Chemistry and Molecular File Formats Reference

Chemistry and Molecular File Formats Reference

This reference covers file formats commonly used in computational chemistry, cheminformatics, molecular modeling, and related fields.

Structure File Formats

.pdb - Protein Data Bank

Description: Standard format for 3D structures of biological macromolecules Typical Data: Atomic coordinates, residue information, secondary structure, crystal structure data Use Cases: Protein structure analysis, molecular visualization, docking studies Python Libraries:

• Biopython: Bio.PDB
• MDAnalysis: MDAnalysis.Universe('file.pdb')
• PyMOL: pymol.cmd.load('file.pdb')
• ProDy: prody.parsePDB('file.pdb') EDA Approach:
• Structure validation (bond lengths, angles, clashes)
• Secondary structure analysis
• B-factor distribution
• Missing residues/atoms detection
• Ramachandran plots for validation
• Surface area and volume calculations

.cif - Crystallographic Information File

Description: Structured data format for crystallographic information Typical Data: Unit cell parameters, atomic coordinates, symmetry operations, experimental data Use Cases: Crystal structure determination, structural biology, materials science Python Libraries:

• gemmi: gemmi.cif.read_file('file.cif')
• PyCifRW: CifFile.ReadCif('file.cif')
• Biopython: Bio.PDB.MMCIFParser() EDA Approach:
• Data completeness check
• Resolution and quality metrics
• Unit cell parameter analysis
• Symmetry group validation
• Atomic displacement parameters
• R-factors and validation metrics

.mol - MDL Molfile

Description: Chemical structure file format by MDL/Accelrys Typical Data: 2D/3D coordinates, atom types, bond orders, charges Use Cases: Chemical database storage, cheminformatics, drug design Python Libraries:

• RDKit: Chem.MolFromMolFile('file.mol')
• Open Babel: pybel.readfile('mol', 'file.mol')
• ChemoPy: For descriptor calculation EDA Approach:
• Molecular property calculation (MW, logP, TPSA)
• Functional group analysis
• Ring system detection
• Stereochemistry validation
• 2D/3D coordinate consistency
• Valence and charge validation

.mol2 - Tripos Mol2

Description: Complete 3D molecular structure format with atom typing Typical Data: Coordinates, SYBYL atom types, bond types, charges, substructures Use Cases: Molecular docking, QSAR studies, drug discovery Python Libraries:

• RDKit: Chem.MolFromMol2File('file.mol2')
• Open Babel: pybel.readfile('mol2', 'file.mol2')
• MDAnalysis: Can parse mol2 topology EDA Approach:
• Atom type distribution
• Partial charge analysis
• Bond type statistics
• Substructure identification
• Conformational analysis
• Energy minimization status check

.sdf - Structure Data File

Description: Multi-structure file format with associated data Typical Data: Multiple molecular structures with properties/annotations Use Cases: Chemical databases, virtual screening, compound libraries Python Libraries:

• RDKit: Chem.SDMolSupplier('file.sdf')
• Open Babel: pybel.readfile('sdf', 'file.sdf')
• PandasTools (RDKit): For DataFrame integration EDA Approach:
• Dataset size and diversity metrics
• Property distribution analysis (MW, logP, etc.)
• Structural diversity (Tanimoto similarity)
• Missing data assessment
• Outlier detection in properties
• Scaffold analysis

.xyz - XYZ Coordinates

Description: Simple Cartesian coordinate format Typical Data: Atom types and 3D coordinates Use Cases: Quantum chemistry, geometry optimization, molecular dynamics Python Libraries:

• ASE: ase.io.read('file.xyz')
• Open Babel: pybel.readfile('xyz', 'file.xyz')
• cclib: For parsing QM outputs with xyz EDA Approach:
• Geometry analysis (bond lengths, angles, dihedrals)
• Center of mass calculation
• Moment of inertia
• Molecular size metrics
• Coordinate validation
• Symmetry detection

.smi / .smiles - SMILES String

Description: Line notation for chemical structures Typical Data: Text representation of molecular structure Use Cases: Chemical databases, literature mining, data exchange Python Libraries:

• RDKit: Chem.MolFromSmiles(smiles)
• Open Babel: Can parse SMILES
• DeepChem: For ML on SMILES EDA Approach:
• SMILES syntax validation
• Descriptor calculation from SMILES
• Fingerprint generation
• Substructure searching
• Tautomer enumeration
• Stereoisomer handling

.pdbqt - AutoDock PDBQT

Description: Modified PDB format for AutoDock docking Typical Data: Coordinates, partial charges, atom types for docking Use Cases: Molecular docking, virtual screening Python Libraries:

• Meeko: For PDBQT preparation
• Open Babel: Can read PDBQT
• ProDy: Limited PDBQT support EDA Approach:
• Charge distribution analysis
• Rotatable bond identification
• Atom type validation
• Coordinate quality check
• Hydrogen placement validation
• Torsion definition analysis

.mae - Maestro Format

Description: Schrödinger's proprietary molecular structure format Typical Data: Structures, properties, annotations from Schrödinger suite Use Cases: Drug discovery, molecular modeling with Schrödinger tools Python Libraries:

• schrodinger.structure: Requires Schrödinger installation
• Custom parsers for basic reading EDA Approach:
• Property extraction and analysis
• Structure quality metrics
• Conformer analysis
• Docking score distributions
• Ligand efficiency metrics

.gro - GROMACS Coordinate File

Description: Molecular structure file for GROMACS MD simulations Typical Data: Atom positions, velocities, box vectors Use Cases: Molecular dynamics simulations, GROMACS workflows Python Libraries:

• MDAnalysis: Universe('file.gro')
• MDTraj: mdtraj.load_gro('file.gro')
• GromacsWrapper: For GROMACS integration EDA Approach:
• System composition analysis
• Box dimension validation
• Atom position distribution
• Velocity distribution (if present)
• Density calculation
• Solvation analysis

Computational Chemistry Output Formats

.log - Gaussian Log File

Description: Output from Gaussian quantum chemistry calculations Typical Data: Energies, geometries, frequencies, orbitals, populations Use Cases: QM calculations, geometry optimization, frequency analysis Python Libraries:

• cclib: cclib.io.ccread('file.log')
• GaussianRunPack: For Gaussian workflows
• Custom parsers with regex EDA Approach:
• Convergence analysis
• Energy profile extraction
• Vibrational frequency analysis
• Orbital energy levels
• Population analysis (Mulliken, NBO)
• Thermochemistry data extraction

.out - Quantum Chemistry Output

Description: Generic output file from various QM packages Typical Data: Calculation results, energies, properties Use Cases: QM calculations across different software Python Libraries:

• cclib: Universal parser for QM outputs
• ASE: Can read some output formats EDA Approach:
• Software-specific parsing
• Convergence criteria check
• Energy and gradient trends
• Basis set and method validation
• Computational cost analysis

.wfn / .wfx - Wavefunction Files

Description: Wavefunction data for quantum chemical analysis Typical Data: Molecular orbitals, basis sets, density matrices Use Cases: Electron density analysis, QTAIM analysis Python Libraries:

• Multiwfn: Interface via Python
• Horton: For wavefunction analysis
• Custom parsers for specific formats EDA Approach:
• Orbital population analysis
• Electron density distribution
• Critical point analysis (QTAIM)
• Molecular orbital visualization
• Bonding analysis

.fchk - Gaussian Formatted Checkpoint

Description: Formatted checkpoint file from Gaussian Typical Data: Complete wavefunction data, results, geometry Use Cases: Post-processing Gaussian calculations Python Libraries:

• cclib: Can parse fchk files
• GaussView Python API (if available)
• Custom parsers EDA Approach:
• Wavefunction quality assessment
• Property extraction
• Basis set information
• Gradient and Hessian analysis
• Natural orbital analysis

.cube - Gaussian Cube File

Description: Volumetric data on a 3D grid Typical Data: Electron density, molecular orbitals, ESP on grid Use Cases: Visualization of volumetric properties Python Libraries:

• cclib: cclib.io.ccread('file.cube')
• ase.io: ase.io.read('file.cube')
• pyquante: For cube file manipulation EDA Approach:
• Grid dimension and spacing analysis
• Value distribution statistics
• Isosurface value determination
• Integration over volume
• Comparison between different cubes

Molecular Dynamics Formats

.dcd - Binary Trajectory

Description: Binary trajectory format (CHARMM, NAMD) Typical Data: Time series of atomic coordinates Use Cases: MD trajectory analysis Python Libraries:

• MDAnalysis: Universe(topology, 'traj.dcd')
• MDTraj: mdtraj.load_dcd('traj.dcd', top='topology.pdb')
• PyTraj (Amber): Limited support EDA Approach:
• RMSD/RMSF analysis
• Trajectory length and frame count
• Coordinate range and drift
• Periodic boundary handling
• File integrity check
• Time step validation

.xtc - Compressed Trajectory

Description: GROMACS compressed trajectory format Typical Data: Compressed coordinates from MD simulations Use Cases: Space-efficient MD trajectory storage Python Libraries:

• MDAnalysis: Universe(topology, 'traj.xtc')
• MDTraj: mdtraj.load_xtc('traj.xtc', top='topology.pdb') EDA Approach:
• Compression ratio assessment
• Precision loss evaluation
• RMSD over time
• Structural stability metrics
• Sampling frequency analysis

.trr - GROMACS Trajectory

Description: Full precision GROMACS trajectory Typical Data: Coordinates, velocities, forces from MD Use Cases: High-precision MD analysis Python Libraries:

• MDAnalysis: Full support
• MDTraj: Can read trr files
• GromacsWrapper EDA Approach:
• Full system dynamics analysis
• Energy conservation check (with velocities)
• Force analysis
• Temperature and pressure validation
• System equilibration assessment

.nc / .netcdf - Amber NetCDF Trajectory

Description: Network Common Data Form trajectory Typical Data: MD coordinates, velocities, forces Use Cases: Amber MD simulations, large trajectory storage Python Libraries:

• MDAnalysis: NetCDF support
• PyTraj: Native Amber analysis
• netCDF4: Low-level access EDA Approach:
• Metadata extraction
• Trajectory statistics
• Time series analysis
• Replica exchange analysis
• Multi-dimensional data extraction

.top - GROMACS Topology

Description: Molecular topology for GROMACS Typical Data: Atom types, bonds, angles, force field parameters Use Cases: MD simulation setup and analysis Python Libraries:

• ParmEd: parmed.load_file('system.top')
• MDAnalysis: Can parse topology
• Custom parsers for specific fields EDA Approach:
• Force field parameter validation
• System composition
• Bond/angle/dihedral distribution
• Charge neutrality check
• Molecule type enumeration

.psf - Protein Structure File (CHARMM)

Description: Topology file for CHARMM/NAMD Typical Data: Atom connectivity, types, charges Use Cases: CHARMM/NAMD MD simulations Python Libraries:

• MDAnalysis: Native PSF support
• ParmEd: Can read PSF files EDA Approach:
• Topology validation
• Connectivity analysis
• Charge distribution
• Atom type statistics
• Segment analysis

.prmtop - Amber Parameter/Topology

Description: Amber topology and parameter file Typical Data: System topology, force field parameters Use Cases: Amber MD simulations Python Libraries:

• ParmEd: parmed.load_file('system.prmtop')
• PyTraj: Native Amber support EDA Approach:
• Force field completeness
• Parameter validation
• System size and composition
• Periodic box information
• Atom mask creation for analysis

.inpcrd / .rst7 - Amber Coordinates

Description: Amber coordinate/restart file Typical Data: Atomic coordinates, velocities, box info Use Cases: Starting coordinates for Amber MD Python Libraries:

• ParmEd: Works with prmtop
• PyTraj: Amber coordinate reading EDA Approach:
• Coordinate validity
• System initialization check
• Box vector validation
• Velocity distribution (if restart)
• Energy minimization status

Spectroscopy and Analytical Data

.jcamp / .jdx - JCAMP-DX

Description: Joint Committee on Atomic and Molecular Physical Data eXchange Typical Data: Spectroscopic data (IR, NMR, MS, UV-Vis) Use Cases: Spectroscopy data exchange and archiving Python Libraries:

• jcamp: jcamp.jcamp_reader('file.jdx')
• nmrglue: For NMR JCAMP files
• Custom parsers for specific subtypes EDA Approach:
• Peak detection and analysis
• Baseline correction assessment
• Signal-to-noise calculation
• Spectral range validation
• Integration analysis
• Comparison with reference spectra

.mzML - Mass Spectrometry Markup Language

Description: Standard XML format for mass spectrometry data Typical Data: MS/MS spectra, chromatograms, metadata Use Cases: Proteomics, metabolomics, mass spectrometry workflows Python Libraries:

• pymzml: pymzml.run.Reader('file.mzML')
• pyteomics: pyteomics.mzml.read('file.mzML')
• MSFileReader wrappers EDA Approach:
• Scan count and types
• MS level distribution
• Retention time range
• m/z range and resolution
• Peak intensity distribution
• Data completeness
• Quality control metrics

.mzXML - Mass Spectrometry XML

Description: Open XML format for MS data Typical Data: Mass spectra, retention times, peak lists Use Cases: Legacy MS data, metabolomics Python Libraries:

• pymzml: Can read mzXML
• pyteomics.mzxml
• lxml for direct XML parsing EDA Approach:
• Similar to mzML
• Version compatibility check
• Conversion quality assessment
• Peak picking validation

.raw - Vendor Raw Data

Description: Proprietary instrument data files (Thermo, Bruker, etc.) Typical Data: Raw instrument signals, unprocessed data Use Cases: Direct instrument data access Python Libraries:

• pymsfilereader: For Thermo RAW files
• ThermoRawFileParser: CLI wrapper
• Vendor-specific APIs (Thermo, Bruker Compass) EDA Approach:
• Instrument method extraction
• Raw signal quality
• Calibration status
• Scan function analysis
• Chromatographic quality metrics

.d - Agilent Data Directory

Description: Agilent's data folder structure Typical Data: LC-MS, GC-MS data and metadata Use Cases: Agilent instrument data processing Python Libraries:

• agilent-reader: Community tools
• Chemstation Python integration
• Custom directory parsing EDA Approach:
• Directory structure validation
• Method parameter extraction
• Signal file integrity
• Calibration curve analysis
• Sequence information extraction

.fid - NMR Free Induction Decay

Description: Raw NMR time-domain data Typical Data: Time-domain NMR signal Use Cases: NMR processing and analysis Python Libraries:

• nmrglue: nmrglue.bruker.read_fid('fid')
• nmrstarlib: For NMR-STAR files EDA Approach:
• Signal decay analysis
• Noise level assessment
• Acquisition parameter validation
• Apodization function selection
• Zero-filling optimization
• Phasing parameter estimation

.ft - NMR Frequency-Domain Data

Description: Processed NMR spectrum Typical Data: Frequency-domain NMR data Use Cases: NMR analysis and interpretation Python Libraries:

• nmrglue: Comprehensive NMR support
• pyNMR: For processing EDA Approach:
• Peak picking and integration
• Chemical shift calibration
• Multiplicity analysis
• Coupling constant extraction
• Spectral quality metrics
• Reference compound identification

.spc - Spectroscopy File

Description: Thermo Galactic spectroscopy format Typical Data: IR, Raman, UV-Vis spectra Use Cases: Spectroscopic data from various instruments Python Libraries:

• spc: spc.File('file.spc')
• Custom parsers for binary format EDA Approach:
• Spectral resolution
• Wavelength/wavenumber range
• Baseline characterization
• Peak identification
• Derivative spectra calculation

Chemical Database Formats

.inchi - International Chemical Identifier

Description: Text identifier for chemical substances Typical Data: Layered chemical structure representation Use Cases: Chemical database keys, structure searching Python Libraries:

• RDKit: Chem.MolFromInchi(inchi)
• Open Babel: InChI conversion EDA Approach:
• InChI validation
• Layer analysis
• Stereochemistry verification
• InChI key generation
• Structure round-trip validation

.cdx / .cdxml - ChemDraw Exchange

Description: ChemDraw drawing file format Typical Data: 2D chemical structures with annotations Use Cases: Chemical drawing, publication figures Python Libraries:

• RDKit: Can import some CDXML
• Open Babel: Limited support
• ChemDraw Python API (commercial) EDA Approach:
• Structure extraction
• Annotation preservation
• Style consistency
• 2D coordinate validation

.cml - Chemical Markup Language

Description: XML-based chemical structure format Typical Data: Chemical structures, reactions, properties Use Cases: Semantic chemical data representation Python Libraries:

• RDKit: CML support
• Open Babel: Good CML support
• lxml: For XML parsing EDA Approach:
• XML schema validation
• Namespace handling
• Property extraction
• Reaction scheme analysis
• Metadata completeness

.rxn - MDL Reaction File

Description: Chemical reaction structure file Typical Data: Reactants, products, reaction arrows Use Cases: Reaction databases, synthesis planning Python Libraries:

• RDKit: Chem.ReactionFromRxnFile('file.rxn')
• Open Babel: Reaction support EDA Approach:
• Reaction balancing validation
• Atom mapping analysis
• Reagent identification
• Stereochemistry changes
• Reaction classification

.rdf - Reaction Data File

Description: Multi-reaction file format Typical Data: Multiple reactions with data Use Cases: Reaction databases Python Libraries:

• RDKit: RDF reading capabilities
• Custom parsers EDA Approach:
• Reaction yield statistics
• Condition analysis
• Success rate patterns
• Reagent frequency analysis

Computational Output and Data

.hdf5 / .h5 - Hierarchical Data Format

Description: Container for scientific data arrays Typical Data: Large arrays, metadata, hierarchical organization Use Cases: Large dataset storage, computational results Python Libraries:

• h5py: h5py.File('file.h5', 'r')
• pytables: Advanced HDF5 interface
• pandas: Can read HDF5 EDA Approach:
• Dataset structure exploration
• Array shape and dtype analysis
• Metadata extraction
• Memory-efficient data sampling
• Chunk optimization analysis
• Compression ratio assessment

.pkl / .pickle - Python Pickle

Description: Serialized Python objects Typical Data: Any Python object (molecules, dataframes, models) Use Cases: Intermediate data storage, model persistence Python Libraries:

• pickle: Built-in serialization
• joblib: Enhanced pickling for large arrays
• dill: Extended pickle support EDA Approach:
• Object type inspection
• Size and complexity analysis
• Version compatibility check
• Security validation (trusted source)
• Deserialization testing

.npy / .npz - NumPy Arrays

Description: NumPy array binary format Typical Data: Numerical arrays (coordinates, features, matrices) Use Cases: Fast numerical data I/O Python Libraries:

• numpy: np.load('file.npy')
• Direct memory mapping for large files EDA Approach:
• Array shape and dimensions
• Data type and precision
• Statistical summary (mean, std, range)
• Missing value detection
• Outlier identification
• Memory footprint analysis

.mat - MATLAB Data File

Description: MATLAB workspace data Typical Data: Arrays, structures from MATLAB Use Cases: MATLAB-Python data exchange Python Libraries:

• scipy.io: scipy.io.loadmat('file.mat')
• h5py: For v7.3 MAT files EDA Approach:
• Variable extraction and types
• Array dimension analysis
• Structure field exploration
• MATLAB version compatibility
• Data type conversion validation

.csv - Comma-Separated Values

Description: Tabular data in text format Typical Data: Chemical properties, experimental data, descriptors Use Cases: Data exchange, analysis, machine learning Python Libraries:

• pandas: pd.read_csv('file.csv')
• csv: Built-in module
• polars: Fast CSV reading EDA Approach:
• Data types inference
• Missing value patterns
• Statistical summaries
• Correlation analysis
• Distribution visualization
• Outlier detection

.json - JavaScript Object Notation

Description: Structured text data format Typical Data: Chemical properties, metadata, API responses Use Cases: Data interchange, configuration, web APIs Python Libraries:

• json: Built-in JSON support
• pandas: pd.read_json()
• ujson: Faster JSON parsing EDA Approach:
• Schema validation
• Nesting depth analysis
• Key-value distribution
• Data type consistency
• Array length statistics

.parquet - Apache Parquet

Description: Columnar storage format Typical Data: Large tabular datasets efficiently Use Cases: Big data, efficient columnar analytics Python Libraries:

• pandas: pd.read_parquet('file.parquet')
• pyarrow: Direct parquet access
• fastparquet: Alternative implementation EDA Approach:
• Column statistics from metadata
• Partition analysis
• Compression efficiency
• Row group structure
• Fast sampling for large files
• Schema evolution tracking