Molecular Representation Learning (MRL) has emerged as a powerful tool for
drug and materials discovery in a variety of tasks such as virtual screening
and inverse design. While there has been a surge of interest in advancing
model-centric techniques, the influence of both data quantity and quality on
molecular representations is not yet clearly understood within this field. In
this paper, we delve into the neural scaling behaviors of MRL from a
data-centric viewpoint, examining four key dimensions: (1) data modalities, (2)
dataset splitting, (3) the role of pre-training, and (4) model capacity. Our
empirical studies confirm a consistent power-law relationship between data
volume and MRL performance across these dimensions. Additionally, through
detailed analysis, we identify potential avenues for improving learning
efficiency. To challenge these scaling laws, we adapt seven popular data
pruning strategies to molecular data and benchmark their performance. Our
findings underline the importance of data-centric MRL and highlight possible
directions for future research.

从数据中心的观点出发，本文研究了分子表示学习的神经缩放行为，在数据量、数据质量和模型容量等四个关键维度上，证实了数据量和分子表示性能之间的一致性幂律关系，并通过详细分析，发现了提高学习效率的可能途径。