  The appearance of a new dangerous and contagious disease requires the
development of a drug therapy faster than what is foreseen by usual mechanisms.
Many drug therapy developments consist in investigating through different
clinical trials the effects of different specific drug combinations by
delivering it into a test group of ill patients, meanwhile a placebo treatment
is delivered to the remaining ill patients, known as the control group. We
compare the above technique to a new technique in which all patients receive a
different and reasonable combination of drugs and use this outcome to feed a
Neural Network. By averaging out fluctuations and recognizing different patient
features, the Neural Network learns the pattern that connects the patients
initial state to the outcome of the treatments and therefore can predict the
best drug therapy better than the above method. In contrast to many available
works, we do not study any detail of drugs composition nor interaction, but
instead pose and solve the problem from a phenomenological point of view, which
allows us to compare both methods. Although the conclusion is reached through
mathematical modeling and is stable upon any reasonable model, this is a
proof-of-concept that should be studied within other expertises before
confronting a real scenario. All calculations, tools and scripts have been made
open source for the community to test, modify or expand it. Finally it should
be mentioned that, although the results presented here are in the context of a
new disease in medical sciences, these are useful for any field that requires a
experimental technique with a control group.
