Advances in the Use of Computational Methods in the Design of Targeted Pleiotropic Drugs

The multifactorial nature of diseases involving metabolism, cancer and neurodegeneration often requires the use of multiple drugs with widely variable side effect and pharmacokinetic profiles. Additionally, novel ways to disrupt biological activities have been realized through the use of synthetically pleiotropic compounds like PROTACs. However, designing multi-target coverage without encountering undue toxicity remains a significant challenge. This difficult task may be amenable to new techniques like machine learning and physics-based calculations like Free Energy Perturbation. This Research Topic serves to be a focal point for new research and novel reviews addressing the use of new computational techniques for the design of selective pleiotropic drugsThe goals of this Research Topic are as follows:1) Stimulate a collection of new perspectives on synthetic pleiotropism (e.g. PROTACs, molecular glues)2) Highlight new computational techniques for affording selective compounds that can still intentionally hit multiple targets3) Highlight new computational techniques for biological pathway deconvolution especially with respect to cross-pathway feedback4) Showcase new computational techniques for predicting pleiotropic compounds with good DMPKFor this Research Topic, we encourage the submission of mini-reviews and original publications concerning the following topics:1) Computational advances in pleiotropic target pathway discovery and validation2) Computational advances in engineering selectivity against a narrow set of biological targets3) Computational advances in the design of PROTACs, molecular glues and other compound families employing synthetic pleiotropism Topic editors: Zeno Apostolides, Thomas Kaiser, Pieter Burger Source: https://www.frontiersin.org/research-topics/67414/advances-in-the-use-of-computational-methods-in-the-design-of-targeted-pleiotropic-drugs