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 …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

GNNs and chemical fingerprints are the predominant approaches to representing molecules
for property prediction. However, in NLP, transformers have become the de-facto standard …

In recent years, the tendency of the number of financial institutions to include
cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital …

With the rapid progress of AI in both academia and industry, Deep Learning has been widely
introduced into various areas in drug discovery to accelerate its pace and cut R&D costs …

There has been a recent surge of interest in using machine learning across chemical space
in order to predict properties of molecules or design molecules and materials with the …

Motivation Drug discovery demands rapid quantification of compound–protein interaction
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …