Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation

• URL: http://arxiv.org/abs/2311.02798v3
• Date: Sun, 12 Jan 2025 21:23:17 GMT
• Title: Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation
• Authors: Yue Wan, Jialu Wu, Tingjun Hou, Chang-Yu Hsieh, Xiaowei Jia,
• Abstract summary: This paper introduces a novel pre-training framework that learns robust and generalizable chemical knowledge. Our approach demonstrates competitive performance across various molecular property benchmarks.
• Score: 10.025809630976065
• License:
• Abstract: Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a novel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs.

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