Federated learning (FL) provides a distributed machine learning system that enables
participants to train using local data to create a shared model by eliminating the requirement …

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by
supporting and contributing to the evidence-based practice of medicine, personalizing …

Federated multipartner machine learning has been touted as an appealing and efficient
method to increase the effective training data volume and thereby the predictivity of models …

Streamlined data-driven drug discovery remains challenging, especially in resource-limited
settings. We present ZairaChem, an artificial intelligence (AI)-and machine learning (ML) …

In the early 2000s pharmaceutical drug discovery was beginning to use computational
approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also …

While machine learning (ML) research has recently grown more in popularity, its application
in the omics domain is constrained by access to sufficiently large, high-quality datasets …

Early in silico assessment of the potential of a series of compounds to deliver a drug is one
of the major challenges in computer-assisted drug design. The goal is to identify the right …

In the field of drug discovery and pharmacology research, precise and rapid prediction of
drug-target binding affinity (DTA) and drug-drug interaction (DDI) are essential for drug …

Federated learning (FL) provides a privacy-preserving approach for collaborative training of
machine learning models. Given the potential data heterogeneity, it is crucial to select …

Recent advancements in multimodal machine learning have empowered the development
of accurate and robust AI systems in the medical domain, especially within centralized …