InChI (International Chemical Identifier) is an open standard from IUPAC that represents molecular structures as hierarchical, layered strings optimized for database interoperability, unique identification, and web search via its hashed InChIKey.
A comprehensive review of how solid-state materials can be numerically represented for machine learning, spanning structural features, graph neural networks, compositional descriptors, transfer learning, and generative models for inverse design.
BioT5 uses SELFIES representations and separate tokenization to pre-train a unified T5 model across molecules, proteins, and text, achieving state-of-the-art results on 10 of 15 downstream tasks.
MolFM pre-trains a multimodal encoder that fuses 2D molecular graphs, biomedical text, and knowledge graph entities through fine-grained cross-modal attention, achieving strong gains on cross-modal retrieval, molecule captioning, text-based generation, and property prediction.
MoMu pre-trains dual graph and text encoders on 15K molecule graph-text pairs using contrastive learning, enabling cross-modal retrieval, molecule captioning, zero-shot text-to-graph generation, and improved molecular property prediction.
DMP combines a SMILES Transformer and a GNN branch during pre-training, using masked language modeling plus a BYOL-inspired dual-view consistency loss to learn complementary molecular representations.
MG-BERT combines GNN-style local attention with BERT’s masked pretraining on molecular graphs, learning context-sensitive atomic representations that improve ADMET property prediction across 11 benchmark datasets.
Mol2vec treats molecular substructures as words and compounds as sentences, training Word2vec on 19.9M molecules to produce dense embeddings that capture chemical intuition and enable competitive property prediction.
Introduces Atom-in-SMILES (AIS), a tokenization scheme that encodes local chemical environments into SMILES tokens, improving prediction quality across canonicalization, retrosynthesis, and property prediction tasks.
Winter et al. propose CDDD, a translation-based encoder-decoder that learns continuous molecular descriptors by translating between equivalent chemical representations like SMILES and InChI, pretrained on 72 million compounds.
DeepSMILES replaces paired parentheses and ring closure symbols in SMILES with a postfix notation and single ring-size digits, making it easier for generative models to produce syntactically valid molecular strings.
Group SELFIES extends SELFIES with group tokens representing functional groups and substructures, maintaining chemical robustness while improving distribution learning and molecular generation quality.