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OPDiv is an algorithm that optimizes the selection of diverse high-scoring compounds in virtual screening by using integer optimization to balance score and diversity, addressing the trade-off in cheminformatics pipelines.
PETA proposes a parameter-efficient framework for test-time adaptation in virtual screening, improving performance by updating only LayerNorm parameters during inference.
This paper benchmarks pretrained molecular language models for virtual screening and demonstrates that explicit domain adaptation improves their performance and sample efficiency across drug discovery, materials, and catalysis libraries.
This paper reveals that standard marginal conformal prediction fails to cover minority classes in imbalanced virtual screening datasets, and demonstrates that class-conditional (Mondrian) conformal prediction restores per-class coverage.
Introduces BoBa, a bandit-guided surrogate optimization framework that eliminates full-library inference by adaptively allocating computation across partitions of the action space, enabling efficient virtual screening of ultra-large chemical libraries.