Meta-MolNet: A Cross-Domain Benchmark for Few Examples Drug Discovery

作者：Qiujie Lv, Guanxing Chen, Ziduo Yang, Weihe Zhong, Calvin Yu‐Chian Chen · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2024 · DOI：10.1109/tnnls.2024.3359657 · 被引用次数：37 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Materials Science、Machine Learning in Bioinformatics

Predicting the pharmacological activity, toxicity, and pharmacokinetic properties of molecules is a central task in drug discovery. Existing machine learning methods are transferred from one resource rich molecular property to another data scarce property in the same scaffold dataset. However, existing models may produce fragile and highly uncertain predictions for new scaffold molecules. And these models were tested on different benchmarks, which seriously affected the quality of their evaluation results. In this article, we introduce Meta-MolNet, a collection of data benchmark and algorithms, which is a standard benchmark platform for measuring model generalization and uncertainty quantification capabilities. Meta-MolNet manages a wide range of molecular datasets with high ratio of molecules/scaffolds, which often leads to more difficult data shift and generalization problems. Furthermore, we propose a graph attention network based on cross-domain meta-learning, Meta-GAT, which uses bilevel optimization to learn meta-knowledge from the scaffold family molecular dataset in the source domain. Meta-GAT benefits from meta-knowledge that reduces the requirement of sample complexity to enable reliable predictions of new scaffold molecules in the target domain through internal iteration of a few examples. We evaluate existing methods as baselines for the community, and the Meta-MolNet benchmark demonstrates the effectiveness of measuring the proposed algorithm in domain generaliza...