Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

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.

Unraveling the Significance of MET Focal Amplification in Lung Cancer: Integrative NGS, FISH, and IHC Investigation.

MET amplification (METamp) represents a promising therapeutic target in non-small cell lung cancer, but no consensus has been established to identify METamp-dependent tumors that could potentially benefit from MET inhibitors. In this study, an analysis of MET amplification/overexpression status was performed in a retrospectively recruited cohort comprising 231 patients with non-small cell lung cancer from Shanghai Chest Hospital (SCH cohort) using 3 methods: fluorescence in situ hybridization (FISH), hybrid capture-based next-generation sequencing, and immunohistochemistry for c-MET and phospho-MET. The SCH cohort included 130 cases known to be METamp positive by FISH and 101 negative controls. The clinical relevance of these approaches in predicting the efficacy of MET inhibitors was evaluated. Additionally, next-generation sequencing data from another 2 cohorts including 22,010 lung cancer cases were utilized to examine the biological characteristics of different METamp subtypes. Of the 231 cases, 145 showed MET amplification/overexpression using at least 1 method, whereas only half of them could be identified by all 3 methods. METamp can occur as focal amplification or polysomy. Our study revealed that the inconsistency between next-generation sequencing and FISH primarily occurred in the polysomy subtype. Further investigations indicated that compared with polysomy, focal amplification correlated with fewer co-occurring driver mutations, higher protein expressions of c-MET and phospho-MET, and higher incidence in acquired resistance than in de novo setting. Moreover, patients with focal amplification presented a more robust response to MET inhibitors compared with those with polysomy. Notably, a strong correlation was observed between focal amplification and programmed cell death ligand-1 expression, indicating potential therapeutic implications with combined MET inhibitor and immunotherapy for patients with both alterations. Our findings provide insights into the molecular complexity and clinical relevance of METamp in lung cancer, highlighting the role of MET focal amplification as an oncogenic driver and its feasibility as a primary biomarker to further investigate the clinical activity of MET inhibitors in future studies.

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, existing OCSR methods fall far short of our expectations for realistic requirements due to their poor recovery accuracy. In this paper, we developed a deep neural network model named ABC-Net (Atom and Bond Center Network) to predict graph structures directly. Based on the divide-and-conquer principle, we propose to model an atom or a bond as a single point in the center. In this way, we can leverage a fully convolutional neural network (CNN) to generate a series of heat-maps to identify these points and predict relevant properties, such as atom types, atom charges, bond types and other properties. Thus, the molecular structure can be recovered by assembling the detected atoms and bonds. Our approach integrates all the detection and property prediction tasks into a single fully CNN, which is scalable and capable of processing molecular images quite efficiently. Experimental results demonstrate that our method could achieve a significant improvement in recognition performance compared with publicly available tools. The proposed method could be considered as a promising solution to OCSR problems and a starting point for the acquisition of molecular information in the literature.

Hollow Superhelices Based on Chiral Europium Coordination Polymers.

The construction of helical nanotubes based on chiral coordination polymers (CPs) is an intriguing but challenging task, which is important for the development of functional materials that combine macroscopic chirality with tube-related properties. Here, we selected a chiral europium phosphonate system, e.g., Eu(NO3)3/R-,S-pempH2, and carried out a systematic work. By controlling the hydrothermal reaction conditions such as the pH value of the reaction mixture, the molar ratio and concentration of the reactants, we obtained block-like crystals of R/S-1b, rod-like crystals ofR/S-3r, hollow superhelices of R/S-2hh, and solid superhelices of R/S-4sh. In the latter two cases, the chirality has been successfully transferred and amplificated from the molecular level to the macroscopic level. Interestingly, compounds R/S-2hh and R/S-4sh have the same chemical composition of Eu(R/S-pempH)3×2H2O and show identical PXRD patterns, thus can be considered as the same material except for different morphologies. We further investigated their circularly polarized luminescence (CPL) properties and found that the hollow superhelix of R/S-2hh had a larger dissymmetry factor than the solid superhelix of R/S-4sh. This study not only provides the first example of hollow superhelices of chiral CPs, but also offers the possibility of modulating the chiroptical properties of CPs through morphological control.

Single-Molecule Magnet Rods: Remarkably Elongated Lanthanide Phosphonate Cores with Quasilinear Hydrazones.

Large metal-phosphonate clusters typically exhibit regular polyhedral, wheel-shaped, spherical, or capsule-shaped morphologies more effectively than high-aspect ratio topologies. A system of elongated lanthanide core topologies has now been synthesized by the reaction of lanthanide 1-naphthylmethylphosphonates and four differently terminated pyrazinyl hydrazones. Four new rod-shaped dysprosium phosphonate clusters, [Dy6(O3PC11H9)4(L1)4(µ4-O)(DMF)4]·2DMF·3MeCN·3H2O (1), [Dy8(O3PC11H9)4(L2)4(µ3-O)4(CO2)4(H2O)4]·6DMF·4MeCN·3H2O (2), [Dy12Na(O3PC11H9)6(L3)6(µ3-O)2(pyr)6]·DMF·2MeCN·H2O (3), and [Dy14(O3PC11H9)12(L4)8(µ3-O)2(DMF)4(MeOH)2(H2O)4]·5DMF·2MeCN·H2O (4), were obtained. Four single-pyrazinyl hydrazones function as pentadentate bis-chelate terminal co-ligands, coordinating the periphery of dysprosium phosphonate rods. A sodium ion serves as a cation template for constructing heterobimetallic 3 by occupying the void, demonstrating the ability to reliably control cluster length by modifying the hydrazone co-ligand structure and cation template. Additionally, it was observed that the elongation of the rods has a significant directional impact on the magnetic relaxation behavior, transitioning from a one-step process in 1 to a three-step process in 2, a two-step process in 3, and finally a two-step process in 4.

A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model.

Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation. To our surprise, a new-scaffold AR antagonist C2 with comparable bioactivity with Enz was identified at the initial round of screening. C2 showed pronounced inhibition on the transcriptional function (IC50 = 0.63 µM) and nuclear translocation of AR and significant antiproliferative and antimetastatic activity on PCa cell line of LNCaP. In addition, C2 exhibited a stronger ability to block the cell cycle of LNCaP than Enz at lower dose and superior AR specificity. Our study highlights the success of MIEC-SVM in discovering AR antagonists, and compound C2 presents a promising new scaffold for the development of AR-targeted therapeutics.

Comparative analysis of medical treatments for long-term control of normal tension glaucoma: A systematic review and model-based network meta-analysis.

BACKGROUND: To evaluate and compare the long-term efficacy of medical treatments for normal tension glaucoma (NTG) in controlling intraocular pressure (IOP), and establish a hierarchical ranking based on their effectiveness. 'Long-term' is defined as a treatment duration of over 12 weeks in randomised controlled trials (RCTs). METHODS: This systematic review and model-based network meta-analysis (MBNMA) collected data of 795 patients with 997 eyes from RCTs. Patients with NTG were selected based on strict inclusion/exclusion criteria, with randomsation procedures and masking as reported in the individual trials. Eight different medications were compared, including prostaglandin analogues, beta-blockers, brimonidine, unoprostone isopropyl, brovincamine, and palmitoylethanolamide (PEA). Notably, PEA is an oral medication, while other drugs are topical agents. RESULTS: Primary outcome is the long-term efficacy of IOP control across medications with different follow-up durations. Among the eight medications, PEA demonstrates the highest efficacy (Surface under the cumulative ranking, SUCRA = 7.46%), followed by two prostaglandin analogues: travoprost (SUCRA = 6.86%) and latanoprost (SUCRA = 6.76%), then two beta-blockers: nipradilol (SUCRA = 4.90%) and timolol (SUCRA = 4.89%). Both brimonidine and unoprostone isopropyl have SUCRA scores below 4.0%, indicating modest but limited efficacy. Brovincamine has the lowest SUCRA score (1.32%), reflecting minimal effectiveness. CONCLUSIONS: This study revealed PEA as a promising agent for long-term IOP control in NTG patients, suggesting potential use as primary or adjunctive therapy. The outcomes call for PEA's consideration in clinical practice and highlight the need for further research into its long-term efficacy and safety for NTG.

Chronic polystyrene microplastics exposure-induced changes in thick-shell mussel (Mytilus coruscus) metaorganism: A holistic perspective.

Microplastics have emerged as a significant global concern, particularly in marine ecosystems. While extensive research has focused on the toxicological effects of microplastics on marine animals and/or their associated microorganisms as two separate entities, the holistic perspective of the adaptability and fitness of a marine animal metaorganism-comprising the animal host and its microbiome-remains largely unexplored. In this study, mussel metaorganisms subjected chronic PS-MPs exposure experienced acute mortality but rapidly adapted. We investigated the response of innate immunity, digestive enzymes and their associated microbiomes to chronic PS-MPs exposure. We found that PS-MPs directly and indirectly interacted with the host and microbe within the exposure system. The adaptation was a joint effort between the physiological adjustments of mussel host and genetic adaptation of its microbiome. The mussel hosts exhibited increased antioxidant activity, denser gill filaments and increased immune cells, enhancing their innate immunity. Concurrently, the gill microbiome and the digestive gland microbiome respective selectively enriched for plastic-degrading bacteria and particulate organic matter-utilizing bacteria, facilitating the microbiome's adaptation. The microbial adaptation to chronic PS-MPs exposure altered the ecological roles of mussel microbiome, as evidenced by alterations in microbial interactions and nutrient cycling functions. These findings provided new insights into the ecotoxicological impact of microplastics on marine organisms from a metaorganism perspective.

A Clustering Approach for Atmospheric Phase Error Correction in Ground-Based SAR Using Spatial Autocorrelation.

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority.

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images.

The micro-deformation monitoring radar is usually based on Permanent Scatterer (PS) technology to realize deformation inversion. When the region is continuously monitored for a long time, the radar image amplitude and pixel variance will change significantly with time. Therefore, it is difficult to select phase-stable scatterers by conventional amplitude deviation methods, as they can seriously affect the accuracy of deformation inversion. For different regions studied within the same scenario, using a PS selection method based on the same threshold often increases the size of the deformation error. Therefore, this paper proposes a new PS selection method based on the Gaussian Mixture Model (GMM). Firstly, PS candidates (PSCs) are selected based on the pixels' amplitude information. Then, the amplitude deviation index of each PSC is calculated, and each pixel's probability values in different Gaussian distributions are acquired through iterations. Subsequently, the cluster types of pixels with larger probability values are designated as low-amplitude deviation pixels. Finally, the coherence coefficient and phase stability of low-amplitude deviation pixels are calculated. By comparing the probability values of each of the pixels in different Gaussian distributions, the cluster type with the larger probability, such as high-coherence pixels and high-phase stability pixels, is selected and designated as the final PS. Our analysis of the measured data revealed that the proposed method not only increased the number of PSs in the group, but also improved the stability of the number of PSs between groups.

APOE ε4-associated downregulation of the IL-7/IL-7R pathway in effector memory T cells: Implications for Alzheimer's disease.

INTRODUCTION: The apolipoprotein E (APOE) ε4 allele exerts a significant influence on peripheral inflammation and neuroinflammation, yet the underlying mechanisms remain elusive. METHODS: The present study enrolled 54 patients diagnosed with late-onset Alzheimer's disease (AD; including 28 APOE ε4 carriers and 26 non-carriers). Plasma inflammatory cytokine concentration was assessed, alongside bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) analysis of peripheral blood mononuclear cells (PBMCs). RESULTS: Plasma tumor necrosis factor α, interferon γ, and interleukin (IL)-33 levels increased in the APOE ε4 carriers but IL-7 expression notably decreased. A negative correlation was observed between plasma IL-7 level and the hippocampal atrophy degree. Additionally, the expression of IL-7R and CD28 also decreased in PBMCs of APOE ε4 carriers. ScRNA-seq data results indicated that the changes were mainly related to the CD4+ Tem (effector memory) and CD8+ Tem T cells. DISCUSSION: These findings shed light on the role of the downregulated IL-7/IL-7R pathway associated with the APOE ε4 allele in modulating neuroinflammation and hippocampal atrophy. HIGHLIGHTS: The apolipoprotein E (APOE) ε4 allele decreases plasma interleukin (IL)-7 and aggravates hippocampal atrophy in Alzheimer's disease. Plasma IL-7 level is negatively associated with the degree of hippocampal atrophy. The expression of IL-7R signaling decreased in peripheral blood mononuclear cells of APOE ε4 carriers Dysregulation of the IL-7/IL-7R signal pathways enriches T cells.

Assessing Uveitis Risk following Pediatric Down Syndrome Diagnosis: A TriNetX Database Study.

Background and Objectives: The risks of uveitis development among pediatric patients with Down syndrome (DS) remain unclear. Therefore, we aimed to determine the risk of uveitis following a diagnosis of DS. Materials and Methods: This multi-institutional retrospective cohort study utilized the TriNetX database to identify individuals aged 18 years and younger with and without a diagnosis of DS between 1 January 2000 and 31 December 2023. The non-DS cohort consisted of randomly selected control patients matched by selected variables. This included gender, age, ethnicity, and certain comorbidities. The main outcome is the incidence of new-onset uveitis. Statistical analysis of the uveitis risk was reported using hazard ratios (HRs) and 95% confidence intervals (CIs). Separate analyses of the uveitis risk among DS patients based on age groups and gender were also performed. Results: A total of 53,993 individuals with DS (46.83% female, 58.26% white, mean age at index 5.21 ± 5.76 years) and 53,993 non-DS individuals (45.56% female, 58.28% white, mean age at index 5.21 ± 5.76 years) were recruited from the TriNetX database. Our analysis also showed no overall increased risk of uveitis among DS patients (HR: 1.33 [CI: 0.89-1.99]) compared to the non-DS cohort across the 23-year study period. Subgroup analyses based on different age groups showed that those aged 0-1 year (HR: 1.36 [CI: 0.68-2.72]), 0-5 years (HR: 1.34 [CI: 0.75-2.39]), and 6-18 years (HR: 1.15 [CI: 0.67-1.96]) were found to have no association with uveitis risk compared to their respective non-DS comparators. There was also no increased risk of uveitis among females (HR: 1.49 [CI: 0.87-2.56]) or males (HR: 0.82 [CI: 0.48-1.41]) with DS compared to their respective non-DS comparators. Conclusions: Our study found no overall increased risk of uveitis following a diagnosis of DS compared to a matched control population.

A High-Efficiency Bioorthogonal Tumor-Membrane Reactor for In Situ Selective and Sustained Prodrug Activation.

The site-specific activation of bioorthogonal prodrugs has provided great opportunities for reducing the severe side effects of chemotherapy. However, the precise control of activation location, sustained drug production at the target site, and high bioorthogonal reaction efficiency in vivo remain great challenges. Here, we propose the construction of tumor cell membrane reactors in vivo to solve the above problems. Specifically, tumor-targeted liposomes with efficient membrane fusion capabilities are generated to install the bioorthogonal trigger, the amphiphilic tetrazine derivative, on the surface of tumor cells. These predecorated tumor cells act as many living reactors, transforming the tumor into a "drug factory" that in situ activates an externally delivered bioorthogonal prodrug, for example intratumorally injected transcyclooctene-caged doxorubicin. In contrast to the rapid elimination of cargo that is encapsulated and delivered by liposomes, these reactors permit stable retention of bioorthogonal triggers in tumor for 96 h after a single dose of liposomes via intravenous injection, allowing sustained generation of doxorubicin. Interestingly, an additional supplement of liposomes will compensate for the trigger consumed by the reaction and significantly improve the efficiency of the local reaction. This strategy provides a solution to the efficacy versus safety dilemma of tumor chemotherapy.

Identification and characterization of a novel hydroxylamine oxidase, DnfA, that catalyzes the oxidation of hydroxylamine to N2.

Nitrogen (N2) gas in the atmosphere is partially replenished by microbial denitrification of ammonia. Recent study has shown that Alcaligenes ammonioxydans oxidizes ammonia to dinitrogen via a process featuring the intermediate hydroxylamine, termed "Dirammox" (direct ammonia oxidation). However, the unique biochemistry of this process remains unknown. Here, we report an enzyme involved in Dirammox that catalyzes the conversion of hydroxylamine to N2. We tested previously annotated proteins involved in redox reactions, DnfA, DnfB, and DnfC, to determine their ability to catalyze the oxidation of ammonia or hydroxylamine. Our results showed that none of these proteins bound to ammonia or catalyzed its oxidation; however, we did find DnfA bound to hydroxylamine. Further experiments demonstrated that, in the presence of NADH and FAD, DnfA catalyzed the conversion of 15N-labeled hydroxylamine to 15N2. This conversion did not happen under oxygen (O2)-free conditions. Thus, we concluded that DnfA encodes a hydroxylamine oxidase. We demonstrate that DnfA is not homologous to any known hydroxylamine oxidoreductases and contains a diiron center, which was shown to be involved in catalysis via electron paramagnetic resonance experiments. Furthermore, enzyme kinetics of DnfA were assayed, revealing a Km of 92.9 ± 3.0 µM for hydroxylamine and a kcat of 0.028 ± 0.001 s-1. Finally, we show that DnfA was localized in the cytoplasm and periplasm as well as in tubular membrane invaginations in HO-1 cells. To the best of our knowledge, we conclude that DnfA is the first enzyme discovered that catalyzes oxidation of hydroxylamine to N2.

Direct and Specific Detection of Glyphosate Using a Phosphatase-like Nanozyme-Mediated Chemiluminescence Strategy.

Most organophosphorus pesticide (OP) sensors reported in the literature rely on the inhibition effect of OPs on the activity of acetylcholinesterase (AChE), which suffer from the drawbacks of lack of selective recognition of OPs, high cost, and poor stability. Herein, we proposed a novel chemiluminescence (CL) strategy for the direct detection of glyphosate (an organophosphorus herbicide) with high sensitivity and specificity, which is based on the porous hydroxy zirconium oxide nanozyme (ZrOX-OH) obtained via a facile alkali solution treatment of UIO-66. ZrOX-OH displayed excellent phosphatase-like activity, which could catalyze the dephosphorylation of 3-(2'-spiroadamantyl)-4-methoxy-4-(3'-phosphoryloxyphenyl)-1,2-dioxetane (AMPPD) to generate strong CL. The experimental results showed that the phosphatase-like activity of ZrOX-OH is closely related to the content of hydroxyl groups on their surface. Interestingly, ZrOX-OH with phosphatase-like properties exhibited a unique response to glyphosate because of the consumption of the surface hydroxyl group by the unique carboxyl group of glyphosates and was thus employed to develop a CL sensor for direct and selective detection of glyphosate without using bio-enzymes. The recovery for glyphosate detection of cabbage juice ranged from 96.8 to 103.0%. We believe that the as-proposed CL sensor based on ZrOX-OH with phosphatase-like properties supplies a simpler and more highly selective approach for OP assay and provides a new method for the development of CL sensors for the direct analysis of OPs in real samples.

Integrating Reliable Pt-S Bond-Mediated 3D DNA Nanomachine with Magnetic Separation in a Homogeneous Electrochemical Strategy for Exosomal MicroRNA Detection with Low Background and High Sensitivity.

Precise and sensitive analysis of exosomal microRNA (miRNA) is of great importance for noninvasive early disease diagnosis, but it remains a great challenge to detect exosomal miRNA in human blood samples because of their small size, high sequence homology, and low abundance. Herein, we integrated reliable Pt-S bond-mediated three-dimensional (3D) DNA nanomachine and magnetic separation in a homogeneous electrochemical strategy for the detection of exosomal miRNA with low background and high sensitivity. The 3D DNA nanomachine was easily prepared via a facile and rapid freezing method, and it was capable of resisting the influence of biothiols, thus endowing it with high stability. Notably, the as-developed magnetic 3D DNA nanomachine not only enabled the detection system to have a low background but also coupled with liposome nanocarriers to synergistically amplify the current signal. Consequently, by ingeniously combining the low background and multiple signal-amplification strategies in homogeneous electrochemical biosensing, highly sensitive detection of exosomal miRNA was successfully achieved. More significantly, with good anti-interference ability, the as-proposed method could effectively discriminate plasma samples from cancer patients and healthy subjects, thus showing a high potential for application in the nondestructive early clinical diagnosis of disease.

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular input line entry specification (SMILES) strings, which contain all the composition and structure information of molecules. However, current models usually suffer from the scarcity of labeled data. This results in a low generalization ability of SMILES-based DL models, which prevents them from competing with the state-of-the-art computational methods. In this study, we utilized the BiLSTM (bidirectional long short term merory) attention network (BAN) in which we employed a novel multi-step attention mechanism to facilitate the extracting of key features from the SMILES strings. Meanwhile, SMILES enumeration was utilized as a data augmentation method in the training phase to substantially increase the number of labeled data and enlarge the probability of mining more patterns from complex SMILES. We again took advantage of SMILES enumeration in the prediction phase to rectify model prediction bias and provide a more accurate prediction. Combined with the BAN model, our strategies can greatly improve the performance of latent features learned from SMILES strings. In 11 canonical absorption, distribution, metabolism, excretion and toxicity-related tasks, our method outperformed the state-of-the-art approaches.

BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study.

BACKGROUND: With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS: We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION: BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

Scopy: an integrated negative design python library for desirable HTS/VS database design.

BACKGROUND: High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. CONCLUSION: Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.