Datasets:

Tc-43
/

novel_myosin_modulator_designs

license: cc-by-4.0
pretty_name: Novel Myosin Motor Modulators — GA-II Designed Ligand–Receptor Complexes
task_categories:
  - graph-ml
  - other
size_categories:
  - n<1K
language:
  - en
tags:
  - chemistry
  - drug-discovery
  - molecular-docking
  - protein-ligand-complex
  - myosin
  - motor-protein
  - ATPase
  - cardiomyopathy
  - allosteric-modulator
  - biology
  - de-novo-design
  - generative-design
  - structure-based-drug-design
  - synthetic
  - synthetic-data
  - ai-generated
  - computational-chemistry
  - virtual-screening
  - cheminformatics
  - SMILES
  - cardiovascular
  - allosteric
  - cardiac-myosin
  - MYH7
  - mavacamten
  - hypertrophic-cardiomyopathy
  - HCM
  - cardiac-contractility
  - generative-ai
  - small-molecule
  - SDF
  - genetic-algorithm
  - GA-II
  - autodock-vina
  - TC-43
  - technetium

Novel Myosin Motor Modulators — GA-II Designed Ligand–Receptor Complexes

Why this target matters. Direct myosin modulation is the first therapeutic strategy to target cardiac contractility at the motor protein itself, a mechanism validated clinically by mavacamten's approval in hypertrophic cardiomyopathy.

22 computationally designed small-molecule ligands docked into the myosin motor domain, each provided as a single-file protein–ligand complex in PDB format (21 unique ligand structures).

The myosin motor domain is the force-generating ATPase of muscle; allosteric small-molecule modulators of cardiac/skeletal myosin are an established cardiomyopathy modality (e.g., mavacamten).

Receptor note: coordinates correspond to the myosin motor domain (chain A res 33–782). TODO: add the source RCSB PDB accession for the receptor template used to generate these complexes.

Dataset summary


Complex files	22 (`*_cmpx.pdb`)
Unique ligand SMILES	21
Receptor	myosin motor domain (chain A res 33–782)
Generator	Technetium GA-II pocket-conditioned generative platform
Generation date	2025-09-13 – 2025-09-28
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 21 unique ligand structures; docking energy is from the generation/docking pipeline.

Property	Range	Median
Docking energy (AutoDock Vina)	≤ -10.9 kcal/mol (down to -14.4)	—
Molecular weight	333.4 – 498.6 Da	420.5
cLogP	1.8 – 3.9	3.5
TPSA	68.8 – 99.5 Å²	87.9
Fsp3 (fraction sp³ C)	0.2 – 0.5	0.4
H-bond donors	1 – 2	1
H-bond acceptors	4 – 8	6
Rotatable bonds	3 – 7	5

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`)

A combined SMILES index (Novel Myosin Motor Inhibitors.smi, SMILES<TAB>compound-id) is included for quick cheminformatics use.

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.