Contrastive KERMT pretraining on molecular graphs yields 7.6-9.9% average gains over KERMT baseline on Biogen, ExpansionRX, and ChEMBL-MT ADME endpoints via a single probabilistic latent-variable objective and task-specific GNN heads.
arXiv preprint arXiv:2212.02229 , year=
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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.
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.
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Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction
Contrastive KERMT pretraining on molecular graphs yields 7.6-9.9% average gains over KERMT baseline on Biogen, ExpansionRX, and ChEMBL-MT ADME endpoints via a single probabilistic latent-variable objective and task-specific GNN heads.
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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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AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.