> Neural networks in drug discovery: have they lived up to their promise?

     "Over the last decade neural networks have become an efficient method for data analysis in the field of drug discovery. The early problems encountered with neural networks such as overfitting and overtraining have been addressed resulting in a technique that surpasses traditional statistical methods. Neural networks have thus largely lived up to their promise, which was to overcome QSAR statistical problems. The next revolution in QSAR will no doubt involve research into producing better descriptors used in these studies to improve our ability to relate chemical structure to biological activity"

    > The use of neural networks in drug discovery started at the end of the 1980's when they were applied to various chemical problems including the study of quantitative structure–activity relationships (QSAR) 

2002.

https://link.springer.com/chapter/10.1007/3-540-45984-7_6

Not enough robots and silicon for you?

    > Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach

     In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure–activity and quantitative structure–property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.

Biologists aren't slouches. The thought about using computers to process biological data has been "obvious" for as long as there has been computers. AI and biology have been a thing since the Dartmouth conference. A lot of things that could have been tried have already been tried, but I guess we'll keep trying until something works?

There ain't much room for miracles here.