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, Antreas Afantitis
Affiliation:
关键词:
Autotaxin；ATX抑制剂；高通量筛选；虚拟筛选；binary QSAR；化学库。
摘要: 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.
