Collaboration is key to AI-aided drug discovery
The current Covid-19 crisis highlights the need to rapidly identify new treatments for emerging diseases. But realigning large scale experimental efforts to changing circumstances cannot happen overnight. This pressing call for agility suggests we need to take a new approach to how we search and identify molecular cures and therapies.
Historically, drug discovery has relied on extensive experimental effort to map out the relevant part of the chemical space. Even with the advent of automated high-input screening methods, designing an effective assay for new diseases – and running it at scale – can take months.
Emerging artificial intelligence (AI) technologies have potential to accelerate and transform the search for molecular remedies, enabling rapid, large-scale identification of effective drug candidates. With this goal in mind, we started an initiative called AI Cures with the hope of lowering the barrier for people from varied backgrounds to get involved and contribute. By openly sharing data, critical analysis and methods, we hope to explore the many ways AI can help drug discovery. High scientific standards and open analysis can also mitigate the negative impact from possible false, early announcements.
The simplest task for AI algorithms is in silico property prediction. The algorithms learn to forecast outcomes of experimental assays, such as whether cells treated with a given compound are able to withstand viral infection, allowing large libraries of compounds to be scanned quickly and cheaply for potent candidates. The AI tools do require initial data to learn from, but the amount of data needed can be substantially less than a comprehensive experimental survey. Indeed, the algorithms are naturally opportunistic, seeking consistent statistical patterns that relate molecular features with measured outcomes. The clearer the signal in the initial data, the less is needed to predict or effectively simulate experimental outcomes. In our prior work on antibiotics, for example, an initial screen of only two thousand compounds sufficed for identifying promising candidates from much larger libraries containing around 100 million compounds.
AI tools can assist in the race to repurpose compounds that are already going through the clinical approval process. There are only on the order of 10,000 such compounds, some of them currently undergoing clinical trials, and much of the effort to screen and test their effectiveness against Covid-19 is already underway. The experimental outcomes for various viral inhibition screens do not agree very well with each other, however, leaving plenty of room for AI tools to reconcile differences in cell types, cell lines and experimental protocols.
Unfortunately, the single drug repurposing effort may not yield an effective treatment.
