Converting SELFIES Strings to 2D Molecular Images
Visualize SELFIES molecular representations and test their 100% robustness through random sampling experiments.
Molecular-Representation - Page 5
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 molecular string representation for ML, implemented in the open-source selfies Python library.
MARCEL: Molecular Representation & Conformers
MARCEL dataset provides 722K+ conformers across 76K+ molecules for drug discovery, catalysis, and molecular …
SMILES: Compact Notation for Chemical Structures
SMILES (Simplified Molecular Input Line Entry System) represents chemical structures using compact ASCII strings.
GEOM: Energy-Annotated Molecular Conformations
Dataset card for GEOM, providing energy-annotated molecular conformations generated via CREST/xTB and refined with DFT …
DenoiseVAE: Adaptive Noise for Molecular Pre-training
Liu et al.'s ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise for better molecular …
Beyond Atoms: 3D Space Modeling for Molecular Pretraining
Lu et al. introduce SpaceFormer, a Transformer that models entire 3D molecular space (not just atoms) for superior …
GEOM Dataset: 3D Molecular Conformer Generation
Learn how GEOM transforms 2D molecular graphs into dynamic 3D conformer ensembles for molecular machine learning …
Invalid SMILES Benefit Chemical Language Models: A Study
Skinnider (2024) shows that generating invalid SMILES actually improves chemical language model performance through …
Synthetic Isomer Data Generation Pipeline
An end-to-end cheminformatics pipeline transforming 1D chemical formulas into 3D conformer datasets using graph …
Can You Hear the Shape of a Molecule? (Part Three)
Supervised learning reveals hidden eigenvalue patterns that clustering missed, testing k-NN and logistic regression on …