Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

Epilepsy is a common cause of serious cognitive disorders and is known to have impact on patients’ memory and executive functions. Therefore, the development of antiepileptic drugs for the improvement of spatial learning and memory in patients with epileptic cognitive dysfunction is important. In the present work, we systematically predicted and analyzed the potential effects of Ginkgo terpene trilactones (GTTL) on cognition and pathologic changes utilizing in silico and in vivo approaches. Based on our established chemogenomics knowledgebase, we first conducted the network systems pharmacology analysis to predict that ginkgolide A/B/C may target 5-HT 1A, 5-HT 1B, and 5-HT 2B. The detailed interactions were then further validated by molecular docking and molecular dynamics (MD) simulations. In addition, status epilepticus (SE) was induced by lithium–pilocarpine injection in adult Wistar male rats, and the results of enzyme-linked immunosorbent assay (ELISA) demonstrated that administration with GTTL can increase the expression of brain-derived neurotrophic factor (BDNF) when compared to the model group. Interestingly, recent studies suggest that the occurrence of a reciprocal involvement of 5-HT receptor activation along with the hippocampal BDNF-increased expression can significantly ameliorate neurologic changes and reverse behavioral deficits in status epilepticus rats while improving cognitive function and alleviating neuronal injury. Therefore, we evaluated the effects of GTTL (bilobalide, ginkgolide A, ginkgolide B, and ginkgolide C) on synergistic antiepileptic effect. Our experimental data showed that the spatial learning and memory abilities (e.g., electroencephalography analysis and Morris water maze test for behavioral assessment) of rats administrated with GTTL were significantly improved under the middle dose (80 mg/kg, GTTL) and high dose (160 mg/kg, GTTL). Moreover, the number of neurons in the hippocampus of the GTTL group increased when compared to the model group. Our studies showed that GTTL not only protected rat cerebral hippocampal neurons against epilepsy but also improved the learning and memory ability. Therefore, GTTL may be a potential drug candidate for the prevention and/or treatment of epilepsy.

A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug–disease associations through the analysis of the similarity of GES profiles on known pairs of drug–disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug–disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway.

A traditional single-target analgesic, though it may be highly selective and potent, may not be sufficient to mitigate pain. An alternative strategy for alleviation of pain is to seek simultaneous modulation at multiple nodes in the network of pain-signaling pathways through a multitarget analgesic or drug combinations. Here we present a comprehensive pain-domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated computing tools for target identification and systems pharmacology research. Pain-CKB is constructed on the basis of our established chemogenomics technology with new features, including multiple compound support, multicavity protein support, and customizable symbol display. The determination of bioactivity is also revised to avoid the use of complex machine learning models. Our one-stop computing platform describes the chemical molecules, genes, and proteins involved in pain regulation. To date, Pain-CKB has archived 272 analgesics in the market, 84 pain-related targets with 207 available 3D crystal or cryo-EM structures, and 234 662 chemical agents reported for these target proteins. Moreover, Pain-CKB implements user-friendly web-interfaced computing tools and applications for the prediction and analysis of the relevant protein targets and visualization of the outputs, including HTDocking, TargetHunter, BBB permeation predictor, NGL viewer, Spider Plot, etc. The Pain-CKB server is accessible at .