Effective drug discovery begins with the right assay, but the definition of "right" will shift as technology advances. More often than not, "right" is the product of tribal knowledge, namely the traditions of one's close peer group, study lineage and corporate culture. Instead, the right assay should be a fit-for-purpose application born of a broader, continuously updated, and unbiased consensus. As Steve Hamilton, aka The Lab Man, at the Society for Laboratory Automation and Screening (SLAS) has often stated in his blog posts, "developing assays – properly – is the cornerstone for life sciences R&D."
Investments in artificial intelligence (AI) for drug discovery are surging. Big Pharmas are throwing big bucks at AI. Sanofi signed a 300 Million dollars deal with the Scottish AI startup Exscentia, and GSK did the same for 42 Million dollars. Also, the Silicon Valley VC firm Andreessen Horowitz launched a new 450 Million dollars bio investment fund, with one focus area in applications of AI to drug discovery.
In this craze, lots of pharma and biotech decision-makers wonder whether they should jump on the bandwagon, or wait and see.
Without a doubt, the area of artificial intelligence (AI) has been a sensation lately -- judging by the amount of hype around this topic. But the hype is not a guarantee of a real breakthrough, which is defined by facts and measurable achievements, not just loud statements.
The fact is, however, AI-driven systems managed to learn chess at a champion level in just 4 hours, and beat human champions in Jeopardy, and Go -- we all know that. And Facebook can recognize faces in a blurry photo where you are hanging out with a bunch of friends -- not worse than you (human) can do. You can try and see it for yourself anytime -- it works!
Choosing the right biological target or a combination of targets is a fundamental task for any successful drug discovery project. All the subsequent efforts -- be it a small molecule hit identification, lead optimization, pharmacokinetic studies, or a clinical trial -- will just be as effective, at the end of the day, as was the initial decision to choose one target or another.
The question is often raised, but the answer remains to be uncovered because the definition of drug "target" continues to evolve. Historical conceptualization is focused on catalytic sites, substrate binding sites, or epigenetic modification sites. Current understanding that protein-protein interactions are druggable, along with the emerging realization that "nodes" in signaling pathways and biological networks themselves can be manipulated with small molecules in non-traditional ways, has opened up new targeting options. This review is intended to provide a status update, and you can also access a list of 36 actionable web resources for target hunting.