In my opinion it is bioinformatics.which is simply under represented here. So, I feel, whilst it is of course polite to ask it is difficult to consider it in any other technical disapline.
I'm well aware of the field because it was one of the early runners in deep learning, which is one way to process the data in question (but far from an orthodox approach). So its same stuff vectorization of C- based compounds and the like, parameterisation, training, testing, retraining. Same old. It stems from the well trodden loop of try a compound, have a guess at tweaking the structure, retesting the new structure, non-target drug effects, tweak it again and say 'hey maybe some bioinformatics will make better guesses'.
In addition, protein structural modelling is part of the field and there is a long standing presence here from @MatteoF and @Greener(sp?) et al
"Pharmacodynamics given trial data" will be focused around its own set of statistical methods, which most of us will probably not hold expertise in, nor the packages that implement these statistical methods, but that's bioinformatics per se.
The two caveats I would hold are:
- If the question was cost-effectiveness of a given drug therapy, that isn't a biological processes so would be outside Bioinformatics SE. If for example, its focused around comparative efficacy .. that is biological.
- Finally, I also definitely get the impression this ain't singly medical statistics, this is computational biochemistry, possibly within a trial population. In medical stats the biology of compound X is almost irrelevant and that would be more appropriate for Cross-validation Stackexchange than Bioinformatics SE in my opinion.