Drug discovery covers the whole field of the drug development process, from identifying chemical compounds that could become useful drugs to the clinical trial phase. The process is long and complicated, with every stage ripe for implementation of artificial intelligence models.
Implementation of AI drug discovery models simplifies and vastly shortens the process. Above all, a good model allows pharmaceutical companies to focus on drugs with the most promise. It also allows companies to minimize time and money spent on drugs that will later be disqualified.
Internal data for AI drug discovery models should contain data from all drugs previously created and marketed by the pharmaceutical company. The data should account for creation time. Further, it should record important insights from different steps in the process, including target identification, drug toxicity, clinical trials, and revenue from the drug.
Databases provide enormous amounts of information, especially biochemical, genomic, and toxicity data. The best AI model allows drug developers to easily cross-reference data from all these resources with the aforementioned internal data from previous work.
Other data which could prove useful to a drug discovery model include surveys and pharma company feedback. Additionally, developers should include clinical trial data and new academic studies.
AI-driven personalized medicine contains information on people’s genetic code and other intimate information. As a result, security is a major issue and the data must be secure and protected.
Finally, big data requires large amounts of computation for the AI models to work effectively. Consequently, drug developers need access to technology with high computational abilities.
In 2018 the flu vaccine ‘turbocharger’, developed by scientists from Flinders University in Australia, went into clinical trials. The team’s press release hailed it as the first drug designed by Artificial Intelligence.
“We had to teach the AI program on a set of compounds that are known to activate the human immune system, and a set of compounds that don’t work. The job of the AI was then to work out for itself what distinguished a drug that worked from one that doesn’t,” Petrovsky said, who is also the Research Director of Australian biotechnology company Vaxine.
“We then developed another program, called the synthetic chemist which generated trillions of different chemical compounds that we then fed to SAM so that it could sift through all of these to find candidates that it thought might be good human immune drugs.”
Advera Health Analytics Drug Safety Data & Services provides drug safety analytics.
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