Converting SELFIES Strings to 2D Molecular Images
Learn how to visualize SELFIES molecular representations and explore their unique advantages through random sampling, …
Computational Chemistry
Converting SMILES Strings to 2D Molecular Images
Learn how to create 2D molecular images from SMILES strings using RDKit and PIL, with proper formatting and legends.
SELFIES (Self-Referencing Embedded Strings)
SELFIES is a 100% robust string-based representation for chemical molecules, designed for machine learning applications …...
MARCEL: Molecular Representation and Conformer Ensemble Learning
MARCEL dataset provides 722K+ conformers across 76K+ molecules for drug discovery, catalysis, and molecular …
Müller-Brown Potential
The Müller-Brown potential: a classic two-dimensional analytical benchmark for testing optimization algorithms, reaction …...
SMILES (Simplified Molecular Input Line Entry System)
SMILES is a specification for describing the structure of chemical molecules using short ASCII strings....
The Number of Isomeric Hydrocarbons of the Methane Series
Henze and Blair's 1931 JACS paper introducing the recursive method for counting alkane isomers, founding mathematical …...
GEOM: Energy-Annotated Molecular Conformations
A dataset card for the GEOM dataset, a collection of energy-annotated molecular conformations for property prediction …
GDB-11: Chemical Universe Database (26.4M Molecules)
A dataset card for the Generated Database 11 (GDB-11), a database of 26.4 million small organic molecules for virtual …
Implementing the Müller-Brown Potential in PyTorch
Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …
Müller-Brown Basin MA: Langevin Dynamics Simulation
Langevin dynamics simulation showing particle motion in the deep reactant minimum (Basin MA) of the Müller-Brown …
Müller-Brown Basin MB: Langevin Dynamics Simulation
Langevin dynamics simulation showing particle motion in the product minimum (Basin MB) of the Müller-Brown potential …