Abstract:
Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and
cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate
centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways,
disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological
network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug
targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to
identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with
the fundamental stage being the identification and validation of drug targets of interest for further downstream processes.
Thus, various computational methods have been developed to complement experimental approaches in drug discovery.
Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets
and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective
methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and
opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during
the computational drug discovery process.
