I think that new approaches to doing drug R&D are always welcome and if you know of any particular interesting and useful approaches id love to learn more. The

I think that new approaches to doing drug R&D are always welcome and if you know of any particular interesting and useful approaches id love to learn more. The "bottoms up" approach you mention seems like it could be really powerful and definitely complementary to whats being done now. I have however seen a lot of approaches "sort of" like that not work that well. I also know of a lot of less quantitative approaches taht actually work really well, and thats how a lot of R&D is done today I disagree w the statement that we'll never cure cancer w/o info on mutation rates. Many of the most impactful medicines we have were discovered without this kind of low level, quantitative info. Many through serendipity. Penicillin is the classic example. We have actually made huge progress in treating many cancers in the last five years. We can turn many melanomas from death sentences into relatively manageable chronic diseases, and a lot of blood cancers are being completely eradicated in some patients (at least for a time). We have a long way to go, but most of these insights come from triangulating various pieces of molecular biological and chemical information to create testable insights There has been a move lately to more "genomic" medicine where you identify a specific genentic mutation, and target that to treat disease. Most diseases aren't that simple though, and these are rarely curative There was also a move in the early 2000s to measure expression levels of hundreds of thousands of cancer genes to try to understand which ones would metastasize, how to treat them, etc. In some cases algorithms got pretty good at predicting rates of metastasis, but most didnt, and even fewer yielded clinically actionable insights. And I dont know of any drugs that were developed based on this work The idea of the moment is the "liquid biopsy", measuring pieces of DNA or proteins in the blood, and using machine learning to correlate these "biomarkers" with risk of getting cancer or other endpoints. The jury is still out on whether this works to my knowledge, though frankly i havent followed the science too closely No one is giving up, not in the least -- in fact more money is being invested into drug research than ever before. Its just really hard, and "triangulation" and insight are more powerful right now than purely quantitative methods. I hope that changes. But for things to change, we probably need better tools for collecting data before algorithms could be really powerful