Editorial: Computational Approaches in Drug Discovery and Precision Medicine
Zunnan Huang, Yao Xiao, Ruo‐Xu Gu · Frontiers in Chemistry · 2021
Computational Approaches in Drug Discovery and Precision MedicineDuring the past decades, computational approaches have been highly involved in all stages of drug discovery (Terstappen and Reggiani, 2001;Duarte et al., 2019) and precision medicine (Barbolosi et al., 2016;Delavan et al., 2018), from screening of leading compounds to preclinical trials.These methods, including both structure-based molecular modeling techniques (Kalyaanamoorthy and Chen, 2011) and artificial intelligence (Fleming, 2018;Williams et al., 2018;Chan et al., 2019), accelerate the discovery of drug candidates, guide the repurposing of existing drugs, improve our understanding of biomolecular nanomachines, and reduce the use of experimental animals.To present state-of-the-art computational studies in this field, we launched a research topic in Frontiers in Chemistry entitled "Computational Approaches in Drug Discovery and Precision Medicine."This research topic included nine articles, including two reviews and seven original research articles, which covered theoretical predictions of ligand-protein interactions, drug resistance mechanism, and drug selectivity mechanism in protein-ligand binding, and the prediction of preclinical properties of ligands as well.Two manuscripts reported case study of virtual screening of drug candidates.Han et al. constructed natural product database by analyzing the ingredients of traditional Chinese medicine prescriptions for treating prostate cancer. Molecular docking and wet experiments were then performed to screen possible ligands targeting androgen receptor, a protein involved in the pathogenesis of prostate cancer. Halim et al. docked active components of frankincense, macrocyclic diterpenoid derivatives, and boswellic acids to multiple proteins which are known drug targets of psoriasis in order to screen possible candidates for treating psoriasis.In addition, the review article by Zhao et al. summarized recent structure-based and ligand-based virtual screening of analgesics targeting opioid receptors, the computational guided studies of subtype selectivity of σ and κ opioid receptors, as well as the activation mechanism.Molecular modeling has become an important complimentary to experiments in the study of ligand-protein interaction mechanisms.In this regard, Yang et al. investigated subtype selectivity mechanism of pyrabactin, an abscisic acid (ABA)-mimicking ligand, for the ABA receptors, using sequence and structural comparison and free energy calculations.Zhang et al. explored how the mutations of Mycobacterium tuberculosis RNA polymerase develop drug resistance to rifampicin.During the past years, artificial intelligence has been increasingly involved in drug development.In this research topic, two manuscripts employed the application of machine learning to predict the