Therapeutic Area Segment Analysis: Oncology (Largest Segment) vs Infectious Disease (Fastest-Growing Segment)

The oncology segment led the artificial intelligence (AI) in drug discovery market in 2026, accounting for a 26.33% share. Its strong position is supported by the complexity of cancer biology, the large number of potential therapeutic targets, and the need to identify effective drug candidates across diverse disease mechanisms. AI tools can accelerate target identification, molecular screening, candidate prioritization, and prediction of drug–target interactions, helping researchers manage large and complex datasets more efficiently. The growing emphasis on precision medicine and biomarker-driven therapeutic development further strengthens the role of AI in oncology research, where patient heterogeneity creates a strong need for more targeted and data-intensive discovery approaches.

Infectious disease represents the fastest-growing therapeutic area, driven by the need for accelerated identification of treatments against evolving pathogens and emerging disease threats. AI-based approaches can analyze biological and molecular datasets rapidly, supporting target discovery, compound screening, and prediction of potential therapeutic activity. The increasing importance of preparedness for emerging infections, antimicrobial resistance, and rapidly changing pathogen profiles is encouraging researchers to adopt computational methods that can shorten early discovery cycles and improve candidate selection. These capabilities are increasing the relevance of AI-enabled drug discovery across infectious disease research.

Application Segment Analysis: Drug Optimization and Repurposing (Largest Segment) vs Preclinical Testing (Fastest-Growing Segment)

Drug optimization and repurposing held the largest position in the artificial intelligence (AI) in drug discovery market in 2026. AI is particularly valuable in this application because it can integrate chemical, biological, and clinical information to identify opportunities for improving existing candidates or finding new therapeutic uses for previously studied compounds. Such approaches can support the evaluation of drug–target relationships, molecular properties, safety considerations, and potential treatment combinations, helping researchers make more informed development decisions. The ability to extract additional value from existing therapeutic knowledge is reinforcing demand for AI-assisted optimization and repurposing workflows.

Preclinical testing is the fastest-growing application, supported by increasing use of computational tools to improve candidate evaluation before clinical development. AI can assist researchers in predicting biological responses, identifying potential toxicity risks, analyzing experimental results, and prioritizing candidates for further investigation. Greater emphasis on improving development efficiency and reducing the likelihood of advancing unsuitable compounds is encouraging the integration of AI into preclinical workflows. As drug developers generate increasingly complex datasets across laboratory and biological studies, AI-driven analysis is becoming an important tool for strengthening candidate selection and experimental decision-making.