Department of Pharmacy, University of Pisa, 56126 Pisa, Italy.
Center for Biotechnology, Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA.
Molecules. 2021 Aug 24;26(17):5124. doi: 10.3390/molecules26175124.
In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.
计算机虚拟靶标钓取，旨在为查询分子识别可能的蛋白质靶标，是一种新兴的药物发现方法，由于其广泛的应用而备受关注。该策略可以阐明其靶标仍未知的化合物的作用机制和生物活性。此外，靶标钓取可用于鉴定候选药物的脱靶，从而识别并预防其可能的不良反应。基于这些原因，靶标钓取越来越成为多药理学、药物再利用和新药物靶标识别的关键方法。虽然实验性靶标钓取可能漫长且难以实施，因为单一小分子与不同蛋白质靶标之间可能发生大量相互作用，但计算机虚拟方法可以更快、更经济、更高效地针对特定蛋白质结构，因此更容易实施。此外，它可以与对接和虚拟筛选研究相结合，以及最近开发的越来越多的基于网络的工具，使靶标钓取成为药物发现更具吸引力的方法。值得特别强调的是，机器学习在该领域的应用越来越多，既是主要的靶标钓取方法，也是已应用策略的进一步发展。本综述报告了过去几年中开发和应用的基于配体和基于受体的主要计算机虚拟靶标钓取策略，特别关注科学界可免费访问的不同网络工具，用于进行靶标钓取研究。
