This 2016 paper first proposed treating organic reaction prediction as a neural machine translation problem, using a GRU-based sequence-to-sequence model with attention to translate reactant SMILES strings into product SMILES strings.
Proposes ORGAN, a framework that extends SeqGAN with domain-specific reward functions via reinforcement learning, enabling tunable generation of molecules optimized for druglikeness, solubility, and synthesizability while maintaining sample diversity.
PharmaGPT is a suite of domain-specific LLMs (13B and 70B parameters) built on LLaMA with continued pretraining on biopharmaceutical and chemical data, achieving strong results on NAPLEX and Chinese pharmacist exams.
PharMolixFM proposes a unified framework for all-atom foundation models using three multi-modal generative approaches (diffusion, flow matching, BFN) and demonstrates competitive docking accuracy with fast inference.
ReactionT5 introduces a two-stage pretraining pipeline (compound then reaction) on the Open Reaction Database, enabling competitive product and yield prediction with as few as 30 fine-tuning reactions.
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.