Transformers for molecular property prediction: Lessons learned from the past five years

arXiv:2404.03969v1 Announce Type: new
Abstract: 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 and chemical properties and molecular fingerprints in statistical models and classical machine learning to advanced deep learning approaches. In this review, we aim to distill insights from current research on employing transformer models for MPP. We analyze the currently available models and explore key questions that arise …

abstract advanced arxiv computational cs.cl cs.lg discovery diverse drug discovery environmental environmental science fingerprints five lessons learned machine machine learning prediction property protection q-bio.qm science simple statistical transformers type vital