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

RİFAİOĞLU A. S. , Atas H., Martin M. J. , Cetin-Atalay R., Atalay V., Dogan T.

BRIEFINGS IN BIOINFORMATICS, cilt.20, ss.1878-1912, 2019 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 20 Konu: 5
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1093/bib/bby061
  • Sayfa Sayıları: ss.1878-1912


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 generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance.