A graph-based genetic algorithm (GB-GA) and a graph-based generative model with Monte Carlo tree search (GB-GM-MCTS) for molecular optimization that match or outperform ML-based generative approaches while being orders of magnitude faster.
This study applies perplexity, a model-intrinsic metric from NLP, to rank de novo molecular designs generated by SMILES-based chemical language models and introduces a delta score to detect pretraining bias in transfer-learned CLMs.
Introduces SPECTRA, a framework that generates spectral performance curves to measure how ML model accuracy degrades as train-test overlap decreases across molecular sequencing tasks.
STONED introduces simple string manipulation algorithms on SELFIES for molecular design, achieving competitive results with deep generative models while requiring no training data or GPU resources.
Introduces TamGen, a target-aware molecular generation method using a pre-trained GPT-like chemical language model with protein structure conditioning. A Design-Refine-Test pipeline discovers 14 inhibitors against tuberculosis ClpP protease, with IC50 values as low as 1.9 uM.
This paper couples a retrosynthesis transformer with a forward reaction transformer through parameter sharing, cycle consistency checks, and multinomial latent variables. The combined approach reduces top-1 SMILES invalidity to 0.1% on USPTO-50K, improves top-10 accuracy to 78.5%, and achieves 87.3% pathway coverage on a multi-pathway in-house dataset.
BARTSmiles pre-trains a BART-large model on 1.7 billion SMILES strings from ZINC20 and achieves the best reported results on 11 classification, regression, and generation benchmarks.
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.