The Limitations of GenAI in Drug Discovery — A Deep Dive

Generative AI promises transformative advances across many domains — image synthesis, language understanding, and automated design. Yet in drug discovery, GenAI often falls short of its hype. Despite impressive models and millions invested, real-world impact has been limited. Why GenAI Fails in Drug Discovery and How Semantic Data Fixes It In this article, we explore why GenAI struggles with core drug discovery tasks, the pitfalls that hinder its performance, and what must change for real success. 1. The Complexity of Biological Systems Unlike language or visual data, biological systems are: High-dimensional Non-linear Context-dependent Governed by complex chemistry and physics GenAI models trained on shallow or incomplete data cannot capture this complexity reliably. For instance: Small structural changes in molecules can drastically affect biological function. Multimodal data interactions (genomic, proteomic, phenotypic) are not well handled by vanilla generative architectures. 2. Da