In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …

Graph Neural Networks (GNNs) have shown great power in learning node representations
on graphs. However, they may inherit historical prejudices from training data, leading to …

Graph Neural Networks (GNNs) have been successfully adopted in recommender systems
by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless …

Graph Neural Networks (GNNs) have achieved unprecedented success in identifying
categorical labels of graphs. However, most existing graph classification problems with …

Recently, molecular relational learning, whose goal is to predict the interaction behavior
between molecular pairs, got a surge of interest in molecular sciences due to its wide range …

Self-supervised graph representation learning has recently shown considerable promise in
a range of fields, including bioinformatics and social networks. A large number of graph …

Predicting synergistic drug combinations can help accelerate discovery of cancer
treatments, particularly therapies personalized to a patient's specific tumor via biopsied cells …

Abstract Therapeutics Data Commons (tdcommons. ai) is an open science initiative with
unified datasets, AI models, and benchmarks to support research across therapeutic …

Early-stage drug discovery is highly dependent upon drug target evaluation, understanding
of disease progression and identification of patient characteristics linked to disease …

Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for
link prediction (LP). While numerous studies aim to improve the overall LP performance of …