Tiffany Yesavage, PhD
The use of artificial intelligence (AI) to develop new drugs is in its infancy. Nevertheless, AI-designed drugs have been entering the early phases of clinical trials over the past couple of years. GEN recently interviewed experts from four pioneering companies about how they are harnessing AI for drug discovery.
The potential of AI
“It is estimated that 90% of drugs fail in clinical trials,” laments Stefan N. Lukianov, CEO of Salve Therapeutics. Nonetheless, he is optimistic that failure rates will fall as drug development benefits from AI. “AI and machine learning,” he declares, “represent an exciting new way to improve efficacy and safety and get more drugs to the market.”
Lukianov explains that AI refers to the general ability of computers to operate in ways that resemble human intelligence. He adds that “machine learning is a subcategory of AI that uses preexisting data to devise novel solutions to complex problems.” More specifically, machine learning enables computers to identify patterns in data, make decisions, and learn without direct instruction.
He recognizes that the use of AI and machine learning in drug discovery is still in an early phase, but he expects that AI-based approaches will be better than traditional approaches. AI-based drugs, he explains, can be engineered to possess “efficacy features” such as affinity, target specificity, and optimized binding. He notes that wet lab validation can then be used to ensure that only the best candidates will proceed to clinical trials.
Salve Therapeutics: Exploring the human virome with AI
On the subject of the human virome, Lukianov notes, “Every tissue in the human body has a range of co-evolved viruses.” He adds that specific tissues are the locations of rare genetic diseases that can disrupt tissue structure and function. “Therefore,” he concludes, “naturally occurring viruses in these tissues are an ideal way to carry gene therapy payloads to treat the disease.”
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