GCDB: a glaucomatous chemogenomics database forin silicodrug discovery

Abstract

Glaucoma is a group of neurodegenerative diseases that can cause irreversible blindness. The current medications, which mainly reduce intraocular pressure to slow the progression of disease, may have local and systemic side effects. Recently, medications with possible neuroprotective effects have attracted much attention. To assist in the identification of new glaucoma drugs, we created a glaucomatous chemogenomics database (GCDB; http://cadd.pharmacy.nankai.edu.cn/gcdb/home) in which various glaucoma-related chemogenomics data records are assembled, including 275 genes, 105 proteins, 83 approved or clinical trial drugs, 90 206 chemicals associated with 213 093 records of reported bioactivities from 22 324 corresponding bioassays and 5630 references. Moreover, an improved chemical similarity ensemble approach computational algorithm was incorporated in the GCDB to identify new targets and design new drugs. Further, we demonstrated the application of GCDB in a case study screening two chemical libraries, Maybridge and Specs, to identify interactions between small molecules and glaucoma-related proteins. Finally, six and four compounds were selected from the final hits for in vitro human glucocorticoid receptor (hGR) and adenosine A3 receptor (A3AR) inhibitory assays, respectively. Of these compounds, six were shown to have inhibitory activities against hGR, with IC50 values ranging from 2.92–28.43 μM, whereas one compoundshowed inhibitory activity against A3AR, with an IC50 of 6.15 μM. Overall, GCDB will be helpful in target identification and glaucoma chemogenomics data exchange and sharing, and facilitate drug discovery for glaucoma treatment.

An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties

“…We applied SVM-RFE as the first step feature selection and obtain 11 features. As reported that SVM-RFE usually provides a criterion to rank features based on their relevancy and complementarity but does not take the redundancy among features into account [ 16 ], we therefore implemented the second step to remove potential redundant features of high correlations. We calculated the Pearson correlation coefficients among 11 features and removed potential redundant features with a threshold of 0.65.…”

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

“…Thus, the cellular pathways and environment and the biological functions and processes affected by the chemicals are systematically explored. Such system-level analyses (Bian et al 2019; Pei et al 2017, 2019; Wei et al 2018; Wu et al 2019; Xu et al 2016) assist in deciphering polypharmacological effects and disease mechanisms (Fig. 2).…”