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.

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.

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.

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.

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.

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.

Stoichiometry and architecture of the platelet membrane complex glycoprotein Ib-IX-V.

Glycoprotein (GP) Ib-IX-V is the second most abundant platelet receptor for thrombin and other ligands crucial for hemostasis and thrombosis. Its activity is involved in platelet adhesion to vascular injury sites and thrombin-induced platelet aggregation. GPIb-IX-V is a heteromeric complex composed of four subunits, GPIbα, GPIbß, GPV and GPIX, in a stoichiometric ratio that has been wildly debated. Despite its important physiological roles, the overall structure and molecular arrangement of GPIb-IX-V are not yet fully understood. Here, we purify stable and functional human GPIb-IX-V complex from reconstituted EXPi293F cells in high homogeneity, and perform biochemical and structural characterization of this complex. Single-particle cryo-electron microscopy structure of GPIb-IX-V is determined at ∼11 Å resolution, which unveils the architecture of GPIb-IX-V and its subunit organization. Size-exclusion chromatography-multi-angle static light scattering analysis reveals that GPIb-IX-V contains GPIb-IX and GPV at a 1:1 stoichiometric ratio and surface plasmon resonance assays show that association of GPV leads to slow kinetics of thrombin binding to GPIb-IX-V. Taken together, our results provide the first three-dimensional architecture of the intact GPIb-IX-V complex, which extends our understanding of the structure and functional mechanism of this complex in hemostasis and thrombosis.

Ni-Carbon Microtube/Polytetrafluoroethylene as Flexible Electrothermal Microwave Absorbers.

Flexible microwave absorbers with Joule heating performance are urgently desired to meet the demands of extreme service environments. Herein, a type of flexible composite film is constructed by homogeneously dispersing a hierarchical Ni-carbon microtube (Ni/CMT) into a processable polytetrafluoroethylene (PTFE) matrix. The Ni/CMT are interconnected into a 3D conductive network, in which the huge interior cavity of the carbon microtube (CMT) improves impedance matching and provides additional hyper channels for electromagnetic (EM) waves dissipation, and the hierarchical magnetic Ni nanoparticles enhance the synergistic interactions between confined heterogeneous interfaces. Such an ingenious structure endows the composites with excellent electrothermal performance and improves their serviceability for application under extreme environments. Moreover, under a low fill loading of 3 wt.%, the Ni/CMT/PTFE (NCP) can achieve excellent low-frequency microwave absorption (MA) property with a minimum reflection loss of -59.12 dB at 5.92 GHz, which covers almost the entire C-band. Relying on their brilliant MA property, an EM sensor is designed and achieved by the resonance coupling of the patterned NCP. This work opens up a new way for the design of next-generation microwave absorbers that meet the requirements of EM packaging, proofing water and removing ice, fire safety, and health monitoring.

Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications.

Matched molecular pair analysis (MMPA) is a tool to extract knowledge of medicinal chemistry from existing experimental data, and it relates changes in activities or properties to specific structural changes. More recently, MMPA has also been applied in multi-objective optimization and de novo drug design. Here, we discuss the concept, techniques, and case studies of MMPA, which provide an overview of the current development in the field of MMPA. This Perspective also summarizes up-to-date MMPA applications and highlights the successes and opportunities for further MMPA advances.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs.

TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine.

Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important roles. However, most of the existing TCM ingredient databases do not provide simplification function for extracting key ingredients in each herb or formula, which hinders the research on the mechanism of actions of the ingredients in TCM databases. The lack of quality control and standardization of the data in most of these existing databases is also a prominent disadvantage. Therefore, we developed a Traditional Chinese Medicine Simplified Integrated Database (TCMSID) with high storage, high quality and standardization. The database includes 499 herbs registered in the Chinese pharmacopeia with 20,015 ingredients, 3270 targets as well as corresponding detailed information. TCMSID is not only a database of herbal ingredients, but also a TCM simplification platform. Key ingredients from TCM herbs are available to be screened out and regarded as representatives to explore the mechanism of TCM herbs by implementing multi-tool target prediction and multilevel network construction. TCMSID provides abundant data sources and analysis platforms for TCM simplification and drug discovery, which is expected to promote modernization and internationalization of TCM and enhance its international status in the future. TCMSID is freely available at https://tcm.scbdd.com .

Nitrogen, manganese, iron, and carbon resource acquisition are potential functions of the wild rice Oryza rufipogon core rhizomicrobiome.

BACKGROUND: The assembly of the rhizomicrobiome, i.e., the microbiome in the soil adhering to the root, is influenced by soil conditions. Here, we investigated the core rhizomicrobiome of a wild plant species transplanted to an identical soil type with small differences in chemical factors and the impact of these soil chemistry differences on the core microbiome after long-term cultivation. We sampled three natural reserve populations of wild rice (i.e., in situ) and three populations of transplanted in situ wild rice grown ex situ for more than 40 years to determine the core wild rice rhizomicrobiome. RESULTS: Generalized joint attribute modeling (GJAM) identified a total of 44 amplicon sequence variants (ASVs) composing the core wild rice rhizomicrobiome, including 35 bacterial ASVs belonging to the phyla Actinobacteria, Chloroflexi, Firmicutes, and Nitrospirae and 9 fungal ASVs belonging to the phyla Ascomycota, Basidiomycota, and Rozellomycota. Nine core bacterial ASVs belonging to the genera Haliangium, Anaeromyxobacter, Bradyrhizobium, and Bacillus were more abundant in the rhizosphere of ex situ wild rice than in the rhizosphere of in situ wild rice. The main ecological functions of the core microbiome were nitrogen fixation, manganese oxidation, aerobic chemoheterotrophy, chemoheterotrophy, and iron respiration, suggesting roles of the core rhizomicrobiome in improving nutrient resource acquisition for rice growth. The function of the core rhizosphere bacterial community was significantly (p < 0.05) shaped by electrical conductivity, total nitrogen, and available phosphorus present in the soil adhering to the roots. CONCLUSION: We discovered that nitrogen, manganese, iron, and carbon resource acquisition are potential functions of the core rhizomicrobiome of the wild rice Oryza rufipogon. Our findings suggest that further potential utilization of the core rhizomicrobiome should consider the effects of soil properties on the abundances of different genera. Video Abstract.