The pharmaceutical industry is facing a crisis is R&D. About 50% of late-stage clinical trials fail due to ineffective drug targets, resulting in only 15% of drugs advancing from Phase 2 to approval. And researchers tend to coalesce around the same disease areas and targets.
Artificial intelligence can help expand the drug discovery universe by making predictions in more novel areas of biology and chemistry. By extracting text from scientific papers, AI can help identify relevant information faster and make links between biomedical entities, such as medicines and proteins, often with relatively little information.
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Despite the potential of artificial intelligence to identify new targets for disease faster, at lower cost, and with lower failure rates, adoption of this technology is still low. Trust has a significant role to play in that.
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I strongly believe that the best way for AI to become smarter, to be adopted by scientists, and therefore to make an impact is to have interdisciplinary teams developing this technology even as they test hypotheses in the lab to make the systems better able to learn. Enabling those feedback loops to improve the algorithms through testing their predictions and assumptions will also improve trust in artificial intelligence.
Read full, original post: AI will revolutionize drug discovery — but only if experts are involved
