review · Briefings in Bioinformatics
Drug development frequently faces setbacks due to unexpected clinical side effects and cross-reactivity, often stemming from issues with selected drug targets. These problems arise from incomplete knowledge about targets and unpredictable drug interactions. Identifying and validating suitable drug targets, particularly for complex diseases, is a major challenge in drug discovery. This research provides a comprehensive overview of various computational methods and tools designed to predict and validate drug targets and drug-like molecules. It highlights their advantages, compares their effectiveness, and explores common reasons for drug failure. The aim is to guide researchers in selecting the most efficient computational approaches for their drug discovery programmes.
Improving drug discovery efficiency is crucial for developing new medicines and reducing the high failure rates in clinical trials. By providing a guide to effective computational methods, this research can help accelerate the identification of viable drug targets, potentially leading to safer and more successful treatments for various diseases.
This research provides a foundational review of computational methods for drug target and lead prediction, which could be highly valuable for pharmaceutical companies and biotechnology firms. It supports early-stage drug discovery programmes by guiding researchers in selecting efficient tools to identify promising drug candidates, thereby potentially reducing development costs and time. The abstract does not indicate a direct product or a specific near-market application, but rather a resource to improve existing research processes.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1093/bib/bbz103
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
