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.
This position paper demonstrates that genetic algorithms (GAs) perform surprisingly well on molecular generation benchmarks, often outperforming complex deep learning methods. The authors propose the GA criterion: new molecule generation algorithms should demonstrate a clear advantage over GAs.
Proposes a benchmark for de novo drug design using SMINA docking scores across eight drug targets, revealing that popular generative models fail to outperform random ZINC subsets.
Tartarus introduces a modular suite of realistic molecular design benchmarks grounded in computational chemistry simulations. Benchmarking eight generative models reveals that no single algorithm dominates all tasks, and simple genetic algorithms often outperform deep generative models.
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.
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.
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.
Technical report on Uni-Parser, an industrial-grade document parsing engine that uses a modular multi-expert architecture to parse scientific PDFs into structured representations. Integrates MolParser 1.5 for OCSR, achieving 88.6% accuracy on chemical structures while processing up to 20 pages per second.
Proposes the ‘Optical Chemical Structure Understanding’ (OCSU) task to translate molecular images into multi-level descriptions (motifs, IUPAC, SMILES). Introduces the Vis-CheBI20 dataset and two paradigms: DoubleCheck (OCSR-based) and Mol-VL (OCSR-free).