Molecular Property Prediction (MPP) is a critical task in computational drug discovery, aimed at identifying molecules with desirable pharmacological and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Machine learning models have been widely used in this fast-growing field, with two types of models being commonly employed: traditional non-deep models and deep models.

Transformers may have sufficient supervision for generalization.

Since its inception in 1982, Oja's algorithm has become an established method for streaming principle component analysis (PCA).

In large-scale applications, datasets often contain billions of high-dimensional points. Grouping similar data points into clusters is crucial for understanding and organizing datasets.

We propose a method called clustered conformal prediction that clusters together classes having "similar" conformal