A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia

Abstract

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. However, it is not suitable for dealing with the conflicts between targets or between drugs. With the release of high-precision protein structure prediction artificial intelligence, large-scale high-precision protein structure prediction and docking have become possible. In this article, we propose a multi-target drug discovery method by the example of therapeutic hypothermia (TH). First, we performed protein structure prediction for all protein targets of each group by AlphaFold2 and RoseTTAFold. Then, QuickVina 2 is used for molecular docking between the proteins and drugs. After docking, we use PageRank to rank single drugs and drug combinations of each group. The ePharmaLib was used for predicting the side effect targets. Given the differences in the weights of different targets, the method can effectively avoid inhibiting beneficial proteins while inhibiting harmful proteins. So it could minimize the conflicts between different doses and be friendly to chronotherapeutics. Besides, this method also has potential in precision medicine for its high compatibility with bioinformatics and promotes the development of pharmacogenomics and bioinfo-pharmacology.

Splittable systems in biomedical applications

“…106 The predication of protein interaction sites and folding patterns is vital for identifying optimal split sites and ensuring effective reassembly under physiological conditions. 107 Prediction algorithms that are highly customized for specific splittable protein types have also been used to produce more efficient modeling and docking structure predictions. For example, automated tools were developed for SPELL (split proteins regulated by a ligand or by light) to predict these sites with minimal spontaneous assembly.…”

Recent Advances and Challenges in Protein Structure Prediction

“…Combined with AlphaFold2, Bruno E. Correia et al designed a pipeline for de novo protein design, and the pipeline produced viable sequences with minimal postdesign intervention. There are many other studies on AlphaFold2-assisted protein design reported, such as references and . Within two years of AlphaFold2’s emergence, many successful applications of AlphaFold2 in biology and medicine have been reported, demonstrating its immense impact and revolutionary potential.…”