In Silico Prediction of ERRα Agonists Based on Combined Features and Stacking Ensemble Method.

Estrogen-related receptor α (ERRα) is considered a very promising target for treating metabolic diseases such as type 2 diabetes. Development of a prediction model to quickly identify potential ERRα agonists can significantly reduce the time spent on virtual screening. In this study, 298 ERRα agonists and numerous nonagonists were collected from various sources to build a new dataset of ERRα agonists. Then a total of 90 models were built using a combination of different algorithms, molecular characterization methods, and data sampling techniques. The consensus model with optimal performance was also validated on the test set (AUC=0.876, BA=0.816) and external validation set (AUC=0.867, BA=0.777) based on five selected baseline models. Furthermore, the model's applicability domain and privileged substructures were examined, and the feature importance was analyzed using the SHAP method to help interpret the model. Based on the above, it's hoped that our publicly accessible data, models, codes, and analytical techniques will prove valuable in quick screening and rational designing more novel and potent ERRα agonists.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 µM, with Oxyberberine showing promising effects even at 10 µM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

CeO2/Co heterostructure encapsulated in hollow necklace-like carbon fiber as an advanced host material for high-performance lithium-sulfur batteries.

The shuttle effect of lithium polysulfides (LiPSs) and the sluggish reaction kinetics of LiPSs conversion pose serious challenges to the commercial feasibility of lithium-sulfur (Li-S) batteries. To address these obstacles, herein, we construct CeO2/Co heterostructures in hollow necklace-like carbon fibers (CeO2/Co-CNFs) as the cathode host material for Li-S batteries. The specific surface area of fibers is significantly enhanced by using a template, thereby promoting the utilization efficiency of sulfur. Meanwhile, CeO2/Co-CNFs show strong conductivity, effective adsorption to LiPSs, and robust catalytic activity for LiPSs conversion. As a result, the Li-S battery with CeO2/Co-CNFs displays 961 mAh g-1 at 0.2 C, with an 86 % capacity retention rate after 100 cycles. At 2.0 C current density, the composite cathode maintains an initial discharge capacity of 782 mAh g-1, with a mere 0.044 % capacity loss per cycle. Furthermore, in situations with limited electrolytes, high sulfur loading, and high areal mass loading, the composite cathode can provide a high areal capacity of 6.2 mg cm-2 over 100 cycles. This work provides a useful approach for investigating high-performance Li-S battery cathodes.

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory.

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

Novel green/red oxyfluoride phosphors with excellent optical properties for near-UV excited white light emitting diode.

Novel eye-sensitive Ba3Nb2O2F12(H2O)2:Tb3+ green and Ba3Nb2O2F12(H2O)2:Mn4+ red oxyfluoride phosphors with extremely strong absorption in the UV region were designed and synthesized by simple co-precipitation strategy. Particularly, Tb3+ ions were doped in this matrix for the first time, which greatly improves their absorption efficiency in the near ultraviolet region (367 nm) and emits sharp green light (544 nm). In addition, the Ba3Nb2O2F12(H2O)2:Mn4+ red phosphors have strong zero phonon line (ZPL) emission at 625 nm, which is conducive to improving the sensitivity of human eye and color purity. Meanwhile, the optical properties of the red phosphor are significantly enhanced via doping K+ cations as charge compensators. Crystal field environment and nephelauxetic effect of the as-prepared phosphors before and after K+ cation doping were systematically analyzed. Moreover, these synthesized red/green phosphors have good thermal stability and moisture resistance. Remarkably, the as-prepared Ba3Nb2O2F12(H2O)2:5%Mn4+ or K0.9Ba2.1Nb2O2F12(H2O)2:5%Mn4+ red phosphors can be directly mixed with the as-synthesized Ba3Nb2O2F12(H2O)2:13%Tb3+ green phosphor coating on 365 nm near-ultraviolet LED chip to package WLED devices with excellent electroluminescence performance. These findings are conducive to opening an avenue for screening the unique structure of optical materials.

Unraveling the Ligand-Binding Sites of CYP3A4 by Molecular Dynamics Simulations with Solvent Probes.

Cytochrome P450 3A4 (CYP3A4) is one of the most important drug-metabolizing enzymes in the human body and is well known for its complicated, atypical kinetic characteristics. The existence of multiple ligand-binding sites in CYP3A4 has been widely recognized as being capable of interfering with the active pocket through allosteric effects. The identification of ligand-binding sites other than the canonical active site above the heme is especially important for understanding the atypical kinetic characteristics of CYP3A4 and the intriguing association between the ligand and the receptor. In this study, we first employed mixed-solvent molecular dynamics (MixMD) simulations coupled with the online computational predictive tools to explore potential ligand-binding sites in CYP3A4. The MixMD approach demonstrates better performance in dealing with the receptor flexibility compared with other computational tools. From the sites identified by MixMD, we then picked out multiple sites for further exploration using ensemble docking and conventional molecular dynamics (cMD) simulations. Our results indicate that three extra sites are suitable for ligand binding in CYP3A4, including one experimentally confirmed site and two novel sites.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition.

Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug-metabolizing P450s could alter drug metabolism and lead to undesirable drug-drug interactions. Therefore, it is of great significance to explore the inhibition of P450s in drug discovery. Currently, machine learning including deep learning algorithms has been widely used for constructing in silico models for the prediction of P450 inhibition. These models exhibited varying predictive performance depending on the use of machine learning algorithms and molecular representations. This leads to the difficulty in the selection of appropriate models for practical use. In this study, we systematically evaluated the conventional machine learning and deep learning models for three major P450 enzymes, CYP3A4, CYP2D6, and CYP2C9 from several perspectives, such as algorithms, molecular representation, and data partitioning strategies. Our results showed that the XGBoost and CatBoost algorithms coupled with the combined fingerprint/physicochemical descriptor features exhibited the best performance with Area Under Curve (AUC)  of 0.92, while the deep learning models were generally inferior to the conventional machine learning models (average AUC reached 0.89) on the same test sets. We also found that data volume and sampling strategy had a minor effect on model performance. We anticipate that these results are helpful for the selection of molecular representations and machine learning/deep learning algorithms in the P450 model construction and the future model development of P450 inhibition.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.

The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods.

Skin Corrosion/Irritation (Corr./Irrit.) has long been a health hazard in the Globally Harmonized System (GHS). Several in silico models have been built to predict Skin Corr./Irrit. as an alternative to the increasingly restricted animal testing. However, current studies are limited by data amount/quality and model availability. To address these issues, we compiled a traceable consensus GHS data set comprising 731 Corr., 1283 Irrit., and 1205 negative (Neg.) samples from 6 governmental databases and 2 external data sets. Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. For 10-fold cross-validation, the best Corr. vs Neg. classifier achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 97.1%, while the best Irrit. vs Neg. classifier achieved an AUC of 84.7%. Compared with existing in silico tools on external validation, our Attentive FP classifiers showed the highest metrics on Corr. vs Neg. and the second highest accuracy on Irrit. vs Neg. The SHapley Additive exPlanation approach was further applied to figure out important molecular features, and the attention weights were visualized to perform interpretable prediction. Structural alerts associated with Skin Corr./Irrit. were also identified. The interpretable Attentive FP classifiers were integrated into the software AttentiveSkin at https://github.com/BeeBeeWong/AttentiveSkin. The conventional ML classifiers are also provided on our platform admetSAR at http://lmmd.ecust.edu.cn/admetsar2/. Considering the data deficiency and the limited model availability of Skin Corr./Irrit., we believe that our data set and models could facilitate chemical safety assessment and relevant studies.

Iridium-Catalyzed Enantioselective Transfer Hydrogenation of 1,1-Dialkylethenes with Ethanol: Scope and Mechanism.

Despite half a century's advance in the field of transition-metal-catalyzed asymmetric alkene hydrogenation, the enantioselective hydrogenation of purely alkyl-substituted 1,1-dialkylethenes has remained an unmet challenge. Herein, we describe a chiral PCNOx-pincer iridium complex for asymmetric transfer hydrogenation of this alkene class with ethanol, furnishing all-alkyl-substituted tertiary stereocenters. High levels of enantioselectivity can be achieved in the reactions of substrates with secondary/primary and primary/primary alkyl combinations. The catalyst is further applied to the redox isomerization of disubstituted alkenols, producing a tertiary stereocenter remote to the resulting carbonyl group. Mechanistic studies reveal a dihydride species, (PCNOx)Ir(H)2, as the catalytically active intermediate, which can decay to a dimeric species (κ3-PCNOx)IrH(µ-H)2IrH(κ2-PCNOx) via a ligand-remetalation pathway. The catalyst deactivation under the hydrogenation conditions with H2 is much faster than that under the transfer hydrogenation conditions with EtOH, which explains why the (PCNOx)Ir catalyst is effective for the transfer hydrogenation but ineffective for the hydrogenation. The suppression of di-to-trisubstituted alkene isomerization by regioselective 1,2-insertion is partly responsible for the success of this system, underscoring the critical role played by the pincer ligand in enantioselective transfer hydrogenation of 1,1-dialkylethenes. Moreover, computational studies elucidate the significant influence of the London dispersion interaction between the ligand and the substrate on enantioselectivity control, as illustrated by the complete reversal of stereochemistry through cyclohexyl-to-cyclopropyl group substitution in the alkene substrates.

AptaDB: a comprehensive database integrating aptamer-target interactions.

Aptamers have emerged as research hotspots of the next generation due to excellent performance benefits and application potentials in pharmacology, medicine, and analytical chemistry. Despite the numerous aptamer investigations, the lack of comprehensive data integration has hindered the development of computational methods for aptamers and the reuse of aptamers. A public access database named AptaDB, derived from experimentally validated data manually collected from the literature, was hence developed, integrating comprehensive aptamer-related data, which include six key components: (i) experimentally validated aptamer-target interaction information, (ii) aptamer property information, (iii) structure information of aptamer, (iv) target information, (v) experimental activity information, and (vi) algorithmically calculated similar aptamers. AptaDB currently contains 1350 experimentally validated aptamer-target interactions, 1230 binding affinity constants, 1293 aptamer sequences, and more. Compared to other aptamer databases, it contains twice the number of entries found in available databases. The collection and integration of the above information categories is unique among available aptamer databases and provides a user-friendly interface. AptaDB will also be continuously updated as aptamer research evolves. We expect that AptaDB will become a powerful source for aptamer rational design and a valuable tool for aptamer screening in the future. For access to AptaDB, please visit http://lmmd.ecust.edu.cn/aptadb/.

DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles.

Evaluation of chemical drug-likeness is essential for the discovery of high-quality drug candidates while avoiding unwarranted biological and clinical trial costs. A high-quality drug candidate should have promising drug-like properties, including pharmacological activity, suitable physicochemical and ADMET properties. Hence, in silico prediction of chemical drug-likeness has been proposed while being a challenging task. Although several prediction models have been developed to assess chemical drug-likeness, they have such drawbacks as sample dependence and poor interpretability. In this study, we developed a novel strategy, named DBPP-Predictor, to predict chemical drug-likeness based on property profile representation by integrating physicochemical and ADMET properties. The results demonstrated that DBPP-Predictor exhibited considerable generalization capability with AUC (area under the curve) values from 0.817 to 0.913 on external validation sets. In terms of application feasibility analysis, the results indicated that DBPP-Predictor not only demonstrated consistent and reasonable scoring performance on different data sets, but also was able to guide structural optimization. Moreover, it offered a new drug-likeness assessment perspective, without significant linear correlation with existing methods. We also developed a free standalone software for users to make drug-likeness prediction and property profile visualization for their compounds of interest. In summary, our DBPP-Predictor provided a valuable tool for the prediction of chemical drug-likeness, helping to identify appropriate drug candidates for further development.

In Silico prediction of inhibitors for multiple transporters via machine learning methods.

Transporters play an indispensable role in facilitating the transport of nutrients, signaling molecules and the elimination of metabolites and toxins in human cells. Contemporary computational methods have been employed in the prediction of transporter inhibitors. However, these methods often focus on isolated endpoints, overlooking the interactions between transporters and lacking good interpretation. In this study, we integrated a comprehensive dataset and constructed models to assess the inhibitory effects on seven transporters. Both conventional machine learning and multi-task deep learning methods were employed. The results demonstrated that the MLT-GAT model achieved superior performance with an average AUC value of 0.882. It is noteworthy that our model excels not only in prediction performance but also in achieving robust interpretability, aided by GNN-Explainer. It provided valuable insights into transporter inhibition. The reliability of our model's predictions positioned it as a promising and valuable tool in the field of transporter inhibition research. Related data and code are available at https://gitee.com/wutiantian99/transporter_code.git.

Facilely Direct Construction, White-Light Emission, and Color-Adjustable Luminescence of LaF3 :Pr3+ @SiO2 Yolk-Shell Nanospheres with Moisture Resistance.

Poor water stability and single luminous color are the major drawbacks of the most phosphors reported. Therefore, it is important to realize multicolor luminescence in a phosphor with single host and single activator as well as moisture resistance. LaF3 :Pr3+ @SiO2 yolk-shell nanospheres are facilely obtained by a designing new technology of a simple and cost-effective electrospray ionization combined with a dicrucible fluorating technique without using protective gas. In addition, tunable photoluminescence, especially white-light emission, is successfully obtained in LaF3 :Pr3+ @SiO2 yolk-shell nanospheres by adjusting Pr3+ ion concentrations, and the luminescence mechanism of Pr3+ ion is advanced. Compared with the counterpart LaF3 :Pr3+ nanospheres, the water stability of LaF3 :Pr3+ @SiO2 yolk-shell nanospheres is improved by 15% after immersion in water for 72 h, and the fluorescence intensity can be maintained at 86% of the initial intensity. Furthermore, by treating the yolk-shell nanospheres with hydrofluoric acid, it is not only demonstrated that the shell-layer is SiO2 but also core-LaF3 :Pr3+ nanospheres are obtained. Particularly, only fluorination procedure among the halogenation can produce such special yolk-shell nanospheres, the formation mechanism of yolk-shell nanospheres is proposed detailedly based on the sound experiments and a corresponding new technology is built. These findings broaden practical applications of LaF3 :Pr3+ @SiO2 yolk-shell nanospheres.

mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events.

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.

Changes in the soil fungal communities of steppe grasslands at varying degradation levels in North China.

The grasslands in North China are rich in fungal resources. However, the knowledge of the structure and function of fungal communities and the role of microbial communities in vegetation restoration and succession are limited. Thus, we used an Illumina HiSeq PE250 high-throughput sequencing platform to study the changing characteristics of soil fungal communities in degraded grasslands, which were categorized as non-degraded (ND), lightly degraded, moderately degraded, and severely degraded (SD). Moreover, a correlation analysis between soil physical and chemical properties and fungal communities was completed. The results showed that the number of plant species, vegetation coverage, aboveground biomass, and diversity index decreased significantly with increasing degradation, and there were significant differences in the physical and chemical properties of the soil among the different degraded grasslands. The dominant fungal phyla in the degraded grassland were as follows: Ascomycota, 44.88%-65.03%; Basidiomycota, 12.68%-29.91%; and unclassified, 5.51%-16.91%. The dominant fungi were as follows: Mortierella, 6.50%-11.41%; Chaetomium, 6.71%-11.58%; others, 25.95%-36.14%; and unclassified, 25.56%-53.0%. There were significant differences in the microbial Shannon-Wiener and Chao1 indices between the ND and degraded meadows, and the composition and diversity of the soil fungal community differed significantly as the meadows continued to deteriorate. The results showed that pH was the most critical factor affecting soil microbial and fungal communities in SD grasslands, whereas soil microbial and fungal communities in ND grasslands were mainly affected by water content and other environmental factors.

Dual-Confinement Effect of Nanocages@Nanotubes Suppresses Polysulfide Shuttle Effect for High-Performance Lithium-Sulfur Batteries.

The shuttle effect of lithium polysulfides (LiPSs) severely hinders the development and commercialization of lithium-sulfur batteries, and the design of high-conductive carbon fiber-host material has become a key solution to suppress the shuttle effect. In this work, a unique Co/CoN-carbon nanocages@TiO2-carbon nanotubes structure (NC@TiO2-CNTs) is constructed using an electrospinning and nitriding process. Lithium-sulfur batteries using NC@TiO2-CNTs as cathode host materials exhibit high sulfur utilization (1527 mAh g-1 at 0.2 C) and can still maintain a discharge capacity of 663 mAh g-1 at a high current density of 5 C, and the capacity loss is only 0.056% per cycle during 500 cycles at 1 C. It is worth noting that even under extreme conditions (sulfur-loading = 90%, surface-loading = 5.0 mg cm-2 (S), and E/S = 6.63 µL mg-1), the lithium-sulfur batteries can still provide a reversible capacity of 4 mAh cm-2. Throughdensity functional theory calculations, it has been found that the Co/CoN heterostructures can adsorb and catalyze LiPSs conversion effectively. Simultaneously, the TiO2 can adsorb LiPSs and transfer Li+ selectively, achieving dual confinement for the shuttle effect of LiPSs (nanocages and nanotubes). The new findings provide a new performance enhancement strategy for the commercialization of lithium-sulfur batteries.

XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures.

Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R2 = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine.