Background:
Automated compound testing is currently the de facto standard method for drug
screening, but it has not brought the great increase in the number of new drugs that was expected. Computer-
aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement
or even alternative to the robotic drug discovery. There are different methods and approaches to
address this problem and most of them are often included in one of the main screening strategies. Machine
learning, however, has established itself as a virtual screening methodology in its own right and it may
grow in popularity with the new trends on artificial intelligence.
Objective:
This paper will attempt to provide a comprehensive and structured review that collects the most
important proposals made so far in this area of research. Particular attention is given to some recent developments
carried out in the machine learning field: the deep learning approach, which is pointed out as a
future key player in the virtual screening landscape.
A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources
