3 ms·
There’s ~ 15K-20K diseases that we’ve classified. There’s 3.2Bn genetic base pairs. There’s just over 7Bn people. Maybe like 7M-20M people suffer from a dise
by kumarski 10y ago
There’s ~ 15K-20K diseases that we’ve classified.
There’s 3.2Bn genetic base pairs.
There’s just over 7Bn people.
Maybe like 7M-20M people suffer from a disease or even well below or above that.
Finding someone at a given time in the exact modality that would be testable by a given trial is really hard.
Genes are sets of base pairs.
A lot of drug discovery is a situation where you have to test compounds empirically against a notion of a theory of a viable drug target.
Some drugs may be efficacious, but they may kill you.
Some genes may work in coalition with other genes, not at all, or against some genes.
There's also the fundamental notion about the druggability of a disease. This article does a great job in outlining it: http://omicsomics.blogspot.com/2017/03/targets-drugability-revisited.html http://omicsomics.blogspot.com/2017/03/targets-drugability-r...
There are no monstrously hard computational power problems in drug development that are waiting for mathematical brains to pursue. There are hard Empirical problems. Empirical problems are problems which require actual experiments to show resultant data.
- letitgo12345 10y ago> There are no monstrously hard computational power problems in drug development that are waiting for mathematical brains to pursue Not sure if I would put it that way. For ex, in principle, you could use molecular dynamics and search to find protein sequences that form other proteins/antibodies that can hit targets you want or operate in a more complex fashion. So hard computational power can most definitely help out (and depending on level of advance, help out a great deal). Ofc that doesn't mean you don't need experiments at all.