NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products
Journal:
arXiv
Published Date:
Mar 22, 2025
Abstract
Natural products, as metabolites from microorganisms, animals, or plants,
exhibit diverse biological activities, making them crucial for drug discovery.
Nowadays, existing deep learning methods for natural products research
primarily rely on supervised learning approaches designed for specific
downstream tasks. However, such one-model-for-a-task paradigm often lacks
generalizability and leaves significant room for performance improvement.
Additionally, existing molecular characterization methods are not well-suited
for the unique tasks associated with natural products. To address these
limitations, we have pre-trained a foundation model for natural products based
on their unique properties. Our approach employs a novel pretraining strategy
that is especially tailored to natural products. By incorporating contrastive
learning and masked graph learning objectives, we emphasize evolutional
information from molecular scaffolds while capturing side-chain information.
Our framework achieves state-of-the-art (SOTA) results in various downstream
tasks related to natural product mining and drug discovery. We first compare
taxonomy classification with synthesized molecule-focused baselines to
demonstrate that current models are inadequate for understanding natural
synthesis. Furthermore, by diving into a fine-grained analysis at both the gene
and microbial levels, NaFM demonstrates the ability to capture evolutionary
information. Eventually, our method is experimented with virtual screening,
illustrating informative natural product representations that can lead to more
effective identification of potential drug candidates.
