In recent years, studies of traditional medicinal plants have gradually increased worldwide because the natural sources and variety of such plants allow them to complement modern pharmacological approaches. As computer technology has developed, in silico approaches such as virtual screening and network analysis have been widely utilized in efforts to elucidate the pharmacological basis of the functions of traditional medicinal plants. In the process of new drug discovery, the application of virtual screening and network pharmacology can enrich active compounds among the candidates and adequately indicate the mechanism of action of medicinal plants, reducing the cost and increasing the efficiency of the whole procedure. In this review, we first provide a detailed research routine for examining traditional medicinal plants by in silico techniques and elaborate on their theoretical principles. We also survey common databases, software programs and website tools that can be used for virtual screening and pharmacological network construction. Furthermore, we conclude with a simple example that illustrates the whole methodology, and we present perspectives on the development and application of this in silico methodology to reveal the pharmacological basis of the effects of traditional medicinal plants.

Background: Ganoderma lucidum, a double-walled basidiospore produced by porous basidiomycete fungi, has been used as a traditional medicine for thousands of years. It is considered a valuable Chinese medicine for strengthening body resistance, invigorating the spleen, and replenishing Qi. G. lucidum contains a variety of active ingredients, such as polysaccharides, triterpenoids, nucleosides, sterols, alkaloids, polypeptides, fatty acids, steroids, and inorganic elements, and has anticancer, anti-inflammatory, hepatoprotection, hypoglycemic, anti-melanogenesis, anti-aging, and skin barrier-repairing activity. Conclusions: The review summarizes the traditional usages, distribution, active constituents, structure, and biological effects of G. lucidum, with an aim to offer directions for further research and better usage of G. lucidum as a medicinal raw material.

Modern studies have shown that adaptogens can non-specifically enhance the resistance of human body under a wide range of external stress conditions with a multi-targeted and multi-channel network-like manner, especially by affect the immune-neuro-endocrine system and the hypothalamic–pituitary–adrenal axis. This review article draws the attention to the relationships of adaptogens, tonics from traditional Chinese medicine (TCM) and ginseng-like herbs worldwide, which all have similar plant sources and clinical applications. To clarify the sources and pharmacological mechanisms of these plant-originated adaptogens, which will provide useful information for the utilization of adaptogens to improve the human health. Meanwhile, the TCMs and the world-wide ginseng-like herbs from each region’s ethnopharmacology will be beneficial modernization and globalization.

Small molecule drugs are rarely selective enough to interact solely with their designated targets. Unintended "off-target" interactions often lead to side effects, but also serendipitously lead to new therapeutic uses. Identification of the off-targets of a compound is therefore of significant value to the evaluation of its developmental potential. In computational biology, the strategy of "reverse docking" has been introduced to predict the targets of a compound, which uses a compound to virtually screen a library of proteins, reversing the bait and prey in "normal" docking screenings. The present study shows that, in reverse docking, additional optimization of the scoring function may help to improve the target prediction accuracy. In a case study with the Glide scores, we found that only 57% of the ligand-protein relationships could be correctly identified in a library of 58 complexes whose crystal binding conformations were all able to be accurately reproduced. This was likely a result of the constant over- or under-estimation of the scores for specific proteins. In other words, there were interprotein noises in the Glide scores. Introducing a correction term based on protein characteristics improved the target-prediction accuracy by 27% (57-72%). It is our hope that this focused discussion on the Glide scores would invite further efforts to characterize and normalize this type of interprotein noises in all docking scores, so that better target prediction accuracy can be achieved with the strategy of reverse docking.