Generative models have become one of the hottest areas in de-novo molecular design over just several years, basically revolutionizing our perception of what can be done with artificial intelligence in this area. One important aspect of generative models is that they can produce new quality hit molecules using combined data from various experimental and theoretical sources -- and output results rapidly. 

One notable drug discovery startup betting on deep learning and generative models for innovative drug design is Vancouver-based Variational AI.

Computer-aided drug design (CADD) is a central part of so-called “rational drug design”, pioneered in the last century by companies like Vertex. Over the last decades, CADD had great influence on the way new therapeutics are discovered, however, it also showed limitations due to modest accuracy of computational tools.  

The majority of software tools used for computational chemistry and biology rely on molecular mechanics -- a simplified representation of molecules, essentially reducing them down to “balls and sticks”: atoms and bonds between them. In this way it is easier to compute, but accuracy suffers greatly.

In order to gain adequate accuracy, one has to account for the electronic behavior of atoms and molecules, i.e. consider subatomic particles -- electrons and protons. This is what quantum mechanical (QM) methods are all about -- and the theory is not new, dating back to the early decades of the 20th century.