# Proteome-informed machine learning studies of cocaine addiction

arXiv: https://arxiv.org/abs/2109.08718 · HTML: https://mlsanity.com/paper/2109.08718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 73 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)

## Built on by (most cited first, 4 of 4)

- [ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots](https://mlsanity.com/paper/2308.06920/) (2023)
- [Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data](https://mlsanity.com/paper/2211.06759/) (2022)
- [Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data](https://mlsanity.com/paper/2406.06479/) (2024)
- [Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia](https://mlsanity.com/paper/2501.02824/) (2025)

## Abstract (arXiv)

Cocaine addiction accounts for a large portion of substance use disorders and threatens millions of lives worldwide. There is an urgent need to come up with efficient anti-cocaine addiction drugs. Unfortunately, no medications have been approved by the Food and Drug Administration (FDA), despite the extensive effort in the past few decades. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstream and downstream of dopamine transporter (DAT) functions impacted by cocaine. However, traditional in vivo or in vitro experiments can not address the roles of so many proteins, highlighting the need for innovative strategies in the field. We propose a proteome-informed machine learning/deep learning (ML/DL) platform to discover nearly optimal anti-cocaine addiction lead compounds. We construct and analyze proteomic protein-protein interaction (PPI) networks for cocaine dependence to identify 141 involved drug targets and represent over 60,000 associated drug candidates or experimental drugs in the latent space using an autoencoder (EA) model trained from over 104 million molecules. We build 32 ML models for cross-target analysis of these drug candidates for side effects and repurposing potential. We further screen the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of these candidates. Our platform reveals that essentially all of the existing drug candidates, including dozens of experimental drugs, fail to pass our cross-target and ADMET screenings. Nonetheless, we have identified two nearly optimal leads for further optimization.