Potential and Doubts: AI has the potential to reduce drug development time by half, but doubts exist regarding the reliability of AI tools in drug development.
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Case Study – Hepatocellular Carcinoma Cure:
- In 2022, Insilico Medicine, Stanford University, and the University of Toronto used AI tools to find a cure for Hepatocellular carcinoma.
- PandaOmics, an AI platform, shortlisted 20 potential targets from research papers.
- AlphaFold predicted protein 3D structures, and Chemistry42 designed 8,918 new chemicals.
- The entire process took just 30 days, a significant time reduction.
AI Benefits in Drug Development:
- Drug development typically costs $2.6 billion and takes around 12 years but can be significantly reduced with AI.
- AI companies complete drug discovery and preclinical stages in less than four years, whereas big pharma takes five to six years.
- Machine learning (ML) is widely used in drug discovery for data-driven predictions.
- AI models can analyze vast amounts of data from diverse sources beyond human capacity.
Limitations of AI in Pharma:
- Choosing the wrong target remains a challenge, as many potential drugs fail clinical trials due to incomplete disease understanding.
- Example: Drugs targeting beta-amyloid for Alzheimer’s have not succeeded despite significant investments.
- AI operates as a “black box,” not revealing the logic behind predictions, leading to misinterpretation and bias.
- Trust issues surround AI-created drugs among biologists and industry leaders.
Also Read: AI and Law: Legal Framework for AI in India