An iterative knowledge-based scoring function to predict protein–ligand interactions: II. Validation of the scoring function

2006 ◽  
Vol 27 (15) ◽  
pp. 1876-1882 ◽  
Author(s):  
Sheng-You Huang ◽  
Xiaoqin Zou
Keyword(s):  
Scoring Function ◽  
Knowledge Based ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions

2013 ◽  
Vol 19 (11) ◽  
pp. 5015-5030 ◽  
Author(s):  
Yingtao Liu ◽  
Zhijian Xu ◽  
Zhuo Yang ◽  
Kaixian Chen ◽  
Weiliang Zhu
Keyword(s):  
Scoring Function ◽  
Halogen Bonding ◽  
Knowledge Based ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Knowledge-based scoring function to predict protein-ligand interactions

2000 ◽  
Vol 295 (2) ◽  
pp. 337-356 ◽  
Author(s):  
Holger Gohlke ◽  
Manfred Hendlich ◽  
Gerhard Klebe
Keyword(s):  
Scoring Function ◽  
Knowledge Based ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

scholarly journals ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xujun Zhang ◽  
Chao Shen ◽  
Xueying Guo ◽  
Zhe Wang ◽  
Gaoqi Weng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Web Server ◽  
Scoring Functions ◽  
Protein Ligand Interactions ◽  
Prediction Module ◽  
Ligand Interactions ◽  
Benchmark Datasets

AbstractVirtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

scholarly journals Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors

2020 ◽  
Vol 21 (15) ◽  
pp. 5183 ◽  
Author(s):  
Eric D. Boittier ◽  
Yat Yin Tang ◽  
McKenna E. Buckley ◽  
Zachariah P. Schuurs ◽  
Derek J. Richard ◽  
...  
Keyword(s):  
Scoring Function ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Molecular Fingerprints ◽  
Biologically Relevant ◽  
Protein Ligand Interactions ◽  
Molecular Features ◽  
Ligand Interactions ◽  
Model Protein ◽  
Scoring Accuracy

A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca2+ concentrations. Recently, a small-molecule inhibitor of CD38 was shown to slow down pathways relating to aging and DNA damage. We examined the performance of seven docking programs for their ability to model protein-ligand interactions with CD38. A test set of twelve CD38 crystal structures, containing crystallized biologically relevant substrates, were used to assess pose prediction. The rankings for each program based on the median RMSD between the native and predicted were Vina, AD4 > PLANTS, Gold, Glide, Molegro > rDock. Forty-two compounds with known affinities were docked to assess the accuracy of the programs at affinity/ranking predictions. The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock. Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size. Factors that affect the reliability of pose prediction and scoring are discussed. General limitations and known biases of scoring functions are examined, aided in part by using molecular fingerprints and Random Forest classifiers. This machine learning approach may be used to systematically diagnose molecular features that are correlated with poor scoring accuracy.

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