Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.

ABSTRACT: The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to gener

SUBMITTER: Rifaioglu AS 

PROVIDER: S-EPMC6917215 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature