Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server

Abstract

BackgroundThe identification of target molecules is important for understanding the mechanism of “target deconvolution” in phenotypic screening and “polypharmacology” of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility; however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets.ResultsWe developed a ligand-based virtual screening model comprising 1121 target SAR models built using a random forest algorithm. The performance of each target model was tested by employing the ROC curve and the mean score using an internal five-fold cross validation. Moreover, recall rates for top-k targets were calculated to assess the performance of target ranking. A benchmark model using an optimized sampling method and parameters was examined via external validation set. The result shows recall rates of 67.6% and 73.9% for top-11 (1% of the total targets) and top-33, respectively. We provide a website for users to search the top-k targets for query ligands available publicly at http://rfqsar.kaist.ac.kr.ConclusionsThe target models that we built can be used for both predicting the activity of ligands toward each target and ranking candidate targets for a query ligand using a unified scoring scheme. The scores are additionally fitted to the probability so that users can estimate how likely a ligand–target interaction is active. The user interface of our web site is user friendly and intuitive, offering useful information and cross references.Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1960-x) contains supplementary material, which is available to authorized users.

Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives

“…[34]. e modeling process was done with the software Weka 3.8 [35] which offers several machine learning techniques, and the following regression techniques were used: multilinear regression (MLR), Smola and Scholkopf's algorithm for solving regression problem (SMOreg) [36], instancebased learning with parameter k (IBK) [37], and random forest (RF) [38,39], which are described briefly.…”

Improving the drug discovery process by using multiple classifier systems

“…In-silico screening (sometimes called virtual screening) has been addressed before with quite good results (Burbidge, Trotter, Buxton, & Holden, 2001;Lavecchia, 2015;Lee, Lee, & Kim, 2017). In (Lavecchia, 2015) a great review of the usage of different ML approaches for ligandbased and structure-based in-silico screening is introduced.…”

“…In (Lavecchia, 2015) a great review of the usage of different ML approaches for ligandbased and structure-based in-silico screening is introduced. Additionally, these works show the usage of some ML techniques including Support Vector Machines (used in (Burbidge et al, 2001)), decision trees (DT), ensemble methods (such as Adaboost or Random Forests used in (Lee et al, 2017)), Naïve Bayesian based approaches, K-Nearest Neighbor Methods (kNN) and Artificial Neural Networks (ANN, studied in (Burbidge et al, 2001)).…”

“…Inspired by the above ideas, we designed D2-MCS, a novel screening method able to outperform simple classifiers used in previous works (Burbidge et al, 2001;Lavecchia, 2015;Lee et al, 2017). Next section contains a detailed description of our proposal from the perspectives of software design and method operation.…”

A bioinformatics potpourri

“…drug target nodes or network biomarkers) controlling targets in disease-associated networks [ 27 ]. Drug-target interactions and drug efficacy Ligand-based quantitative structure-activity relationship modeling using Random Forest for drug target identification; web server application [ 28 ]. Whole-body physiologically based pharmacokinetic modelling using constraint-based perturbation analysis with cluster Newton method; can handle mixed patient-dependent and patient-independent parameters [ 29 ].…”