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An AI-Enabled Future

Artificial Intelligence’s Many Uses

Research and Development of Novel Drugs

Jean Lee, a PhD student at Melbourne's Doherty Institute, inspects the superbug Staphylcocus epidermidis on an agar plate in Melbourne on September 4, 2018. - A superbug resistant to all known antibiotics that can cause

By minimizing the lab-to-market time and ensuring the most scientifically grounded research methods, our Neuron Storage™ platform can significantly cut the costs of development thus leveling the playing field so that smaller companies or even startups can begin competing in a pharmaceutical industry notoriously known for stomping our competition from lesser-known up-and-coming companies. 

Currently, PubChem has on file over 240 million known compounds. As you can imagine, finding effective combinations of these compounds to engineer new drugs would be nearly impossible if done manually and without computerized assistance. 

AI tools can be trained to screen starting compounds, identify appropriate reactants, suggest modifications, and predict drug interactions that may cause an issue down the road. This is especially pertinent to the elderly population that often takes a cocktail of medications without considering the adverse effects of combined use. 

Exscientia, a British startup claims to have developed the world’s first pharmaceutical drug created using AI to make it to the human testing phase. Developed as an obsessive-compulsive disorder drug, conception to trial-ready capsule took just one year with testing having begun March of 2020. Beyond the creation of new compounds, the technology can mine through electronically published scientific literature and patient records to help repurpose existing drugs. 

Additionally, AI has been vital in tracking the spread of COVID-19 and helping dictate American policy on ventilator manufacturing, distribution of N95 face masks, and hospitalization triage. 

The use of AI holds great promise in screening high-risk individuals before they enroll in clinical trials thus ensuring safer drug testing. Intelligent computing holds the key to modernizing the patient selection process which still adheres to an antiquated trial-and-error approach. More specifically, mining and analyzing EMRs, medical imaging and ‘omics’ data (genomics, proteomics, metabolomics, etc.) can help researchers target candidates who can most safely participate and who appropriately mirror the patient population being tested. 

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INFO@NEURONSTORAGE.AI

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