AI and Machine Learning: Streamlining and Focusing Clinical Trial Recruitment

AI and Machine Learning: Streamlining and Focusing Clinical Trial Recruitment

Artificial intelligence (AI) and machine learning are increasingly becoming a part of drug discovery and development beginning with identifying new compounds to structuring and designing clinical trials and targeting clinical trial populations.

A recent example came out of Linköping University in Sweden. The investigators utilized an artificial neural network to create maps of biological networks based on how different genes or proteins interact with each other. They leveraged a large database with information about the expression patterns of 20,000 genes in a large group of people. The AI was then taught to find patterns of gene expression.

And in mid-February, a drug developed using AI began testing in human clinical trials. The molecule, DSP-1181, is currently in Phase I clinical trials for obsessive-compulsive disorder. The compound is a long-acting potent serotonin 5-HT1A receptor agonist developed using AI that was part of a collaboration between Japan’s Sumitomo Dainippon Pharma and the UK’s Escientia. The AI developed the compound in about 12 months, compared to a more typical five-year process.


And only a week or so later, researchers at Massachusetts Institute of Technology (MIT) used a machine-learning algorithm to identify a new antibiotic. The computer model can screen more than 100 million compounds in a few days. It was programmed to select potential antibiotics that leverage different mechanisms of action than existing drugs.

For non-tech people, the difference between AI and machine learning isn’t terribly clear. Even when looking at the definitions, the differences aren’t all that clear. Generally speaking, AI is designed to simulate natural intelligence to solve complex problems and, seeming key to the definition, make decisions.

Machine learning, on the other hand, is where a machine, i.e., computer, can learn on its own without being explicitly programmed. It is an application of AI.

Another aspect of that is that AI will attempt to find the best solution, while machine learning will just try to find a solution, optimal or not.

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