Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability

Abstract This study aims at improving upon existing activity predictions methods by augmenting chemical structure fingerprints with bio-activity based fingerprints derived from high-throughput screening (HTS) data (HTSFPs) and thereby showcasing the benefits of combining different descriptor types....

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Bibliographic Details
Main Authors: Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, Ola Engkvist
Format: Article
Language:English
Published: BMC 2019-08-01
Series:Journal of Cheminformatics
Subjects:
Machine learning
Random forest
High throughput screening
Activity prediction
HTSFP
ECFP
Online Access:http://link.springer.com/article/10.1186/s13321-019-0376-1

Internet

http://link.springer.com/article/10.1186/s13321-019-0376-1

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