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Author

R. Miranda-Quintana

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Open access Sep 2026

How effective are contrastive learning-based approaches for activity-cliff prediction?

Tested across the MoleculeACE benchmarks, the results indicate that while contrastive losses prevent GNN mode collapse to some extent and drastically improve the property-based separation in the embedding space, their impact on absolute predictive accuracy remains highly dataset dependent.

Akash Surendran, R. Miranda-Quintana · 0 citations
Open access Sep 2026

Efficient visualization of chemical space

It is shown that basic properties of the metrics used in the original space can be used to infer the shape of the chemical space, which in turns suggests an optimal strategy to project chemical information to lower dimensions.

Akash Surendran, Krisztina Zsigmond, Le-Xin Chen et al. · 0 citations

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