ArticleThe journal of physical chemistry letters2021
Proteome-Informed Machine Learning Studies of Cocaine Addiction.
Article in The journal of physical chemistry letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- Computational Approach to Select Lead-Like Compounds From an Opioid Class of Novel Psychoactive Substances.Chemistry & biodiversity · 2026Article
- Artificial intelligence approaches for anti-addiction drug discovery.Digital discovery · 2025Review
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.Journal of chemical information and modeling · 2025Article
- Machine learning analysis of ARVC informed by sodium channel protein-based interactome networks.Frontiers in pharmacology · 2025Article
- Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling.Advances in neurobiology · 2025Review
- Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data.Journal of computational biophysics and chemistry · 2024Article
- ChatGPT in Drug Discovery: A Case Study on Anticocaine Addiction Drug Development with Chatbots.Journal of chemical information and modeling · 2023Article
- ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots.ArXiv · 2023Article
- Integrating transformer and autoencoder techniques with spectral graph algorithms for the prediction of scarcely labeled molecular data.Computers in biology and medicine · 2023Article
- Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models.Computers in biology and medicine · 2023Article
- Machine Learning Analysis of Cocaine Addiction Informed by DAT, SERT, and NET-Based Interactome Networks.Journal of chemical theory and computation · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstream and downstream of the dopamine transporter. However, it is difficult to study so many proteins with traditional experiments, highlighting the need for innovative strategies in the field. We propose a proteome-informed machine learning (ML) platform for discovering nearly optimal anti-cocaine addiction lead compounds. We analyze proteomic protein-protein interaction networks for cocaine dependence to identify 141 involved drug targets and build 32 ML models for cross-target analysis of more than 60,000 drug candidates or experimental drugs for side effects and repurposing potentials. We further predict their ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Our platform reveals that essentially all of the existing drug candidates fail in our cross-target and ADMET screenings but identifies several nearly optimal leads for further optimization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.