BiScale-GTR achieves claimed state-of-the-art results on MoleculeNet, PharmaBench and LRGB by combining improved fragment tokenization with a parallel GNN-Transformer architecture that operates at both atom and fragment scales.
Pre-training molecular graph representation with 3d geometry.arXiv preprint arXiv:2110.07728
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NaFM is a pretrained foundation model for natural products using scaffold-focused contrastive learning and masked graph objectives that achieves SOTA on taxonomy classification, gene/microbial analysis, and virtual screening tasks.
FARM adds atomic-level functional group annotations to create FG-enhanced SMILES and FG graphs, trains them with masked language modeling and GNNs plus contrastive alignment, and reports state-of-the-art results on 8 of 13 MoleculeNet tasks.
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.
citing papers explorer
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BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning
BiScale-GTR achieves claimed state-of-the-art results on MoleculeNet, PharmaBench and LRGB by combining improved fragment tokenization with a parallel GNN-Transformer architecture that operates at both atom and fragment scales.
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Pretraining a Foundation Model for Small-Molecule Natural Products
NaFM is a pretrained foundation model for natural products using scaffold-focused contrastive learning and masked graph objectives that achieves SOTA on taxonomy classification, gene/microbial analysis, and virtual screening tasks.
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FARM: Enhancing Molecular Representations with Functional Group Awareness
FARM adds atomic-level functional group annotations to create FG-enhanced SMILES and FG graphs, trains them with masked language modeling and GNNs plus contrastive alignment, and reports state-of-the-art results on 8 of 13 MoleculeNet tasks.
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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
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Synergistic Benefits of Joint Molecule Generation and Property Prediction
Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.