Enhancing the Enrichment of Pharmacophore-Based Target Prediction

 

PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand–target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand–pharmacophore fit score. Two retrospective tests were performed which confirmed that the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reaction related off-targets.

 

J. Chem. Inf. Model. 2016. 56, 1175-1183

DOI: 10.1021/acs.jcim.5b00690

User Login


Database


PTID

Contact us


Honglin Li's Lab
Shanghai Key Laboratory of New Drug Design
School of Pharmacy
East China University of Sci. & Tech.
Room 527, Building 18, 130 Meilong Road,
Shanghai, 200237, P. R. China
Tel: (86) 21 6425 0213
Prof. Honglin Li
hlli@ecust.edu.cn

Copyright © 2024 Prof. HongLin Li's Group, School of Pharmacy, East China University of Science & Technology · All Right Reserved.
沪ICP备19004698号-1 |  沪公网安备31011302004713号