<p>Artificial intelligence (AI) has emerged as a powerful tool for solving real world problems across a wide range of industries and is increasingly being utilised by pharmaceutical companies to discover novel drug targets, biomarkers, and new drugs. Several AI-driven small molecules have entered clinical trials over the past few years, but their fate remains unknown. Currently, no commercially available compounds have been developed solely using AI approaches. In this perspective, we examine the current use of AI in drug discovery for neuropsychiatry. The pace of drug discovery in neuropsychiatric medicine has been generally sluggish, largely due to challenges such as poor pharmacological selectivity, the blood-brain barrier, and a limited understanding of disease mechanisms. AI may offer innovative solutions to these challenges. However, relative to fields such as oncology, the impact of AI on the discovery of neuropsychiatric drugs has been limited. Although novel AI-tools have been developed to overcome some of the challenges involved in neuropsychiatry drug discovery, their effectiveness has not been sufficiently evaluated. To date, innovative tools such as AlphaFold have been used to identify drug candidates for multiple neuropsychiatric conditions. AI-driven platforms have been used to study behavioural data from preclinical models to identify novel clinical candidates in clinical trials (e.g., ulotaront, phase III). It is anticipated that the availability of large-scale multi-omics data (‘big data’) will likely increase in the future, allowing us to gain a better understanding of gene-associated mechanisms in psychiatry. Using AI-based technologies such as AlphaFold, future pharmacological targets will be identified based on gene expression data, and large libraries of chemical compounds will be screened rapidly to identify novel drug candidates, resulting in shorter pre-clinical phase with lower costs.</p>