Finding new medicines is a lengthy and expensive process. Artificial intelligence promises to shorten this timeline by sifting through millions of chemical structures, predicting which molecules might bind to disease targets and simulating their effects before laboratory testing. In the lab, robots guided by machine learning synthesize and test compounds at high throughput, feeding results back into models that refine their search. AI also looks at existing drugs and identifies surprising new uses, a practice known as drug repurposing, which can bring therapies to patients faster because safety profiles are already known.
Drug discovery models build on familiar statistical techniques. Classification algorithms separate active from inactive compounds, while regression models predict potency, toxicity or pharmacokinetic properties. Clustering groups molecules by structural similarity, aiding scaffold hopping and diversity exploration. Quantitative structure–activity relationships (QSAR) use regression to map chemical descriptors to biological activity. Graph neural networks learn representations of molecules and proteins, capturing intricate bonding patterns. Reinforcement learning agents propose new structures and optimise them against multi‑objective criteria such as efficacy and solubility.
Practical successes are emerging. During the COVID‑19 pandemic, AI systems screened thousands of compounds and suggested candidates like baricitinib for repurposing, later validated in clinical trials. Biotech start‑ups design novel antibiotics and anticancer drugs by coupling generative models with wet‑lab feedback loops. Pharmaceutical companies use predictive models to triage virtual libraries and focus resources on promising leads. Beyond small molecules, AI aids in designing antibodies and peptides, assessing protein–protein interactions and guiding CRISPR gene‑editing strategies.
Despite excitement, challenges persist. Training data often come from proprietary assays and may contain biases or measurement noise. Models that perform well in silico can fail in vivo due to pharmacodynamics or off‑target effects. Intellectual property and data‑sharing restrictions limit collaboration and reproducibility. It is crucial to validate AI‑proposed candidates experimentally and to maintain transparency around algorithms and datasets. By integrating statistical rigour with domain expertise and ethical considerations, AI can accelerate drug discovery while prioritising safety and accessibility.
