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Artificial intelligence; Drug discovery; Substance use disorders; Machine learning; Neural networks; Therapeutic development; Computational pharmacology; Target identification; Drug repurposing; Predictive modeling

Introduction

Substance use disorders (SUDs) remain a significant global health burden, contributing to considerable morbidity, mortality, and societal costs. Despite extensive research and public health efforts, effective pharmacological treatments for many forms of SUD—including those involving opioids, stimulants, alcohol, and nicotine—are still limited. Traditional drug discovery processes are time-consuming, costly, and often fail to identify compounds that are both safe and effective in clinical settings. These inefficiencies have led to an urgent call for innovative approaches that can accelerate the development of new therapies for addiction [1-5].

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is increasingly being adopted in biomedical research to transform drug discovery pipelines. AI has demonstrated the ability to process vast amounts of biological, chemical, and clinical data to identify novel drug candidates, predict pharmacokinetics and toxicity, and repurpose existing drugs. For SUDs, where the neurobiology is complex and multifactorial, AI offers a promising tool to uncover new therapeutic targets and tailor treatments to individual patient profiles.

This systematic review explores recent advances in AI-assisted drug discovery as applied to substance use disorders, highlighting emerging therapeutics identified through computational methods, novel algorithms used in addiction biology, and the challenges and opportunities in implementing these technologies in clinical practice [6-10].

Discussion

The application of AI in drug discovery encompasses multiple stages of the pipeline, from initial target identification to lead compound optimization and preclinical validation. In the context of SUDs, AI models are especially useful for navigating the complexity of neurobiological pathways involved in addiction—such as dopaminergic signaling, glutamate transmission, and stress response systems.

AI algorithms can analyze heterogeneous datasets, including genomics, proteomics, metabolomics, electronic health records (EHRs), and clinical trial data, to detect hidden patterns and associations. These insights facilitate the identification of molecular targets associated with addictive behaviors and withdrawal symptoms. Additionally, AI can predict drug-likeness, blood-brain barrier penetration, receptor affinity, and side-effect profiles with remarkable speed and accuracy, significantly reducing the need for early-stage experimental screening.

Several AI models—such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs)—have been successfully applied to predict molecular interactions and optimize chemical structures for brain-penetrant drugs. These models are increasingly used to identify compounds that may modulate key receptors implicated in SUD, such as the mu-opioid receptor, GABA receptors, and cannabinoid receptors.

Conclusion

Artificial intelligence is poised to revolutionize the field of drug discovery, offering new hope for the treatment of substance use disorders—a field long hampered by limited pharmacological options. Through rapid data analysis, predictive modeling, and molecular design, AI enables the identification of novel therapeutic candidates and the repurposing of existing drugs, while supporting the movement toward personalized treatment approaches.

The successful application of AI in this domain depends on overcoming critical challenges, including data quality, model interpretability, validation processes, and ethical considerations. However, the growing synergy between computational science and addiction medicine is already yielding promising results. As AI technologies mature and more comprehensive datasets become available, their impact on the development of effective, safe, and individualized treatments for substance use disorders will only deepen.

This systematic review underscores the potential of AI-assisted drug discovery to transform the landscape of addiction treatment, making the transition from generalized protocols to precision therapeutics a tangible and achievable goal.

References

1. Mohammad P, Nosratollah Z, Mohammad R, Abbas A Javad R, et al. (2010) . Afr J Biotechnol 9: 912-919.

   , ,

2. Whitehead Jr RD, Zhang M, Sternberg MR, Schleicher RL, Drammeh B, et al. (2017) Clin Biochem 50: 513-520.

   , ,

3. Ingram CF, Lewis SM (2000) J Clin Pathol 53: 933-937.

   , ,

4. Osborn ZT, Villalba N, Derickson PR, Sewatsky TP, Wagner AP, et al. (2019) Resp Care 64: 1343-1350.

   ,

5. Dybas J, Alcicek FC, Wajda A, Kaczmarska M, Zimna A, et al. (2022) Trend Anal Chem 146: 116481-116481.

   ,

6. Atkins CG, Buckley K, Blades MW, Turner RFB (2017) Appl Spectrosc 71: 767–793.

   , ,

7. Wood BR, Langford SJ, Cooke BM, Lim J, Glenister FK, et al (2004) J Am Chem Soc 126: 9233-9239.

   , ,

8. CLSI (2012) CLSI document EP17-A2 Wayne PA Clinical and Laboratory Standards Institute.

9. NCCLS (2003) NCCLS document EP6-A Wayne PA NCCLS.

   ,

10. NCCLS (2000) NCCLS document H15-A3 Wayne PA NCCLS.

    ,