# DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing

arXiv: https://arxiv.org/abs/2407.02265 · HTML: https://mlsanity.com/paper/2407.02265/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [GraphEBM: Molecular Graph Generation with Energy-Based Models](https://mlsanity.com/paper/2102.00546/) (2021)
- [Differentiable Scaffolding Tree for Molecular Optimization](https://mlsanity.com/paper/2109.10469/) (2021)
- [MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization](https://mlsanity.com/paper/2010.02318/) (2020)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching](https://mlsanity.com/paper/2006.08765/) (2020)
- [CORE: Automatic Molecule Optimization Using Copy & Refine Strategy](https://mlsanity.com/paper/1912.05910/) (2019)
- [Uncertainty Quantification on Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2401.03482/) (2024)
- [TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction](https://mlsanity.com/paper/2404.13235/) (2024)
- [Differential Privacy for Stackelberg Games](https://mlsanity.com/paper/2002.00944/) (2020)

## Built on by (most cited first, 7 of 7)

- [DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning](https://mlsanity.com/paper/2410.02023/) (2024)
- [Accelerated Markov Chain Monte Carlo Using Adaptive Weighting Scheme](https://mlsanity.com/paper/2408.12888/) (2024)
- [How to Train Text Summarization Model with Weak Supervisions](https://mlsanity.com/paper/2409.00098/) (2024)
- [TrialSynth: Generation of Synthetic Sequential Clinical Trial Data](https://mlsanity.com/paper/2409.07089/) (2024)
- [3D Interaction Geometric Pre-training for Molecular Relational Learning](https://mlsanity.com/paper/2412.02957/) (2024)
- [Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform](https://mlsanity.com/paper/2508.15379/) (2025)
- [GPCR-Filter: a deep learning framework for efficient and precise GPCR modulator discovery](https://mlsanity.com/paper/2601.19149/) (2026)

## Abstract (arXiv)

Bringing a novel drug from the original idea to market typically requires more than ten years and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the approved drug to treat new diseases. The process is also known as drug repurposing or drug repositioning. Machine learning methods exhibited huge potential in automating drug repurposing. However, it still encounter some challenges, such as lack of labels and multimodal feature representation. To address these issues, we design DrugCLIP, a cutting-edge contrastive learning method, to learn drug and disease's interaction without negative labels. Additionally, we have curated a drug repurposing dataset based on real-world clinical trial records. Thorough empirical studies are conducted to validate the effectiveness of the proposed DrugCLIP method.