# BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening Source: [BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening]() Collection index: [llms.txt]() Data updated: 2026-02-19T05:00:04.488Z Authors: Anjie Qiao, Zhen Wang, Yaliang Li, Jiahua Rao, Yuedong Yang Published: 2026-02-16T22:26:55.000Z ## Abstract Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable virtual screening by embedding pockets and ligands into a shared space. However, our analyses indicate that such representations can be insensitive to fine-grained binding interactions and may rely on shortcut correlations in training data, limiting their ability to rank ligands by true binding compatibility. To address these issues, we propose BindCLIP, a unified contrastive-generative representation learning framework for virtual screening. BindCLIP jointly trains pocket and ligand encoders using CLIP-style contrastive learning together with a pocket-conditioned diffusion objective for binding pose generation, so that pose-level supervision directly shapes the retrieval embedding space toward interaction-relevant features. To further mitigate shortcut reliance, we introduce hard-negative augmentation and a ligand-ligand anchoring regularizer that prevents representation collapse. Experiments on two public benchmarks demonstrate consistent improvements over strong baselines. BindCLIP achieves substantial gains on challenging out-of-distribution virtual screening and improves ligand-analogue ranking on the FEP+ benchmark. Together, these results indicate that integrating generative, pose-level supervision with contrastive learning yields more interaction-aware embeddings and improves generalization in realistic screening settings, bringing virtual screening closer to real-world applicability. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Virtual Screening on LIT-PCBA (test)](): Virtual Screening; LIT-PCBA (test); AUROC: 59.15. - [Virtual Screening on DUD-E (test)](): Virtual Screening; DUD-E (test); AUROC: not recorded. ## Original sources and implementation [Original paper]() [PDF]()