Systematic characterization of structure-activity relationships and ADMET compliance: a case study

Exploring the treatment of COVID-19 with Yinqiao powder based on network pharmacology

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In China, Yinqiao powder is widely used to prevent and treat COVID-19 patients with Weifen syndrome. In this study, the screening and verification of active ingredients, target selection and DisGeNET scoring, drug-ingredient-gene network construction, protein-protein interaction network construction, molecular docking and surface plasmon resonance (SPR) analysis, gene ontology (GO) functional analysis, gene tissue analysis, and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were used to explore the active ingredients, targets, and potential mechanisms of Yinqiao powder in the treatment of COVID-19. We also predicted the therapeutic effect of Yinqiao powder using TCM anti-COVID-19 (TCMATCOV). Yinqiao powder has a certain therapeutic effect on COVID-19, with an intervention score of 20.16. Hesperetin, eriodictyol, luteolin, quercetin, and naringenin were the potentially effective active ingredients against COVID-19. The hub-proteins were interleukin-6 (IL-6), mitogen-activated protein kinase 3 (MAPK3), tumor necrosis factor (TNF), and tumor protein P53 (TP53). The potential mechanisms of Yinqiao powder in the treatment of COVID-19 are the TNF signaling pathway, T-cell receptor signaling pathway, Toll-like receptor signaling pathway, and MAPK signaling pathway. This study provides a new perspective for discovering potential drugs and mechanisms of COVID-19.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.

Activity landscape analysis of novel 5α-reductase inhibitors

Inhibitors of the enzyme 5[Formula: see text]-reductase (5aR) are promising therapeutic agents for the treatment of benign prostatic hyperplasia (BPH) and prostate cancer. The lack of structural data of the enzyme 5aR prompts the application of ligand-based approaches to systematically explore the activity landscape of 5aR inhibitors. As part of an effort to develop inhibitors of this enzyme for the treatment of BPH, herein we discuss a chemoinformatic-based analysis of the activity landscape of a novel set of 53 novel pregnane and androstene compounds. It was found that, in general, for each pair of compounds in the set, as the structure similarity of the compounds increases the corresponding potency difference decreases. These results are in agreement with an overall smooth activity landscape. However, two potent activity cliff generators were identified pointing to specific small structural changes that have a large impact on the inhibition of 5aR.

Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry

Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery. Herein, we survey advances on key topics on PLM with emphasis on questions often raised regarding the outcomes of the property landscape studies. We also emphasize on concepts of PLM that are being extended to other experimental areas beyond drug discovery. Topics discussed in this paper include applications of PLM to further characterize protein-ligand interactions, the utility of PLM as a quantitative and descriptive approach, and the statistical validation of property cliffs.