The Impact of AI on Advancing Anti-Addiction Therapeutics

Abstract

Substance use disorders affect a large portion of the global population and continue to create major health challenges and current therapeutic strategies remain insufficient to address their complex neurobiological support. Traditional drug discovery methods are slow, expensive, and often ineffective in identifying novel anti-addiction agents. Artificial Intelligence (AI) has emerged as a transformative tool, offering advanced computational techniques such as machine learning (ML), deep learning (DL), natural language processing (NLP), and neural network based predictive modelling.AI-driven approaches enable the integration of large-scale biological, chemical, and clinical datasets to uncover hidden patterns underlying addiction-related pathways. ML and DL models can analyze genomic, proteomic, and neuroimaging data to identify novel molecular targets involved in reward circuitry, craving, and relapse mechanisms. NLP techniques further assist by mining vast biomedical literature, clinical trial repositories, and electronic health records to extract relevant insights and accelerate hypothesis generation. Additionally, reinforcement learning and generative models facilitate de novo molecular design by proposing chemically valid and biologically relevant compounds with improved efficacy and safety profiles. AI-based ADMET prediction reduces late-stage failures by optimizing pharmacokinetic and toxicity parameters early in the discovery pipeline. Collectively, these computational strategies significantly reduce time, cost, and attrition rates compared to conventional approaches, while enabling more personalized and mechanism-driven therapeutic development for substance use disorders. This paper explores the mechanisms, methodologies, and outcomes associated with AI-driven anti-addiction drug discovery. Case studies, datasets, and model outputs are discussed to demonstrate practical applications and future potential.