | 1.9 | | TPSA | 38.3 – 102.4 Ų | 57.1 | | Fsp3 (fraction sp³ C) | 0.2 – 0.6 | 0.3 | | H-bond donors | 0 – 4 | 1 | | H-bond acceptors | 2 – 6 | 3 | | Rotatable bonds | 1 – 3 | 1 | ## File format Each `*_cmpx.pdb` bundles the receptor and one docked ligand pose: | Record | Content | |---|---| | `REMARK VINA RESULT …` | AutoDock Vina docking score (kcal/mol) | | `REMARK CORE ` | the scaffold / attachment context of the design | | `REMARK SMILES ` | the docked ligand (2D structure) | | `REMARK SMILES IDX …` | map of each SMILES heavy-atom position ↔ its ligand atom serial (the 2D↔3D key) | | `ATOM … ` | receptor heavy atoms | | `ATOM … UNL` (after `MODEL 1`) | ligand 3D pose (residue name `UNL`) | ## Usage ```python import glob def read_complex(path): smiles, idx = None, {} with open(path) as fh: for line in fh: if line.startswith("REMARK SMILES IDX"): toks = line.split()[3:] # flat list of (smiles_pos, atom_serial) for i in range(0, len(toks), 2): idx[int(toks[i])] = int(toks[i + 1]) elif line.startswith("REMARK SMILES"): smiles = line.split(None, 2)[2].strip() return smiles, idx # idx[smiles_atom_position] -> ligand atom serial for f in glob.glob("*_cmpx.pdb"): smi, idx = read_complex(f) # ligand atoms are the `ATOM ... UNL` records following `MODEL 1` ``` ## Provenance & intended use - These are **computationally generated** designs and docked poses — **not** experimentally validated binders. No claim of activity or selectivity is made. - Intended for machine-learning, cheminformatics, generative-model benchmarking, and docking-pose research on a well-defined target. ## Citation Generated by **Technetium Therapeutics**. Poses scored with AutoDock Vina.