Computer Aided Drug Design Approaches for Identification of Novel Autotaxin (ATX) Inhibitors

Title:Computer Aided Drug Design Approaches for Identification of Novel Autotaxin (ATX) Inhibitors

Volume: 23 Issue: 17

Author(s): Eleni Vrontaki, Georgia Melagraki, Eleanna Kaffe, Thomas Mavromoustakos, George Kokotos, Vassilis Aidinis and Antreas Afantitis

Affiliation:

Keywords: Autotaxin, ATX inhibitors, high throughput screening, virtual screening, binary QSAR, chemical libraries.

Abstract: Autotaxin (ATX) has become an attractive target with a huge pharmacological and pharmacochemical interest in LPA-related diseases and to date many small organic molecules have been explored as potential ATX inhibitors. As a useful aid in the various efforts of identifying novel effective ATX inhibitors, in silico methods can serve as an important and valuable tool. Especially, Virtual Screening (VS) has recently received increased attention due to the large datasets made available, the development of advanced VS techniques and the encouraging fact that VS has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. Different techniques and workflows have been reported in literature with the goal to prioritize possible potent hits. In this review article several deployed virtual screening strategies for the identification of novel potent ATX inhibitors are described.

Cite this article as:

Vrontaki Eleni, Melagraki Georgia, Kaffe Eleanna, Mavromoustakos Thomas, Kokotos George, Aidinis Vassilis and Afantitis Antreas, Computer Aided Drug Design Approaches for Identification of Novel Autotaxin (ATX) Inhibitors, Current Medicinal Chemistry 2016; 23 (17) . https://dx.doi.org/10.2174/0929867323666160321122228