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Artificial Intelligence in Drug Discovery and Development
Context:
AlphaFold’s 2024 Nobel Prize win underscores AI’s potential to revolutionise drug development by predicting protein structures and designing new ones.
More on News:
- AI-generated drugs have not significantly impacted the 90% clinical trial failure rate.
- Experts see AI as a useful tool, not a magic solution.
- AI aims to cut time (10–15 years) and cost ($1–2 billion) for drug development.
Faster Preclinical Processes:
- AI startups reduced preclinical testing from 3–6 years to 30 months.
- Tools like AlphaFold help design drugs targeting specific proteins, though structural precision challenges remain.
- Small, low-quality datasets limit AI’s effectiveness compared to fields like image analysis. Generating drug-related datasets on cells, animals, or humans for millions to billions of compounds is a significant challenge.
Root Cause Analysis:
- AI models can predict drug performance using features like binding specificity and tissue concentration.
- Phase 0+ trials are proposed for early, low-cost drug testing.
- Communication gaps between AI experts and drug developers hinder systemic improvements.
- Like AI, past innovations (e.g., the Human Genome Project) improved processes but did not reduce failure rates.
Challenges and Limitations:
- Precision Issues: While AlphaFold has made significant strides in protein structure prediction, its ability to design drugs remains uncertain, as minor structural changes can drastically impact a drug’s efficacy.
- Survivorship Bias: There’s a tendency to focus on improving specific aspects of drug properties, neglecting the root causes of drug failure. This bias may lead to incremental improvements rather than transformative breakthroughs.
Ethical and Regulatory Needs:
- AI raises safety and ethical concerns, requiring updated regulatory frameworks.
- AI could accelerate affordable treatments for neglected diseases, addressing global health disparities.
