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Article summary:

1. AI drug discovery is rapidly growing, with investments in the field increasing from $450 million in 2014 to $58 billion in 2021.

2. Traditional drug discovery is notoriously difficult and expensive, taking at least 10 years and costing $1.3 billion to bring a new drug to market.

3. AI has already helped identify promising candidate therapeutics, and it can be used for virtual screening of compounds, predictive modeling of drug efficacy and toxicity, identification of new targets for drug development, analysis of large-scale genomic data, optimization of drug dosing and treatment regimens, and predictive modeling of patient responses to treatment.

Article analysis:

The article provides an overview of how AI is being used in the field of drug discovery and its potential benefits. The article does not provide any evidence or sources for the claims made about the cost savings or speed improvements that AI can bring to the process. Additionally, there is no discussion about potential risks associated with using AI in this context such as bias or errors due to incorrect data input or algorithmic decisions. The article also fails to mention any potential drawbacks or limitations associated with using AI in this context such as lack of transparency or accountability when it comes to decision making processes. Furthermore, there is no discussion about possible counterarguments or alternative approaches that could be taken when it comes to developing drugs using AI technology. Finally, the article does not present both sides equally; instead it focuses solely on the positive aspects without exploring any potential negatives associated with using AI in this context.