Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер 64(16), С. 6259 - 6280

Опубликована: Авг. 13, 2024

Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science. Over the last decades, diverse computational techniques have been developed, from using simple physical chemical properties molecular fingerprints in statistical models classical machine learning to advanced deep approaches. In this review, we aim distill insights current research on employing transformer MPP. We analyze currently available explore key questions that arise when training fine-tuning a model These encompass choice scale of pretraining data, optimal architecture selections, promising objectives. Our analysis highlights areas not yet covered research, inviting further exploration enhance field's understanding. Additionally, address challenges comparing different models, emphasizing need standardized data splitting robust analysis.

Язык: Английский

Bridging imaging-based in vitro methods from biomedical research to regulatory toxicology

Archives of Toxicology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский