Discovering a new drug used to take more than a decade of research and billions of pounds of investment. Many candidates fail along the way, with the pharma industry spending an estimated $50 to $60bn/year on failed cancer drug trials alone[1]. However, AI algorithms can cut the time needed to identify a lead compound to a matter of weeks, if not days.
‘The first step in drug discovery is identifying a ‘hit’ or lead compound, and that has been entirely solved by AI,’ says Michele Vendruscolo, Professor of Biophysics at the University of Cambridge, UK.
Traditionally, drug discovery starts with ‘high throughput’ screening of thousands of chemical compounds to find which ones, if any, bind to and interact with the target protein, which can take up to two years. AI can dramatically speed up this process. Chemical libraries contain information about hundreds of thousands of molecules, including their chemical structures and physical characteristics.
Based on this information, AI models can predict how a molecule will bind to and interact with its target protein, by rapidly analysing vast quantities of data. Millions of potential candidates can be screened, allowing researchers to focus on the most promising leads.
‘We now have decades of results from X ray crystallography and micro spectroscopy studies,’ Vendruscolo explains. ‘We know the structure of hundreds of thousands of proteins. AI is very good at learning from that huge amount of structural data.’
Higher throughput
According to Vendruscolo, AI is now as accurate as experimental screening methods at predicting protein structures and how small molecules interact with them. However, so far this has only proved true for proteins that undergo a folding process to form 3D stable structures with binding pockets into which small molecules can fit.
‘The textbook definition of proteins is that they fold into their native state and then they function,’ says Vendruscolo. ‘But it turns out that about one third of human proteins don’t do that.’
These other ‘intrinsically disordered’ proteins don’t acquire a single conformation, so their structures cannot be determined by standard experimental and computational methods. Many are involved in human diseases, including neurodegenerative conditions such as Alzheimer’s and Parkinson’s. Until recently these proteins were considered ‘undruggable’.
‘The traditional concept of binding in drug discovery is called lock and key, where you have pockets in the surface of the protein which are like locks, and then the small molecule can fit into the crevice and that is like a key,’ says Vendruscolo. ‘But if the protein doesn’t [have a defined structure] there is no lock and no key. There are no approved drugs for clinical use that target dissolved proteins because there are no pockets.’
Vendruscolo and his team have used AI to identify a new way of binding, in which the small molecules don’t need a pocket. The team focused on amyloid beta, an intrinsically disordered protein implicated in Alzheimer’s disease. Clumps of amyloid beta form structures called plaques, which accumulate around neurons, causing them to die.
In soon-to-be published work, the team used AI to quickly screen a chemical library containing millions of small molecules and identified five compounds for further investigation. These compounds don’t bind to a pocket but rather ‘dance around’ the disordered amyloid beta protein before binding to the protein molecules, stabilising them and stopping them from clumping together.
‘The rules for disordered bindings are likely to be far, far more complex than the rules for lock and key,’ says Vendruscolo. ‘However, AI programs based on deep neural networks with trillions of parameters could learn to understand them.’
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