Deep learning methods for molecular representation and property prediction

With advances in artificial intelligence (AI) methods, computer-aided drug design (CADD) has developed rapidly in recent years. Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contemporary applications of deep learning (DL) methods for molecular representation and property prediction. We categorize DL methods according to the format of molecular data (1D, 2D, and 3D). In addition, we discuss some common DL models, such as ensemble learning and transfer learning, and analyze the interpretability methods for these models. We also highlight the challenges and opportunities of DL methods for molecular representation and property prediction.

Automated summary

This review summarizes the recent applications of deep learning methods in molecular representation and property prediction. The DL methods are categorized according to the format of molecular data and common models like ensemble learning and transfer learning are discussed. The interpretability methods for these models are also analyzed, and the challenges and opportunities for DL methods in molecular representation and property prediction are highlighted.