Conventional drug discovery focuses on single protein targets and follows a “sequence, structure, and function” paradigm for selecting best protein targets to screen lead chemical compounds. This established paradigm simply avoids addressing directly the challenge of evaluating chemical toxicity and side effects until a later stage of drug discovery, resulting in inefficiencies and increased time and cost. We developed a new “network biology” perspective to assess proteins as potential drug targets using emerging biomolecular network data sets. To do so, we integrated several types of biological data for current drug targets from DrugBank, protein interaction data from the HAPPI and HPRD databases, literature co-citation data from PubMed, and side effects data from FDA-approved drug usage warnings. We used the Bayes factor and Positive Predictive Values to examine the use of certain network properties, such as network node degrees and essentiality, to predict candidate drug targets. We also developed a metric to evaluate a protein target’s overall side effects by taking into account aggregated side effect scores of all FDA-approved drugs targeting the protein. We discovered that non-essential protein with lower-to-medium network node degree could better serve as drug targets when combined with conventional protein function information. Integrated biomolecular associations, instead of physical interactions, are better sources for predicting drug targets with network biology methods. Our network biology framework presents exciting promises in developing better drug targets that lower the side-effects at later stages of drug development and help establish the field of “network pharmacology.”