# DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening

arXiv: https://arxiv.org/abs/2310.06367 · HTML: https://mlsanity.com/paper/2310.06367/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference](https://mlsanity.com/paper/2005.07704/) (2020)
- [CoSP: Co-supervised pretraining of pocket and ligand](https://mlsanity.com/paper/2206.12241/) (2022)

## Built on by (most cited first, 28 of 28)

- [A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications](https://mlsanity.com/paper/2403.00485/) (2024)
- [PharmAgents: Building a Virtual Pharma with Large Language Model Agents](https://mlsanity.com/paper/2503.22164/) (2025)
- [MolBind: Multimodal Alignment of Language, Molecules, and Proteins](https://mlsanity.com/paper/2403.08167/) (2024)
- [MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents](https://mlsanity.com/paper/2605.27853/) (2026)
- [FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction](https://mlsanity.com/paper/2406.01651/) (2024)
- [Data Leakage and Redundancy in the LIT-PCBA Benchmark](https://mlsanity.com/paper/2507.21404/) (2025)
- [AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation](https://mlsanity.com/paper/2506.05768/) (2025)
- [Learning Protein-Ligand Binding in Hyperbolic Space](https://mlsanity.com/paper/2508.15480/) (2025)
- [S2Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening](https://mlsanity.com/paper/2511.07006/) (2025)
- [KGOT: Unified Knowledge Graph and Optimal Transport Pseudo-Labeling for Molecule-Protein Interaction Prediction](https://mlsanity.com/paper/2512.09365/) (2025)
- [BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening](https://mlsanity.com/paper/2602.15236/) (2026)
- [PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching](https://mlsanity.com/paper/2409.06316/) (2024)
- [Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery](https://mlsanity.com/paper/2508.01799/) (2025)
- [From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models](https://mlsanity.com/paper/2512.10867/) (2025)
- [DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking](https://mlsanity.com/paper/2410.11224/) (2024)
- [ReactEmbed: A Plug-and-Play Module for Unifying Protein-Molecule Representations Guided by Biochemical Reaction Networks](https://mlsanity.com/paper/2501.18278/) (2025)
- [A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery](https://mlsanity.com/paper/2503.04362/) (2025)
- [GenShin: Guiding Rational Liposome Design by Ranking Liposomal Protein Corona through a Docking-Pose-Free GNN](https://mlsanity.com/paper/2504.13853/) (2025)
- [Flow-Based Fragment Identification via Binding Site-Specific Latent Representations](https://mlsanity.com/paper/2509.13216/) (2025)
- [Structure-Aware Contrastive Learning with Fine-Grained Binding Representations for Drug Discovery](https://mlsanity.com/paper/2509.14788/) (2025)
- [SubDyve: Subgraph-Driven Dynamic Propagation for Virtual Screening Enhancement Controlling False Positive](https://mlsanity.com/paper/2509.16273/) (2025)
- [Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration](https://mlsanity.com/paper/2510.07035/) (2025)
- [Latent Retrieval Augmented Generation of Cross-Domain Protein Binders](https://mlsanity.com/paper/2510.10480/) (2025)
- [Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design](https://mlsanity.com/paper/2601.09693/) (2026)
- [h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network](https://mlsanity.com/paper/2604.23134/) (2026)
- [Site4Drug: Predicting Drug-Binding Target Sites with an AI Agent](https://mlsanity.com/paper/2606.01816/) (2026)
- [Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening](https://mlsanity.com/paper/2609.24302/) (2026)
- [One Sequence, Many Decodings: CAGenMol-2 Recasts Drug Design as Masked Molecular Inference](https://mlsanity.com/paper/2609.34301/) (2026)

## Abstract (arXiv)

Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional docking methods are highly time-consuming, and can only work with a restricted search library in real-life applications. Recent supervised learning approaches using scoring functions for binding-affinity prediction, although promising, have not yet surpassed docking methods due to their strong dependency on limited data with reliable binding-affinity labels. In this paper, we propose a novel contrastive learning framework, DrugCLIP, by reformulating virtual screening as a dense retrieval task and employing contrastive learning to align representations of binding protein pockets and molecules from a large quantity of pairwise data without explicit binding-affinity scores. We also introduce a biological-knowledge inspired data augmentation strategy to learn better protein-molecule representations. Extensive experiments show that DrugCLIP significantly outperforms traditional docking and supervised learning methods on diverse virtual screening benchmarks with highly reduced computation time, especially in zero-shot setting.