Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties
has been highlighted as a promising strategy for drug design. Molecular docking program--a …

Characterizing uncertainty in machine learning models has recently gained interest in the
context of machine learning reliability, robustness, safety, and active learning. Here, we …

Elucidating and accurately predicting the druggability and bioactivities of molecules plays a
pivotal role in drug design and discovery and remains an open challenge. Recently, graph …

Graph classification has a wide range of applications in bioinformatics, social sciences,
automated fake news detection, web document classification, and more. In many practical …

Deep learning models that leverage large datasets are often the state of the art for modelling
molecular properties. When the datasets are smaller (< 2000 molecules), it is not clear that …

It is insightful to report an estimator that describes how certain a model is in a prediction,
additionally to the prediction alone. For regression tasks, most approaches implement a …

Artificial Intelligence applications gradually move outside the safe walls of research labs and
invade our daily lives. This is also true for Machine Learning methods on Knowledge …

Various computational methods have been developed for quantitative modeling of organic
chemical reactions; however, the lack of universality as well as the requirement of large …

Drug-likeness prediction is important for the virtual screening of drug candidates. It is
challenging because the drug-likeness is presumably associated with the whole set of …