Table 1. Advanced Computational Tools Used for Pharmacological Prediction and Target Identification in NP Drug Discovery.

tool	algorithm(s)	application(s)	availability	refs
PASS (Prediction of Activity Spectra for Substances)	Naive Bayes	Predicts 3500+ pharmacotherapeutic effects, modes of action, metabolic interactions, and specific toxicity for drug-like compounds from structural formula.	Commercial	Lagunin et al.39
SEA (similarity ensemble approach)	Kruskal algorithm of MST (minimum spanning tree)	Maps proteins based on chemical similarity between ligands.	Free	Keiser et al.40
SPiDER (self-organizing map-based prediction of drug equivalence relationships)	Self-organizing maps	Identifies innovative molecules, explores drug side effects, aids in drug repositioning.	Not disclosed	Reker et al.41
TiGER (target inference generator)	Multiple self-organizing maps	Qualitatively predicts up to 331 targets.	Few features are free, others require a subscription.	Schneider et al.42
DEcRyPT (drug–target relationship predictor)	RF	Deconvolves phenotypic hit targets, accurately predicts affinities.	Not disclosed	Rodrigues et al.43
STarFish (stacked ensemble target fishing)	k-Nearest neighbors, RF, Multilayer Perceptron, Logistic Regression	Considers small molecule binding to 1907 targets, with emphasis on NP target prediction.	Free	Cockroft et al.44