Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework
X Zeng, H ** molecules with text for generative pre-training
Generative pre-trained Transformer (GPT) has demonstrates its great success in natural
language processing and related techniques have been adapted into molecular modeling …
language processing and related techniques have been adapted into molecular modeling …
Improving molecular contrastive learning via faulty negative mitigation and decomposed fragment contrast
Deep learning has been a prevalence in computational chemistry and widely implemented
in molecular property predictions. Recently, self-supervised learning (SSL), especially …
in molecular property predictions. Recently, self-supervised learning (SSL), especially …