A large-scale benchmark of 25 molecular optimization methods on 23 oracles under constrained oracle budgets, showing that sample efficiency is a critical and often neglected dimension of evaluation.
A comprehensive survey classifying molecular representation learning foundation models by input modality (sequence, graph, 3D, image, multimodal) and analyzing four pretraining paradigms for drug discovery 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.
This paper introduces ROGI-XD, a reformulation of the ROuGhness Index that enables fair comparison of QSPR surface roughness across molecular representations of different dimensionalities. Evaluating VAE, GIN, ChemBERTa, and ChemGPT representations, the authors show that pretrained chemical models do not produce smoother structure-property landscapes than simple molecular fingerprints or descriptors.
MolGenSurvey systematically reviews ML models for molecule design, organizing the field by molecular representation (1D/2D/3D), generative method (deep generative models vs. combinatorial optimization), and task type (8 distinct generation/optimization tasks). It catalogs over 100 methods, unifies task definitions via input/output/goal taxonomy, and identifies key challenges including out-of-distribution generation, oracle costs, and lack of unified benchmarks.
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.
This study benchmarks RNN-based chemical language models against graph generative models on three challenging tasks: high penalized LogP distributions, multi-modal molecular distributions, and large-molecule generation from PubChem. The LSTM language models consistently outperform JTVAE and CGVAE.
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.