CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics
Authors
G Colombo
F Marchetti
E Moroni
A Pandini
Publication date
12 April 2021
Publisher
'American Chemical Society (ACS)'
Doi
Abstract
© 2021 The Authors. Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein’s function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins.AIRC IG 2017 - ID. 20019 project; AIRC Fellowship; EC Research Innovation Action H2020 Programme Project HPC-EUROPA3 (INFRAIA-2016-1-730897)
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Sustaining member
Brunel University Research Archive
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:bura.brunel.ac.uk:2438/226...
Last time updated on 13/05/2021