LIMO combines a SELFIES-based VAE with a novel stacked property predictor architecture (decoder output as predictor input) and gradient-based reverse optimization on the latent space. It is 6-8x faster than RL baselines and 12x faster than sampling methods while generating molecules with nanomolar binding affinities, including a predicted KD of 6e-14 M against the human estrogen receptor.
The Regression Transformer (RT) reformulates regression as conditional sequence modelling, enabling a single XLNet-based model to both predict continuous molecular properties and generate novel molecules conditioned on desired property values.
RetMol plugs a lightweight cross-attention retrieval module into a pre-trained Chemformer backbone to guide molecule generation toward multi-property design criteria. It requires no task-specific fine-tuning and works with as few as 23 exemplar molecules. It achieves 94.5% success on QED optimization, 96.9% on GSK3b/JNK3 dual inhibitor design, and 2.84 kcal/mol average binding affinity improvement on SARS-CoV-2 main protease inhibitor optimization.
This paper trains a transformer model to correct invalid SMILES produced by de novo molecular generators (RNN, VAE, GAN). The corrector fixes 60-95% of invalid outputs, and the fixed molecules are comparable in novelty and similarity to valid generator outputs. The approach also enables local chemical space exploration by introducing and correcting errors in existing molecules.
MolGen pre-trains on 100M+ SELFIES molecules, introduces domain-agnostic prefix tuning for cross-domain transfer, and applies a chemical feedback paradigm to reduce molecular hallucinations.
The Molecular Transformer applies the Transformer architecture to forward reaction prediction, treating it as SMILES-to-SMILES machine translation. It achieves 90.4% top-1 accuracy on USPTO_MIT, outperforms quantum-chemistry baselines on regioselectivity, and provides calibrated uncertainty scores (0.89 AUC-ROC) for ranking synthesis pathways.
This paper benchmarks 24 machine and deep learning methods on activity cliff compounds (structurally similar molecules with large potency differences) across 30 macromolecular targets. Traditional ML with molecular fingerprints consistently outperforms graph neural networks and SMILES-based transformers on these challenging cases, especially in low-data regimes.
MoLFormer is a transformer encoder with linear attention and rotary positional embeddings, pretrained via masked language modeling on 1.1 billion molecules from PubChem and ZINC. MoLFormer-XL outperforms GNN baselines on most MoleculeNet classification and regression tasks, and attention analysis reveals that the model learns interatomic spatial relationships directly from SMILES strings.
SELFormer is a transformer-based chemical language model that uses SELFIES instead of SMILES as input. Pretrained on 2M ChEMBL compounds via masked language modeling, it achieves strong classification performance on MoleculeNet tasks, outperforming ChemBERTa-2 by ~12% on average across BACE, BBBP, and HIV.
AdaptMol combines an end-to-end graph reconstruction model with unsupervised domain adaptation via class-conditional MMD on bond features and SMILES-validated self-training. Achieves 82.6% accuracy on hand-drawn molecules (10.7 points above prior best) while maintaining state-of-the-art results on four literature benchmarks, using only 4,080 real hand-drawn images for adaptation.
GraphReco presents a rule-based OCSR system with two key innovations: a Fragment Merging line detection algorithm for precise bond identification and a Markov network for probabilistic resolution of atom/bond ambiguity during graph assembly. Achieves 94.2% accuracy on USPTO-10K, outperforming both traditional rule-based and some ML-based methods.
GraSP introduces a general framework for recognizing graphs in images by framing it as sequential subgraph prediction with a binary classifier. A GNN conditions a CNN via FiLM layers to predict whether a candidate graph is a subgraph of the target. Applied to OCSR on QM9, GraSP achieves 67.5% accuracy with no domain-specific modifications.