Variational graph encoders effectively combine graph convolutional networks with variational autoencoders, and have been widely employed for biomedical graph-structured data. Lam and colleagues developed a framework based on the variational graph encoder, NYAN, to facilitate the prediction of molecular properties in computer-assisted drug design. In NYAN, the low-dimensional latent variables derived from the variational graph autoencoder are leveraged as a universal molecular representation, yielding remarkable performance and versatility throughout the drug discovery process. In this study we assess the reusability of NYAN and investigate its applicability within the context of specific chemical toxicity prediction. The prediction accuracy—based on NYAN latent representations and other popular molecular feature representations—is benchmarked across a broad spectrum of toxicity datasets, and the adaptation of NYAN latent representation to other surrogate models is also explored. NYAN, equipped with common surrogate models, shows competitive or better performance in toxicity prediction compared with other state-of-the-art molecular property prediction methods. We also devise a multi-task learning strategy with feature enhancement and consensus inference by leveraging the low dimensionality and feature diversity of NYAN latent space, further boosting the multi-endpoint acute toxicity estimation. The analysis delves into the adaptability of the generic graph variational model, showcasing its aptitude for tailored tasks within the realm of drug discovery.

变分图编码器有效地将图卷积网络与变分自编码器相结合，并已广泛用于生物医学图结构数据。Lam 及其同事开发了一个基于变分图编码器 NYAN 的框架，以促进计算机辅助药物设计中分子特性的预测。在 NYAN 中，从变分图自动编码器得出的低维潜在变量被用作通用分子表示，在整个药物发现过程中产生卓越的性能和多功能性。在这项研究中，我们评估了 NYAN 的可重用性，并研究了其在特定化学毒性预测背景下的适用性。基于 NYAN 潜在表示和其他流行的分子特征表示的预测准确性在广泛的毒性数据集中进行了基准测试，并且还探讨了 NYAN 潜在表示对其他替代模型的调整。与其他最先进的分子特性预测方法相比，配备常见替代模型的 NYAN 在毒性预测方面显示出有竞争力或更好的性能。我们还利用 NYAN 潜在空间的低维和特征多样性，设计了一种具有特征增强和共识推理的多任务学习策略，进一步推动了多终点急性毒性估计。该分析深入研究了通用图变分模型的适应性，展示了它在药物发现领域内完成定制任务的能力。