Artificial intelligence-aided drug discovery (AIDD) is a new version of computer-aided drug discovery (CADD). AIDD is featured in significantly promoting the performance of conventional CADD. AI markedly enhances the learning ability of CADD. In the 1960s, CADD was established from conventional QSAR approaches, which mainly used regression approaches to derive substructure-activity relationship for compounds with a common scaffold, and guide drug molecular design, figure out the binding features of drugs, and identify potential drug targets. Since the 1990s, structural biology has provided three-dimensional structures of drug targets, enabling drug discovery based on target structure (SBDD), fragment-based drug discovery (FBDD), and structure-based virtual screening (SBVS) with CADD approaches. In the past 30 years, many first in class (FIC) and best in class (BIC) drugs were discovered with CADD. Now, AIDD will further revolutionize CADD by reducing human interventions and mining big chemical and biological data. It is expected that AIDD will significantly enhance the abilities of CADD, virtual screening and drug target identification. This article tries to provide perspectives of CADD and AIDD in medicinal chemistry with case studies.

Cancer is the most important leading cause of death worldwide, with about 10 million deaths caused by cancer in 2020. In situ gel drug delivery systems have attracted much attention in the field of pharmacy and biotechnology due to their good histo-compatibility, excellent injectability, high drug delivery capacity, slow-release drug delivery, and less influence by the in vivo environment. Meanwhile, in situ gel can be combined with chemotherapy, photo-thermal therapy, chemokinetic therapy, immunotherapy and so on to deliver drugs into the tumor site in a less invasive way without surgical operation, forming a semi-solid gel reservoir in the tumor site to realize in situ tumor combined therapy. In this paper, the author summarized the research progress of anti-tumor in situ gel delivery system in the past 10 years, introduced its commonly used polymer materials, classification principles and specific application examples, and finally summarized and discussed the key issues, in order to provide reference for the development of new anti-tumor drug delivery system in the future.

This study focuses on the microbial quality control of the Chinese herbal decoction pieces. In view of the shortcomings of traditional culture methods such as slow detection speed and inability to detect unculturable microorganisms, a new method based on ATP bioluminescence technology combined with statistical analysis methods was established to rapidly predict and quantitatively detect the total aerobic microbial count (TAMC) and total yeast and mold count (TYMC) contaminated Bupleurum chinense DC. decoction pieces. Based on the optimized ATP bioluminesence detection system, accurate detection of pure bacterial solution of Escherichia coli, Bacillus subtilis and Staphylococcus aureus can be achieved, with detection limits of 47.86, 89.13 and 1 862.09 CFU·mL^-1, respectively. The detection time was 6.5 h, and the detection cost was as low as 2 yuan/time. The upper and lower warning limits of TAMC were determined by the misjudgment rates of 10% and 20%, respectively. And the warning limit of TYMC was determined by the misjudgment rate of 20%. The proposed crossing method could quickly predict the amount of microbial contamination in Bupleurum chinense DC. decoction pieces. The constructed partial least squares regression (PLSR) model could accurately quantify the quantity of microbial contamination in Bupleurum chinense DC. decoction pieces. The optimal PLSR prediction model for TAMC had a correction coefficient (R²) of 0.826, a root mean square error of correction set (RMSE_E) of 0.468 and a root mean square error of cross-validation set (RMSE_CV) of 0.465. The R², RMSE_E and RMSE_CV in the prediction model of TYMC were 0.778, 0.543 and 0.541, respectively. The aim of this study is to establish a kind of rapid detection method and prediction models for the microbial limit of traditional Chinese medicine and Chinese herbal decoction pieces, and to provide a more convenient and sensitive detection technology for the microbial quality process control of traditional Chinese medicine products.

Sophorae Flavescentis Radix is the dried root of Sophora flavescens Ait. and Sophorae Tonkinensis Radix et Rhizoma is the dried root and rhizome of Sophora tonkinensis Gagnep. The two drugs are both from the same genus Sophora, having similar and different compositions and efficacies, however, their differences are not fully demonstrated in current standard. In this study, the high-performance thin-layer chromatography with multi-dimensional and multi-level features combined with electric spray mass spectrometry (HPTLC-ESI-MS) was used to discover and identify the characteristic zones in extracts of Sophorae Flavescentis Radix and Sophorae Tonkinensis Radix et Rhizoma, after optimizing the preparation method of the test solution and chromatographic parameters. As a result, 17 main characteristic zones were found on HPTLC chromatograms of Sophorae Flavescentis Radix and Sophorae Tonkinensis Radix et Rhizoma, among them, besides 3 known chemicals, another 12 unknown components were identified by HPTLC-ESI-MS, they are 1 alkaloid and 11 flavonoids. The identification results were verified by the reference standards partially and nuclear magnetic resonance spectra after guided-isolation. Finally, a unified HPTLC specific identification method with different markers was established to identify Sophorae Flavescentis Radix and Sophorae Tonkinensis Radix et Rhizoma simultaneously. Thanks to abundant chemical information provided when using diverse polarity mobile phases and derivatization reagents, the HPTLC technology offers a convenient strategy for discovery, quality evaluation, and identification of target chemicals when connecting with mass spectrometry.

Currently, the resistance of first-line anti-tuberculosis drugs has made the prevention and treatment of tuberculosis increasingly difficult, posing a serious threat to global public health. Several studies have shown that efflux pumps are one of the important causes for bacteria to develop multi-drug resistance and extremely-drug resistance, and efflux pump inhibitors can inhibit the efflux of antibacterial drugs, thereby reducing bacterial drug resistance. Numerous natural products and synthetic compounds have been reported to possess efflux pump inhibitory activity, but they have not been applied in clinical settings because of their toxicity, pharmacokinetic properties, etc. Therefore, we summarized the efflux pump inhibitory activity, antimicrobial activity, and structure-activity relationships of reported efflux pump inhibitors against Mycobacterium tuberculosis in recent years, providing references for the development of new efflux pump inhibitors with better activity and lower toxicity.

Aiming at the hysteresis and destructiveness of off-line static detection of critical quality attribute of the moisture content of the raw material unit of the traditional Chinese medicine manufacturing process, honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis were used as the research carriers, and the drying method was used to measure the moisture content as a reference value. The moving stage was used to simulate the movement process of samples on the conveyor belt in the actual on-site production process, and near-infrared (NIR) spectra were collected, combined with machine learning, to establish NIR on-site dynamic detection model of moisture content in multi-variety honey-processed Chinese herbal slice. The results show that the second derivative method is used to preprocess the spectrum. The number of decision trees (ntree), the number of random features (max feature), and the minimum number of samples for generating leaf nodes (node size) are selected: 46, 76, and 8, respectively. The quantitative analysis model of moisture content has the best effect. The prediction coefficient of determination (the prediction coefficient of determination, R_pre²) and the root mean square error of prediction (root mean square error of prediction, RMSEP) of the model were 0.903 2 and 0.330 2, respectively. The NIR quantitative model for the moisture content of multi-variety honey-processed Chinese herbal slice established in this study has good predictive performance, and can achieve rapid, accurate and non-destructive quantitative analysis of the moisture content of honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis at the same time, and provides a method for determining the moisture content of honey-processed Chinese herbal slice of the raw material unit of the traditional Chinese medicine manufacturing process.

In this study, the ovarian surgery (ovariectomy, OVX) was used to establish the osteoporosis mice model of primary menstruation, in order to evaluate the protective effects and mechanisms of Zhibai Dihuang decotion on postmenopausal osteoporosis (PMOP). The animal experimental protocol has been reviewed and approved by Laboratory Animal Ethics Committee of Jinan University (number: 20210315-03), in compliance with the Institutional Animal Care Guidelines. C57BL/6 mice were divided into five groups, including Sham group, OVX group, low (32 g·kg^-1·day^-1) and high dose (64 g·kg^-1·day^-1) of Zhibai Dihuang decotion groups, positive drug group (alendronate, 9.9 mg·kg^-1·q3d). After modeling, mice were given medication intervention for 8 weeks, and then femoral and tibial tissues were taken to detect indicators such as bone microstructure, bone resorption, and oxidative stress. The experimental results showed that after Zhibai Dihuang decotion administration, the bone microstructure damage caused by OVX surgery was alleviated, and the relevant parameters bone mineral density (BMD), bone volume/total volume (BV/TV), trabecular number (Tb. N) and connectivity density (Conn. D) both significantly increased. At the same time, the number of TRAP positive osteoclasts decreased significantly, and the levels of proteins and genes related to osteoclast differentiation decreased, indicating that Zhibai Dihuang decoction could inhibit the increased activity of osteoclast caused by OVX. Afterwards, network pharmacology was used to construct the active compound action target network of Zhibai Dihuang decotion, and it was found that the target genes of its active ingredients were closely related to the oxidative stress pathway. Finally, the detection results of oxidative stress levels in bone tissues showed that after treatment with Zhibai Dihuang decotion, the levels of oxidative stress products 4-hydroxynonenal (4-HNE) and malondialdehyde (MDA) in bone tissues of mice significantly decreased, while the levels of antioxidant stress substance L-glutathione (GSH) increased. These above results indicated that Zhibai Dihuang decotion can regulate the level of oxidative stress in the body and inhibit osteoclast activity, which played a therapeutic role in PMOP, as well as provided theoretical basis for the prevention and treatment of PMOP with traditional Chinese medicine.

Based on the dual needs of analgesia and anti-inflammation in trauma treatment, this study uses acetaminophen and moxifloxacin hydrochloride as active pharmaceutical ingredients and develops a composite bilayer tablet with a dual-phase drug release system by using binder jet 3D printing technology. Due to the complexity of the 3D printing process, there is an interaction between the various parameters. Through the optimization of the process, the relationship between the key process parameters can be determined more intuitively. In this study, the process of extended-release tablets was optimized to maintain the mechanical properties of the tablets while realizing the regulation of release. The full-factor experimental design of three central points 2³ was used to analyze the factors that significantly affect the quality attributes of extended-release tablets and the interaction between factors. The optimal extended-release process parameters were obtained by the response optimizer: the inkjet quantity of the printing ink was 10 (about 13.8 pL), the powder thickness was 180 μm, and the running speed was 360 mm·s^-1. The in vitro of release of 3D printed composite bilayer tablets showed that the in vitro of release of 3D printed tablets and commercially available tablets conformed to the Ritger-Peppas release model. The results of porosity showed that the immediate-release layer of the preparation has many pores and large pore size, and the dissolution of the immediate release layer within 15 min was greater than 85%. The internal pore size of the extended release layer is large, but it can still release slowly for up to 8 h, the mechanism may be related to the extended release of HPMC gelation. On the basis of verifying the rationality of the design goal of 3D printed composite bilayer tablets, this study also provides a theoretical basis for the preparation of 3D printing complex preparations.

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

Dihydrofolate reductase (DHFR) is a well-known key target in the treatment of tumors, bacterial infections, and parasitic infections; and it plays a critical role in the biosynthesis of cellular DNA. DHFR inhibitors interfere with one-carbon metabolism by inhibiting substrate binding to DHFR, thereby inhibiting cell proliferation. Research on DHFR inhibitors has continued since the 1940s. To date, a variety of DHFR inhibitors have come into the market, primarily used for anti-tumor, antibacterial, antiparasitic, and anti-inflammatory therapy. This review summarizes the research progress of DHFR inhibitors with antitumor or antibacterial effects in recent years based on the classification of single-target and dual-target and looks forward to the opportunities and challenges faced by the work in this field.