We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry.
Gaussian processes have long been a cornerstone of probabilistic machine learning …

The concern of overconfident mis-predictions under distributional shift demands extensive
reliability research on Graph Neural Networks used in critical tasks in drug discovery. Here …

We present FlowMO: an open-source Python library for molecular property prediction with
Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables the user to make …

Bayesian optimization (BO) has become an indispensable tool for autonomous decision-
making across diverse applications from autonomous vehicle control to accelerated drug …

Large molecular representation models pre-trained on massive unlabeled data have shown
great success in predicting molecular properties. However, these models may tend to overfit …

Graph neural networks for molecular property prediction are frequently underspecified by
data and fail to generalise to new scaffolds at test time. A potential solution is Bayesian …

We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian
processes have long been a cornerstone of probabilistic machine learning, affording …

We have seen a rapid increase in the development of deep learning algorithms in recent
decades. However, while these algorithms have unlocked new business areas and led to …

化学の研究開発ではマテリアルズインフォマティクス (機械学習・深層学習を用いた新素材探索や
新材料設計) の技術が導入され始めています. 一方で, 有機化学・無機化学のどの領域かによって …