Proposes Augmented Hill-Climb, a hybrid RL strategy for SMILES-based generative models that improves sample efficiency ~45-fold over REINVENT by filtering low-scoring molecules from the loss computation, with diversity filters to prevent mode collapse.
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.
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.
Introduces curriculum learning to the REINVENT de novo design platform, decomposing complex drug design objectives into simpler sequential tasks that accelerate agent convergence and improve output quality over standard reinforcement learning.
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.
DrugEx v3 extends scaffold-constrained drug design by introducing a Graph Transformer with adjacency-matrix-based positional encoding, achieving 100% molecular validity and high predicted affinity for adenosine A2A receptor ligands.
An evolutionary molecular design framework that evolves ECFP fingerprint vectors using a genetic algorithm, reconstructs valid SMILES via an RNN decoder, and evaluates fitness with a DNN property predictor.
Group SELFIES extends SELFIES with group tokens representing functional groups and substructures, maintaining chemical robustness while improving distribution learning and molecular generation quality.
Lingo3DMol introduces FSMILES, a fragment-based SMILES representation with local and global coordinates, to generate drug-like 3D molecules in protein pockets via a transformer language model.
Link-INVENT is an RNN-based generative model for molecular linker design that uses reinforcement learning with a flexible scoring function, demonstrated on fragment linking, scaffold hopping, and PROTAC design.
LLM-Prop uses the encoder half of T5, fine-tuned on Robocrystallographer text descriptions, to predict crystal properties. It outperforms GNN baselines like ALIGNN on band gap and volume prediction while using fewer parameters.