We investigate Siamese networks for learning related embeddings for augmented
samples of molecular conformers. We find that a non-contrastive (positive-pair
only) auxiliary task aids in supervised training of Euclidean neural networks
(E3NNs) and increases manifold smoothness (MS) around point-cloud geometries.
We demonstrate this property for multiple drug-activity prediction tasks while
maintaining relevant performance metrics, and propose an extension of MS to
probabilistic and regression settings. We provide an analysis of representation
collapse, finding substantial effects of task-weighting, latent dimension, and
regularization. We expect the presented protocol to aid in the development of
reliable E3NNs from molecular conformers, even for small-data drug discovery
programs.

本研究探讨孪生网络用于学习与分子构象相关的嵌入式样本，并发现一种非对比式辅助任务可辅助监督学习和增加流形平滑度。我们在多项药物活性预测任务中展示了此属性，并提出将流形平滑度扩展到概率和回归设置。我们针对表征陷落进行了分析，发现任务加权、潜在维数和正则化等因素都有显著影响。我们期望提供的协议能帮助开发出可靠的 E3NNs 用于分子构象的小数据药物发现计划。