Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Drug discovery and development is a notoriously risky process with high failure rates at
every stage, including disease modeling, target discovery, hit discovery, lead optimization …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

Molecule design is a fundamental problem in molecular science and has critical applications
in a variety of areas, such as drug discovery, material science, etc. However, due to the large …

Drug toxicity prediction is an important step in ensuring patient safety during drug design
studies. While traditional preclinical studies have historically relied on animal models to …

Clinical trials are pivotal for developing new medical treatments, yet they typically pose
some risks such as patient mortality, adverse events, and enrollment failure that waste …

Clinical trials are indispensable for medical research and the development of new
treatments. However, clinical trials often involve thousands of participants and can span …

The importance of uncertainty quantification is increasingly recognized in the diverse field of
machine learning. Accurately assessing model prediction uncertainty can help provide …

Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to
identify potential drug candidates. Traditional methods, grounded in physicochemical …

Background: Clinical trial is a crucial step in the development of a new therapy (eg,
medication) and is remarkably expensive and time-consuming. Forecasting the approval of …