BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening

TL;DR

BindCLIP introduces a unified contrastive-generative framework for virtual screening that enhances interaction-aware ligand-pocket representations, improving out-of-distribution performance and ligand ranking accuracy.

Contribution

It combines contrastive learning with a pose-generation objective and novel regularizers to produce more accurate and generalizable virtual screening embeddings.

Findings

Improves virtual screening accuracy on public benchmarks.

Enhances out-of-distribution ligand ranking performance.

Achieves better interaction-relevant embedding representations.

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.