Table 1.

Classic ML methods to predict protein–ligand binding sites

SN	Approach	Techniques	Features	Database used	Year
1	Oriented Shell Model [73]	Support vector machine	Developed oriented shell model, utilizing distance and angular position distribution	Self-curated	2005
2	SitePredict [74]	Random forest	Predicted small ligand-binding sites mobilizing backbone structure	Self-curated	2008
3	LIBRUS [75]	Support vector machine	Combined ML and homology information for sequence-based ligand-binding residue prediction	Self-curated + FINDSITE’s database	2009
4	Qiu and Wang’s method [76]	Random forest	Used eight structural properties to train random forest classifiers, latter combined to predict binding residues	Q-SiteFinder’s dataset	2011
5	Wong et al.’s method [77]	Support vector machine + differential evolution	Classified the grid points with the location most likely to contain bound ligands	LigASite	2012
6	DoGSiteScorer [78]	Support vector machine	Web server for binding site prediction, analysis and druggability assessment	Self-curated	2012
7	Wong et al.’s method [79]	Support vector machine	Used SVM to cluster most probable ligand-binding pockets using protein properties	LigASite + self-curated	2013
8	TargetS [80]	Support vector machine + modified AdaBoost	Designed template-free predictor with classifier ensemble and spatial clustering	BioLip	2013
9	Wang et al.’s method [81]	Support vector machine + statistical depth function	SVM model integrating sequence and structural information	PDBbind	2013
10	LigandRFs [82]	Random forest	Applied random forest ensemble to identify ligand-binding residues from sequence information alone	CASP9 targets + CASP8 targets	2014
11	Suresh et al.’s method [83]	Naive Bayes classifier	Trained Naive Bayes classifier using only sequence-based information	Self-curated	2015
12	OSML [84]	Support vector machine	Proposed dynamic learning framework for constructing query-driven prediction models	BioLip + CASP9 targets	2015
13	PRANK [7]	Random forests	Developed mechanism to prioritize the predicted putative pockets	Astex Diverse set + self-curated	2015
14	UTProt Galaxy [85]	Support vector machine + neural network + random forest	Developed pipeline for protein–ligand binding site predictive tools using multiomics big data	Self-curated	2015
15	Chen et al.’s method [86]	Random forest	Proposed dynamic ensemble approach to identify protein–ligand binding residues by using sequence information	ccPDB + CASP9 targets + CASP8 targets	2016
16	Chen et al.’s method [87]	Random forest	Predicted allosteric and functional sites on proteins	PDBbind + allosteric DB + CATH DB	2016
17	TargetCom [88]	Support vector machine + modified AdaBoost algorithm	Designed ligand-specific methods to predict the binding sites of protein–ligand interactions by an ensemble classifier	BioLip	2016
18	P2Rank 2.1 [89]	Bayesian optimization	Improved version of P2Rank	Self-curated	2017
19	P2Rank [90]	Random forest	Built stand-alone template-free tool for prediction of ligand-binding sites	Self-curated	2018
20	PrankWeb [91]	Random forest	Online resource providing an interface to P2Rank	Self-curated	2019