Javier Pérez-Sianes, Horacio Pérez-Sánchez and Fernando Díaz* Pages 6 - 28 ( 23 )
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.
Drug discovery, virtual screening, structure-based virtual screening, ligand-based virtual screening, machine learning, deep learning.
Departamento de Informática, University of Valladolid, Valladolid, Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, Departamento de Informática, Escuela de Ingeniería Informática, University of Valladolid, Segovia