Introduces a policy-based reinforcement learning method that fine-tunes an RNN pre-trained on ChEMBL SMILES to generate molecules with specified desirable properties, using an augmented episodic likelihood that anchors the agent to its prior while optimizing a user-defined scoring function.
A review chapter tracing three stages of transformer adoption in chemistry: task-specific single-modality models (reaction prediction, retrosynthesis), multimodal approaches bridging spectra and text, and LLM-powered agents like ChemCrow for general chemical reasoning.
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.
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.
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.
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 minireview of chemical language models for de novo molecule design, covering SMILES and SELFIES representations, RNN and Transformer architectures, distribution learning, goal-directed and conditional generation, and prospective experimental validation.
CogMol uses a SMILES VAE and multi-attribute controlled sampling (CLaSS) to generate novel, target-specific drug molecules for unseen SARS-CoV-2 proteins without model retraining.