Drug discovery
R&D for discovery of new drugs is time-consuming, expensive ($1–3bn) and a high-risk process that takes 12–18 years. From thousands of compounds tested in clinical trials, only a handful of candidates are commercialized.
Artificial Intelligence & Pharma
To reduce R&D costs, improve productivity, broaden molecular diversity and increase success rates, Pharma companies are looking at Artificial Intelligence (AI) and forge partnerships in the AI space: Pfizer & CytoReason, Sanofi & Exscientia, GSK & PathAI, BMS & Owkin.
However, despite increasing numbers of drugs discovered, there is no guarantee of success in clinical trials for AI-discovered compounds. AI is still in the infancy stage. It will probably take years for AI use to peak in drug discovery
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Botanicals & drug discovery
#Botanicals have been used as important sources for drug discovery and have contributed to modern medicine. Over 50% of all drugs are inspired by nature and we have tapped only a fraction of the potential of natural products: Plant-derived medicine offers an alternative knowledge hub for drug discovery.
The team around Dr. Giesbert Schneider, Professor of Computer-Assisted Drug Design at ETH Zurich, has demonstrated that AI is able to recognise the biological activity of natural products in a targeted manner and can thus be used to find new pharmaceutical applications for natural products.
Moreover, AI can help to find molecules that have the same effect as a natural substance but are easier to manufacture.
There are good reasons for Schneider’s focus on nature in the search for new pharmaceutical agents: "Most natural products are by definition potential active ingredients that have been selected via evolutionary mechanisms."
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