ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.

Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

Mirizzi syndrome: Problems and strategies.

Mirizzi syndrome is a serious complication of gallstone disease. It is caused by the impacted stones in the gallbladder neck or cystic duct. One of the features of Mirizzi syndrome is severe inflammation or dense fibrosis at the Calot's triangle. In our clinical practice, bile duct, branches of right hepatic artery and right portal vein clinging to gallbladder infundibulum are often observed due to gallbladder infundibulum adhered to right hepatic hilum. The intraoperative damage of branches of right hepatic artery occurs more easily than that of bile duct, all of which are hidden pitfalls for surgeons. Magnetic resonance cholangiopancreatography (MRCP) and endoscopic retrograde cholangiopancreatography (ERCP) are the preferable tools for the diagnosis of Mirizzi syndrome. Anterograde cholecystectomy in Mirizzi syndrome is easy to damage branches of right hepatic artery and bile duct due to gallbladder infundibulum adhered to right hepatic hilum. Subtotal cholecystectomy is an easy, safe and definitive approach to Mirizzi syndrome. When combined with the application of ERCP, a laparoscopic management of Mirizzi syndrome by well-trained surgeons is feasible and safe. The objective of this review was to highlight its existing problems: (1) low preoperative diagnostic rate, (2) easy to damage bile duct and branches of right hepatic artery, and (3) high concomitant gallbladder carcinoma. Meanwhile, the review aimed to discuss the possible therapeutic strategies: (1) to enhance its preoperative recognition by imaging findings, and (2) to avoid potential pitfalls during surgery.

The present state and challenges of active learning in drug discovery.

Active learning (AL) is an iterative feedback process that efficiently identifies valuable data within vast chemical space, even with limited labeled data. This characteristic renders it a valuable approach to tackle the ongoing challenges faced in drug discovery, such as the ever-expanding explore space and the limitations of labeled data. Consequently, AL is increasingly gaining prominence in the field of drug development. In this paper, we comprehensively review the application of AL at all stages of drug discovery, including compounds-target interaction prediction, virtual screening, molecular generation and optimization, as well as molecular properties prediction. Additionally, we discuss the challenges and prospects associated with the current applications of AL in drug discovery.

Imatinib@glycymicelles entrapped in hydrogel: preparation, characterization, and therapeutic effect on corneal alkali burn in mice.

Imatinib (IMB) is a type of tyrosine kinase inhibitor with great application potential for inhibiting corneal neovascularization (CNV), but its poor water solubility limits its application in eye disease treatment. In this study, novel IMB@glycymicelles entrapped in hydrogel (called IMB@glycymicelle-hydrogel) were prepared, characterized, and evaluated for their therapeutic effects on corneal alkali burn in mice. Imatinib could be successfully loaded in glycymicelles using glycyrrhizin as a nanocarrier with an optimized weight ratio of IMB:nanocarrier. The apparent solubility of IMB was significantly improved from 61.69 ± 5.55 µg/mL to bare IMB to 359,967.62 ± 20,059.42 µg/mL to IMB@glycymicelles. Then, the IMB@glycymicelles were entrapped in hydrogel fabricated with hydroxypropyl methylcellulose and sodium hyaluronate (HA) to prolong retention time on the ocular surface. Rabbit eye tolerance tests showed that IMB@glycymicelle-hydrogel possessed good ocular safety profiles. In a mouse model of corneal alkali burns, the topical administration of IMB@glycymicelle-hydrogel showed strong efficacy by prompting corneal wound healing, recovering corneal sensitivity, relieving corneal opacities, and inhibiting CNV, and these efficacy evaluation parameters were better than those of the positive drug HA. Overall, these results demonstrated that IMB@glycymicelle-hydrogel may be a promising candidate for the effective treatment of alkali ocular damage.

Analysis of risk factors and prognosis of diarrhea after renal transplantation.

BACKGROUND: Diarrhea is a prevalent complication after renal transplantation. OBJECTIVE: To examine the risk factors for diarrhea after renal transplantation, evaluate their combined predictive values, and analyze the prognosis. METHODS: Clinical data of patients who underwent allogeneic renal transplantation in the Second People's Hospital of Shanxi Province from January 2019 to March 2020 were retrospectively analyzed, cases were screened and grouped, independent risk factors for diarrhea after renal transplantation were analyzed by univariate analysis and multivariate analysis, and their predictive value was evaluated by receiver operating characteristic (ROC) curve. The survival time of recipient grafts in diarrhea and non-diarrhea groups were evaluated by Kaplan-Meier and log-rank test. RESULTS: We included 166 recipients in the study and the incidence of diarrhea was 25.9%; univariate and logistic regression multivariate analyses revealed that independent risk factors for diarrhea in recipients were that the type of renal transplant donor was DCD (donation after circulatory death), immunity induction was onducted with basiliximab + antithymocyte globulin (ATG), and ATG alone, the type of mycophenolic acid (MPA) used was mycophenolate mofetil capsules, and delayed graft function (DGF) occurred after transplantation. The ROC curve indicated that the combination of the four factors had good accuracy in predicting the occurrence of diarrhea in recipients. The graft survival rate two years after the operation in the diarrhea group was significantly lower than that in the non-diarrhea group. CONCLUSION: Diarrhea affected the two-year survival rate of the graft. The type of donor, immunity induction scheme, and the type of MPA and DGF were independent risk factors for diarrhea in recipients, and the combination of the four factors had good prognostic prediction value.

OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds.

Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.

Development of the first officially licensed live attenuated duck hepatitis A virus type 3 vaccine strain HB80 in China and its protective efficacy against DHAV-3 infection in ducks.

Duck hepatitis A virus type 3 (DHAV-3) is an infectious virus that is highly fatal to ducklings and causes significant economic losses in the duck industry worldwide. Biosecurity and vaccination are required to control the pathogen. In the present study, we attenuated a lowly pathogenic DHAV-3 clinical isolate, named as HB, by serial passaging in duck embryos, and followed by several adaptive proliferations in specific-pathogen-free (SPF) chicken embryos. The virulence of DHAV-3 at different passages was assessed by infecting 3-day-old ducklings. We found that the HB strain lost pathogenicity to ducklings from the 55th passage onwards. The 80th passage strain (HB80), which achieved good growth capacity in duck embryos with a viral titer of 108.17 50% egg lethal dose per milliliter (ELD50/mL), was selected as a live attenuated vaccine candidate. The HB80 strain did not induce clinical symptoms or pathological lesions in 3-day-old ducklings and showed no virulence reversion after 5 rounds of in vivo back-passage. The minimum effective dose of HB80 was determined to be 104.5 ELD50 by hypodermic inoculation of the neck. Importantly, a single dose of HB80 elicited good immune responses and provided complete protection against challenge with the lethal DHAV-3 strain. Compared with the genomic sequence of the parental HB strain, HB80 had 7 amino acid substitutions, two of them are in the hypervariable region of the VP1 and polymerase-encoding 3D regions, which may play a role in virulence attenuation. Our data suggest that the attenuated HB80 strain is a promising vaccine candidate for the prevention of DHAV-3 infections in China. HB80 has been registered as a New Veterinary Drug Registration Certificate by the Chinese Ministry of Agriculture and Rural Affairs (MARA), and is the first live attenuated DHAV-3 vaccine strain to be officially licensed in China.