Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Developing new drugs remains prohibitively expensive, time-consuming, and often involves
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …

Link prediction is a very fundamental task on graphs. Inspired by traditional path-based
methods, in this paper we propose a general and flexible representation learning framework …

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2
(SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of …

Drug discovery and development is a complex and costly process. Machine learning
approaches are being investigated to help improve the effectiveness and speed of multiple …

Machine learning on graphs has been extensively studied in both academic and industry.
However, as the literature on graph learning booms with a vast number of emerging …

Dynamic link prediction, which aims at forecasting future edges of a node in a dynamic
network, is an important problem in network science and has a wide range of real-world …

Drug repurposing is an approach to identify new medical indications of approved drugs. This
work presents a graph neural network drug repurposing model, which we refer to as …

Background Biomedical translational science is increasingly using computational reasoning
on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome …

Computational approaches have revolutionized the field of drug discovery, collectively
known as Computer-Assisted Drug Design (CADD). Advancements in computing power …