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

TL;DR

BindCLIP is proposed, a unified contrastive-generative representation learning framework for virtual screening that achieves substantial gains on challenging out-of-distribution virtual screening and improves ligand-analogue ranking on the FEP+ benchmark.

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.

Figures (8)

• Figure 1: Illustrative probe cases from the test set.
• Figure 2: Overview of the BindCLIP framework. (a) Pocket-conditioned ligand binding pose generation objective; (b) Contrastive learning objective; (c) Hard-negative augmentation workflow.
• Figure 3: Visualization of pocket, ligand, and hard-negative embeddings with/without the anchoring regularizer (t-SNE).
• Figure 4: Out-of-distribution evaluation on the MF-PCBA subset, excluding assays whose proteins have more than 30% sequence identity with any protein in the training set.
• Figure 5: Evaluation on the 4-target FEP subset (CDK2, TYK2, JNK1, and P38) for ligand activity ranking within a chemical series.