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.
Molecule Attention Transformer (MAT) augments Transformer self-attention with inter-atomic distances and graph adjacency, achieving strong property prediction across diverse molecular tasks with minimal hyperparameter tuning after self-supervised pretraining.
A systematic study of SMILES augmentation strategies for molecular property prediction, showing that augmentation consistently improves CNN and RNN performance and that prediction variance across SMILES correlates with model uncertainty.
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.
MTL-BERT pretrains a BERT model on 1.7M unlabeled SMILES, then fine-tunes jointly on 60 ADMET and molecular property tasks using SMILES enumeration as data augmentation in all phases.
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.
BindGPT formulates 3D molecular design as autoregressive text generation over combined SMILES and XYZ tokens, using large-scale pre-training and reinforcement learning to achieve competitive pocket-conditioned molecule generation.
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.
A comprehensive benchmark evaluating GPT-4, GPT-3.5, Davinci-003, Llama, and Galactica on eight practical chemistry tasks, revealing that LLMs are competitive on classification and text tasks but struggle with SMILES-dependent generation.
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.