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.
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.
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.
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.
AlphaDrug generates drug candidates for specific protein targets by combining an Lmser Transformer (with hierarchical encoder-decoder skip connections) and Monte Carlo tree search guided by docking scores, achieving higher binding affinities than known ligands on 86% of test proteins.
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.
Shows that divergence between optimization and control scores during goal-directed molecular generation is explained by pre-existing disagreement among QSAR models on the training distribution, not by algorithmic exploitation of model-specific biases.
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.
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.
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.