A 2025 Faraday Discussions paper describing the major overhaul of InChI v1.07 that fixed more than 3000 bugs, added support for inorganic and organometallic compounds, and modernized the system to align with FAIR data principles for chemistry databases.
A 2019 format specification introducing two complementary standards for chemical mixtures. Mixfile provides comprehensive mixture descriptions and MInChI provides compact canonical identifiers. This addresses the long-standing lack of standardized machine-readable formats for multi-component chemical systems.
Can we create a SMILES-like notation for nanomaterials? A collaborative workshop tackles the challenge of representing complex, multi-component nanomaterials with a proposed extension to the established InChI system.
A 2023 software update paper documenting improvements to the SELFIES Python library (v2.1.1), including a streamlined context-free grammar, expanded support for aromatic systems and stereochemistry, customizable semantic constraints, ML utility functions, and performance benchmarks on 300K+ molecules.
A 2018 infrastructure paper introducing RInChI (Reaction InChI), the first standardized format for uniquely identifying chemical reactions through algorithmic hashing and layering, enabling reaction database searching and duplicate detection analogous to how InChI works for individual molecules.
The 2020 paper that introduced SELFIES: Mario Krenn and colleagues created a molecular representation that solves SMILES validity problems. It guarantees every generated string corresponds to a valid chemical structure.
David Weininger introduced SMILES notation in 1988, establishing encoding rules for representing chemical structures as compact, human-readable strings.
Build a robust Python CLI tool that converts both SMILES and SELFIES notation into publication-quality 2D molecular images, complete with formulas and legends.
SELFIES is a molecular string representation where every possible string decodes to a valid molecule, solving the invalid-output problem that limits SMILES in generative machine learning.
Comprehensive overview of SMILES notation for chemical structures, covering syntax for atoms, bonds, branches, rings, and stereochemistry, plus its key limitations for machine learning.
A foundational 1931 paper that derives exact recursive formulas for counting alkane structural isomers, correcting historical errors and establishing the first systematic enumeration up to C₄₀.
Can mathematical signatures capture molecular shape? We test whether Coulomb matrix eigenvalues can distinguish alkane constitutional isomers, from unsupervised clustering failures to supervised learning successes.