Datasets:

Tc-43
/

ACE2-B0AT1

license: cc-by-4.0
pretty_name: ACE2–B0AT1 Ligand Designs — Ligand–Receptor Complexes
task_categories:
  - graph-ml
  - other
size_categories:
  - n<1K
language:
  - en
tags:
  - chemistry
  - drug-discovery
  - molecular-docking
  - protein-ligand-complex
  - ACE2
  - B0AT1
  - SLC6A19
  - amino-acid-transporter
  - metabolic-disease
  - PKU
  - biology
  - de-novo-design
  - generative-design
  - structure-based-drug-design
  - synthetic
  - synthetic-data
  - ai-generated
  - computational-chemistry
  - cheminformatics
  - SMILES
  - transporter
  - phenylketonuria
  - neutral-amino-acid-transporter
  - solute-carrier
  - membrane-protein
  - Hartnup-disorder
  - phenylalanine
  - rare-disease
  - generative-ai
  - small-molecule
  - SDF
  - genetic-algorithm
  - GA-II
  - autodock-vina
  - TC-43
  - technetium
  - pocket-conditioned
  - pocket-conditioned-generation
  - de-novo-drug-design

ACE2–B0AT1 Ligand Designs — Ligand–Receptor Complexes

Why this target matters. B0AT1 (SLC6A19) is a rare transporter target with direct human genetic validation — people who lack it (Hartnup disorder) are largely healthy — making it an unusually de-risked oral route to lowering blood phenylalanine in phenylketonuria.

38 small molecules generated de novo by the Technetium TC-43.ai engine (GA-II), conditioned on the ACE2 · B0AT1 (SLC6A19) complex. Each molecule was constructed against this pocket rather than selected from a compound library — docking (AutoDock Vina) came afterwards, to place and score the generated molecules in the site. Every design is supplied as a single-file protein–ligand complex in PDB format (31 unique ligand structures).

B0AT1 (SLC6A19) is the neutral amino-acid transporter that traffics to the cell surface with a partner (ACE2 in the gut). It is an emerging target for phenylketonuria and chronic kidney disease — interest reinforced by positive Phase I data for Maze Therapeutics' oral SLC6A19 inhibitor MZE782 (2025).

Receptor note: coordinates correspond to the ACE2 · B0AT1 heterocomplex (chain A res 5–609; chain B res 20–768; chain C res 20–768; chain D res 5–609). TODO: add the source RCSB PDB accession for the receptor template used to generate these complexes.

Dataset summary


Complex files	38 (`*_cmpx.pdb`)
Unique ligand SMILES	31
Receptor	ACE2 · B0AT1 heterocomplex (chain A res 5–609; chain B res 20–768; chain C res 20–768; chain D res 5–609)
Generator	Technetium GA-II pocket-conditioned generative platform
Generation date	2025-10-04 – 2025-10-07
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 31 unique ligand structures; docking energy is from the generation/docking pipeline.

Property	Range	Median
Docking energy (AutoDock Vina)	≤ -9.5 kcal/mol (down to -11.4)	—
Molecular weight	287.4 – 367.5 Da	336.4
cLogP	1.7 – 4.6	3.5
TPSA	49.3 – 98.7 Å²	70.7
Fsp3 (fraction sp³ C)	0.2 – 0.6	0.3
H-bond donors	1 – 3	2
H-bond acceptors	2 – 5	3
Rotatable bonds	2 – 5	4

File format

Each *_cmpx.pdb bundles the receptor and one docked ligand pose:

Record	Content
`REMARK VINA RESULT <energy> …`	AutoDock Vina docking score (kcal/mol)
`REMARK CORE <smiles>`	the scaffold / attachment context of the design
`REMARK SMILES <smiles>`	the docked ligand (2D structure)
`REMARK SMILES IDX <pos> <serial> …`	map of each SMILES heavy-atom position ↔ its ligand atom serial (the 2D↔3D key)
`ATOM … <chain>`	receptor heavy atoms
`ATOM … UNL` (after `MODEL 1`)	ligand 3D pose (residue name `UNL`)

Usage

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.