Table 2.

Brief description about the common terminologies used in this mini-review.

Concept	Description
Druggability	The property of a druggable molecule (i.e., a biological target) by virtue of which it elicits a favorable clinical response when it contacts a drug-like compound
Systems Biology	Study of the complex biological systems using mathematical and computational modeling
Machine Learning	Subfield of computer science devoted to the development and utilization of algorithms that can learn from and make predictions on data
Network Measures	Numerical attributes used to describe the role and position of every node in a network
Ensemble algorithms	Collection of machine learning algorithms in which the final consensus prediction is made using results from each component algorithm
Support Vector Machines (SVM)	A model that takes the input training data and maps the data points in space and then tries to find a hyperplane that can be used to distinctly classify the data into their respective classes
Decision Tree	Machine learning algorithms based on decision support tools that make use of a graph of conditions and their possible consequences
Random Forest	Ensemble learning algorithm that combines results from multiple decision trees and output the consensus predictions
Closeness Centrality	Network measure that indicates how close each node is to every other node in the network
Betweenness Centrality	Fraction of shortest paths between all nodes passing through the given node