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.

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/.

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.

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.

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.

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/.

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.

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.

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.

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.

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.

Network-Based Methods and Their Applications in Drug Discovery.

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

A seed expansion-based method to identify essential proteins by integrating protein-protein interaction sub-networks and multiple biological characteristics.

BACKGROUND: The identification of essential proteins is of great significance in biology and pathology. However, protein-protein interaction (PPI) data obtained through high-throughput technology include a high number of false positives. To overcome this limitation, numerous computational algorithms based on biological characteristics and topological features have been proposed to identify essential proteins. RESULTS: In this paper, we propose a novel method named SESN for identifying essential proteins. It is a seed expansion method based on PPI sub-networks and multiple biological characteristics. Firstly, SESN utilizes gene expression data to construct PPI sub-networks. Secondly, seed expansion is performed simultaneously in each sub-network, and the expansion process is based on the topological features of predicted essential proteins. Thirdly, the error correction mechanism is based on multiple biological characteristics and the entire PPI network. Finally, SESN analyzes the impact of each biological characteristic, including protein complex, gene expression data, GO annotations, and subcellular localization, and adopts the biological data with the best experimental results. The output of SESN is a set of predicted essential proteins. CONCLUSIONS: The analysis of each component of SESN indicates the effectiveness of all components. We conduct comparison experiments using three datasets from two species, and the experimental results demonstrate that SESN achieves superior performance compared to other methods.

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.

Optical enhancement of highly efficient organic-inorganic oxyfluoride red phosphors via the cation co-doping strategy.

Herein, a new organic cationic matrix [N(CH3)4]3MoO3F3 suitable for Mn4+ doping was constructed. Due to the large steric hindrance of N[CH3]4+ (TMA), charge compensation defects can be effectively prevented in the heterovalent Mn4+-doping process, and a high IQE (91.05%) was obtained. Through the cation co-doping strategy, Mg2+/Zn2+/Li+ cations were introduced into the Mo6+ cationic site, which improved the crystallinity of the matrix and reduced energy losses, so as to improve luminescence intensity, QE, thermal stability, water stability and other spectral properties. Meanwhile, [N(CH3)4]2TiF6:Mn4+ phosphors with the same TMA organic cation and equivalent Mn4+ doping were synthesized for comparison, and the effects of the Mg2+ cation co-doping strategy on the spectral properties of phosphors with different matrix types (fluoride/oxyfluoride) and substitution types (equivalent/non-equivalent) were analyzed. These findings provide the basis for the preparation of new luminescent materials. Furthermore, according to the optical properties exhibited by these phosphors, they are packaged into WLED devices with excellent photoelectric properties, which are suitable for indoor lighting and display fields.

Pharmaceutical Cocrystal Discovery via 3D-SMINBR: A New Network Recommendation Tool Augmented by 3D Molecular Conformations.

Cocrystals have significant potential in various fields such as chemistry, material, and medicine. For instance, pharmaceutical cocrystals have the ability to address issues associated with physicochemical and biopharmaceutical properties. However, it can be challenging to find proper coformers to form cocrystals with drugs of interest. Herein, a new in silico tool called 3D substructure-molecular-interaction network-based recommendation (3D-SMINBR) has been developed to address this problem. This tool first integrated 3D molecular conformations with a weighted network-based recommendation model to prioritize potential coformers for target drugs. In cross-validation, the performance of 3D-SMINBR surpassed the 2D substructure-based predictive model SMINBR in our previous study. Additionally, the generalization capability of 3D-SMINBR was confirmed by testing on unseen cocrystal data. The practicality of this tool was further demonstrated by case studies on cocrystal screening of armillarisin A (Arm) and isoimperatorin (iIM). The obtained Arm-piperazine and iIM-salicylamide cocrystals present improved solubility and dissolution rate compared to their parent drugs. Overall, 3D-SMINBR augmented by 3D molecular conformations would be a useful network-based tool for cocrystal discovery. A free web server for 3D-SMINBR can be freely accessed at http://lmmd.ecust.edu.cn/netcorecsys/.