Computational Chemistry

Fujiyoshi et al.'s segment-based approach for recognizing chemical structures in challenging Japanese patent images with …...

Clevert et al.'s two-stage CNN approach for converting molecular images to SMILES using CDDD embeddings and extensive …...

Chen et al.'s dual-stream encoder approach for robust molecular structure recognition from diverse real-world images …...

Filippov & Nicklaus's open-source rule-based system for converting molecular structure images into machine-readable …...

Fang et al.'s method for converting molecular structure images from scientific documents into machine-readable formats …...

A dataset card for ZINC-22, the largest freely available database of commercially available compounds for virtual …

Learn how to visualize SELFIES molecular representations and explore their unique advantages through random sampling, …

Learn how to create 2D molecular images from SMILES strings using RDKit and PIL, with proper formatting and legends.

SELFIES is a 100% robust string-based representation for chemical molecules, designed for machine learning applications …...

MARCEL dataset provides 722K+ conformers across 76K+ molecules for drug discovery, catalysis, and molecular …

The Müller-Brown potential: a classic two-dimensional analytical benchmark for testing optimization algorithms, reaction …...

SMILES is a specification for describing the structure of chemical molecules using short ASCII strings....