AI is not the beginning of drug discovery — it is the accelerator
Artificial intelligence is often framed as the technology that suddenly changed drug discovery. But that narrative misses an important truth: computational, or in silico, drug discovery did not begin with AI. It has been part of pharmaceutical research for decades. Long before today’s large models and generative systems, scientists were already using molecular docking, QSAR […] The post AI is not…
Artificial intelligence is not a new technology in drug discovery; it has been part of pharmaceutical research for decades. Computational drug discovery methods, such as molecular docking, QSAR models, pharmacokinetic simulations, and virtual screening, have been used since the 1990s. This foundation exists because AI has been amplifying rather than replacing computational chemistry.
Traditional methods were limited by human bandwidth, but AI can analyze millions of chemical structures, identify hidden relationships, predict molecular properties, and improve as more data becomes available. This allows researchers to spend less time searching and more time validating the most promising candidates. This shift is already happening at companies like Isomorphic Labs, Insilico Medicine, Recursion, and NVIDIA.
Effective partnerships between pharmaceutical companies and AI firms are crucial, as each brings unique expertise to the table. While hospitals are slower to adopt AI due to regulatory concerns and workflow complexity, the technology's impact on drug discovery is undeniable. The future of AI in pharmaceuticals is about accelerating existing methods rather than starting from scratch.
Written by urgent.news from e27's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
