AI-driven drug discovery platforms have been yielding impressive results. The first completely AI-driven drug is now in clinical trials and AI systems are being used to optimize the safety and efficacy of existing molecules. However, the performance of AI models remains limited by the amount of data they take in – the so-called data problem. Moreover, as AI models become more complex, their hunger for data becomes increasingly insatiable.
Solving The Data Problem For AI In Drug Discovery
While artificial intelligence has proven its value in drug discovery, for most companies, the power of their AI systems is only as strong as the data those systems are trained on. However, stakeholders – from individual companies to consortiums and service vendors -- are finding creative approaches to overcome the so-called data problem and strengthen their AI models.
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