Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.

Frizzled receptors (FZDs) in Wnt signaling: potential therapeutic targets for human cancers.

Frizzled receptors (FZDs) are key contributors intrinsic to the Wnt signaling pathway, activation of FZDs triggering the Wnt signaling cascade is frequently observed in human tumors and intimately associated with an aggressive carcinoma phenotype. It has been shown that the abnormal expression of FZD receptors contributes to the manifestation of malignant characteristics in human tumors such as enhanced cell proliferation, metastasis, chemotherapy resistance as well as the acquisition of cancer stemness. Given the essential roles of FZD receptors in the Wnt signaling in human tumors, this review aims to consolidate the prevailing knowledge on the specific status of FZD receptors (FZD1-10) and elucidate their respective functions in tumor progression. Furthermore, we delineate the structural basis for binding of FZD and its co-receptors to Wnt, and provide a better theoretical foundation for subsequent studies on related mechanisms. Finally, we describe the existing biological classes of small molecule-based FZD inhibitors in detail in the hope that they can provide useful assistance for design and development of novel drug candidates targeted FZDs.

Design, synthesis and biological activity evaluation of novel covalent S-acylation inhibitors.

In order to obtain diverse S-acylation inhibitors and address the defects of existing S-acylation inhibitors, a series of novel covalent S-acylation inhibitors are designed through synthesis. According to the results of MTT assay, most compounds produce a better anti-proliferation effect on MCF-7, MGC-803 and U937 cell lines than 2-BP. Among them, 8d, 8i, 8j and 10e exert a significant inhibitory effect on MCF-7 cell, with the IC50 values falling below 20 µM. Besides, the toxic effects of some compounds on 3T3 cell line are less significant than 2-BP. According to the results of acyl-biotin exchange (ABE) experiment, most of them could inhibit S-acylation, and 8i performs best in this respect, with the inhibitory rate reaching 89.3% at the concentration of 20 µM. The results of molecular docking show the conjugation of 8i with surrounding amino acids. Additionally, 8i could not only suppress the migration of MCF-7 cell line, but also cause it to stagnate in G0/G1 phase, thus promoting cell apoptosis.

CMNPD: a comprehensive marine natural products database towards facilitating drug discovery from the ocean.

Marine organisms are expected to be an important source of inspiration for drug discovery after terrestrial plants and microorganisms. Despite the remarkable progress in the field of marine natural products (MNPs) chemistry, there are only a few open access databases dedicated to MNPs research. To meet the growing demand for mining and sharing for MNPs-related data resources, we developed CMNPD, a comprehensive marine natural products database based on manually curated data. CMNPD currently contains more than 31 000 chemical entities with various physicochemical and pharmacokinetic properties, standardized biological activity data, systematic taxonomy and geographical distribution of source organisms, and detailed literature citations. It is an integrated platform for structure dereplication (assessment of novelty) of (marine) natural products, discovery of lead compounds, data mining of structure-activity relationships and investigation of chemical ecology. Access is available through a user-friendly web interface at https://www.cmnpd.org. We are committed to providing a free data sharing platform for not only professional MNPs researchers but also the broader scientific community to facilitate drug discovery from the ocean.

Comparative efficacy and safety of disease-modifying therapies in patients with relapsing multiple sclerosis: A systematic review and network meta-analysis.

BACKGROUND: Currently, 19 disease-modifying therapies (DMTs) have been approved for the treatment of patients with relapsing forms of multiple sclerosis (RMS). OBJECTIVE: The objective of this study was to conduct a systematic review and network meta-analysis to evaluate the efficacy and safety of DMTs in adults with RMS. METHODS: We searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, the Food and Drug Administration, and European Medicines Agency websites for randomized controlled trials (RCTs) (from inception to July 2021). Eligible RCTs evaluated approved treatments for RMS as monotherapy and reported at least one of the primary outcome measures of interest. The primary outcome was efficacy (annualized relapse rate and 12-week confirmed disability progression) and safety (serious adverse events [AEs] and discontinuation due to AEs). We assessed the risk of bias (RoB) of included studies using the Cochrane RoB tool version 2.0 (https://www.bmj.com/content/343/bmj.d5928) for RCTs. Surface under the cumulative ranking (SUCRA) was used to rank therapies and to assess quality of general evidence, respectively. The Grading of Recommendations Assessment, Development and Evaluation framework was used to rank therapies and to assess quality of general evidence. RESULTS: A total of 43 records represent 45 RCTs selected for network meta-analysis. In total, 30,720 participants (median of 732; interquartile range: 248-931) were included, of which 67% were female. By SUCRA analysis, alemtuzumab (94.3%) presented the highest probability of being the best alternative for annualized relapse rate, whereas ofatumumab (93.5%) presented the highest probability of being the best alternative for 12-week confirmed disability progression. Interferon beta-1b subcutaneous (87.0%) presented the highest probability of the best safety among all DMTs for serious AEs, whereas alemtuzumab (92.4%) presented the highest probability of the best safety among all DMTs for discontinuation due to AEs. CONCLUSION: Network meta-analysis shows that alemtuzumab and ofatumumab present the highest efficacy among DMTs. Because there is little difference between these probabilities for many treatments, health professionals should use clinical shared decision making when formulating treatment plans with patients.

Large-scale screening of i-motif binding compounds in the silkworm, Bombyx mori.

DNA secondary structure i-motif involves in gene transcription and considered as a novel target for cancer gene therapy. I-motif-binding compounds can either stabilize or destroy the structure, resulting in change in target gene transcription. In this study, a large-scale screening of binding compounds was conducted using the i-motif structure of BmPOUM2, a transcription factor in silkworm, Bombyx mori. Surface plasmon resonance imaging (SPRi) high-throughput binding screening of 3642 compounds found 60 compounds with an binding affinity Kd of 10-7-10-6 M. SPRi and circular dichroism (CD) double screening demonstrated that the BmPOUM2 i-motif structure bound the compounds IF1, IF3, IF4, IF6 and IF7 with Kd of 10-7 M, and the compounds IF2 and tetrakis (4-N-methylpyridyl) porphine (TMPyP4) with a Kd of 10-8 M. Interestingly, IF2, IF3, IF4, IF6 and IF7 promoted the binding of the i-motif-binding protein BmILF with the i-motif structure, whereas TMPyP4 inhibited the binding. This study provided a list of compounds that have potential applications in functional analysis of i-motif structure and in pesticide and drug development through gene transcription regulation by i-motif structure.

Transcription factor OsSGL is a regulator of starch synthesis and grain quality in rice.

Starch biosynthesis during rice endosperm development is important for grain quality, as it influences grain size and physico-chemical properties, which together determine rice eating quality. Cereal starch biosynthetic pathways have been comprehensively investigated; however, their regulation, especially by transcriptional repressors remains largely unknown. Here, we identified a DUF1645 domain-containing protein, STRESS_tolerance and GRAIN_LENGTH (OsSGL), that participates in regulating rice starch biosynthesis. Overexpression of OsSGL reduced total starch and amylose content in the endosperm compared with the wild type. Chromatin immunoprecipitation sequencing and RNA-seq analyses indicated that OsSGL targets the transcriptional activity of several starch and sucrose metabolism genes. In addition, ChIP-qPCR, yeast one-hybrid, EMSA and dual-luciferase assays demonstrated that OsSGL directly inhibits the expression of SUCROSE SYNTHASE 1 (OsSUS1) in the endosperm. Furthermore, OsSUS1 interacts with OsSGL to release its transcriptional repression ability. Unexpectedly, our results also show that knock down and mutation of OsSGL disrupts the starch biosynthetic pathway, causing lower starch and amylose content. Therefore, our findings demonstrate that accurate control of OsSGL homeostasis is essential for starch synthesis and grain quality. In addition, we revealed the molecular mechanism of OsSGL in regulating starch biosynthesis-related genes, which are required for grain quality.

Establishment of inhibitor screening and validation system for tryptophanyl tRNA synthetase using surface plasmon resonance.

With the increase in throughput and sensitivity, biophysical technology has become a major component of the early drug discovery phase. Surface plasmon resonance technology (SPR) is one of the most widely used biophysical technologies. It has the advantages of circumventing labeling, molecular weight limitations, and neglect of low affinity interactions, etc., and provides a robust platform for hit to lead discovery and optimization. Here, we successfully established a reliable and repeatable tryptophanyl tRNA synthetase (TrpRS) SPR high-throughput screening and validation system by optimizing the TrpRS tag, TrpRS immobilization methodology, and the buffer conditions. When TrpRS was immobilized on Streptavidin (SA) sensor chip, the substrate competitive inhibitor indolmycin exhibited the best binding affinity in HBS-P (10 mM HEPES, 150 mM NaCl, 0.05% surfactant P-20, pH 7.4), 1 mM ATP and MgCl2, with a KD (dissociation equilibrium constant) value of 0.6 ± 0.1 µM. The Z-factor values determined in the screening assays were all larger than 0.9. We hope that our proposed research ideas and methods may provide a scientific basis for establishing SPR analysis of other drug targets, accelerate the discovery and optimization of target lead compounds, and assist the clinical application of next-generation drugs.

AIScaffold: A Web-Based Tool for Scaffold Diversification Using Deep Learning.

Molecular scaffolds are widely used in drug design. Many methods and tools have been developed to utilize the information in scaffolds. Scaffold diversification is frequently used by medicinal chemists in tasks such as lead compound optimization, but tools for scaffold diversification are still lacking. Here, we propose AIScaffold (https://iaidrug.stonewise.cn), a web-based tool for scaffold diversification using the deep generative model. This tool can perform large-scale (up to 500,000 molecules) diversification in several minutes and recommend the top 500 (top 0.1%) molecules. Features such as site-specific diversification are also supported. This tool can facilitate the scaffold diversification process for medicinal chemists, thereby accelerating drug design.

Chemical Space, Scaffolds, and Halogenated Compounds of CMNPD: A Comprehensive Chemoinformatic Analysis.

The comprehensive marine natural products database (CMNPD) is a new free access and comprehensive database developed originally by Lyu's team of our research group, including more than 30 000 marine natural products (MNPs) reported from the 1960s. In this article, we aimed to present CMNPD's value in drug discovery and to present several characteristics of MNPs based on our new comprehensive data. We used chemoinformatic analysis methods to report the molecular properties, chemical space, and several scaffold assessments of CMNPD compared with several databases. Then, we reported the characteristics of MNPs from the aspect of halogens, comparing MNPs with terrestrial natural products (TNPs) and drugs. We found that CMNPD had a low proportion (2.91%) of scaffolds utilized by drugs, and high similarities between CMNPD and NPAtlas (a microbial natural products database), which are worth further investigation. The proportion of bromides in MNPs is outstandingly higher (11.0%) in contrast to other halogens. Furthermore, the results showed great differences in halogenated structures between MNPs and drugs, especially brominated substructures. Finally, we found that many marine species (2.52%) reported only halogenated compounds. It can be concluded from these results that CMNPD is a promising source for drug discovery and has many scientific issues relative to MNPs that need to be further investigated.

Prospective association between depressive symptoms and stroke risk among middle-aged and older Chinese.

BACKGROUND: This study aimed to assess the association between baseline symptoms and changes in depressive symptoms and stroke incidents. METHODS: We used data from the Chinese Health and Retirement Longitudinal Study conducted in 2013, 2015, and 2018. In total, 10,100 individuals aged ≥45 years and without a history of stroke in 2013 were included. Depressive symptoms were measured using the 10-item version of the Center for Epidemiological Studied Depression scale (elevated depressive symptoms cutoff ≥10). Changes of depressive symptoms were assessed by two successive surveys (stable low/no, recent onset, recently remitted, and stable high depressive symptoms). We assessed whether baseline depressive symptoms and changes of them were associated with stroke incidents reported through 2018. Logistic regression analyses adjusted for age, gender, education, marital status and other potential confounders were performed. RESULTS: For the analysis of baseline depressive symptoms and stroke (n = 10,100), 545 (5.4%) reported stroke incidents in the following 5-year period. Individuals with elevated depressive symptoms in 2013 experienced a markedly higher stroke risk (odds ratio (OR) = 1.53, 95% confidence interval (CI) = 1.28-1.84) compared with those without elevated depressive symptoms. In the analysis of changes in depressive symptoms (n = 8491, 430 (5.1%) stroke events), participants with stable high (OR = 2.01, 95% CI = 1.58-2.56) and recent-onset (OR = 1.39, 95% CI = 1.04-1.85) depressive symptoms presented higher stroke risk compared to those with stable low/no depressive symptoms, while recently remitted symptoms (OR = 1.12, 95% CI = 0.80-1.57) were not associated with stroke risk. CONCLUSIONS: In conclusion, stable high and newly started depressive symptoms were associated with increased stroke risk, whereas the improvement of depressive symptoms was not related to increase in stroke risk, suggesting that stroke risk may be decreased by effective management of depressive symptoms.

ETCM: an encyclopaedia of traditional Chinese medicine.

Traditional Chinese medicine (TCM) is not only an effective solution for primary health care, but also a great resource for drug innovation and discovery. To meet the increasing needs for TCM-related data resources, we developed ETCM, an Encyclopedia of Traditional Chinese Medicine. ETCM includes comprehensive and standardized information for the commonly used herbs and formulas of TCM, as well as their ingredients. The herb basic property and quality control standard, formula composition, ingredient drug-likeness, as well as many other information provided by ETCM can serve as a convenient resource for users to obtain thorough information about a herb or a formula. To facilitate functional and mechanistic studies of TCM, ETCM provides predicted target genes of TCM ingredients, herbs, and formulas, according to the chemical fingerprint similarity between TCM ingredients and known drugs. A systematic analysis function is also developed in ETCM, which allows users to explore the relationships or build networks among TCM herbs, formulas,ingredients, gene targets, and related pathways or diseases. ETCM is freely accessible at http://www.nrc.ac.cn:9090/ETCM/. We expect ETCM to develop into a major data warehouse for TCM and to promote TCM related researches and drug development in the future.

Target Prediction Model for Natural Products Using Transfer Learning.

A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model's outputs of natural products are reliable. Case studies have proved our model's performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing.

[Pathogenesis of Secondary Pulmonary Tuberculosis and Role of Cord Factor in Secondary Infection].

The primary and secondary tuberculosis features two completely different pathogenesis.At present,the pathogenesis of primary tuberculosis has been clear,whereas that of secondary tuberculosis remains unclear.In order to decipher the mechanism of secondary infection of Mycobacterium tuberculosis and provide insights into vaccine research and drug development,this paper reviews the problems of the widely accepted mechanism of secondary infection,the new findings of the research on the mechanism,as well as the role of cord factors.

p120-catenin suppresses proliferation and tumor growth of oral squamous cell carcinoma via inhibiting nuclear phospholipase C-γ1 signaling.

p120-catenin (p120) serves as a stabilizer of the calcium-dependent cadherin-catenin complex and loss of p120 expression has been observed in several types of human cancers. The p120-dependent E-cadherin-ß-catenin complex has been shown to mediate calcium-induced keratinocyte differentiation via inducing activation of plasma membrane phospholipase C-γ1 (PLC-γ1). On the other hand, PLC-γ1 has been shown to interact with phosphatidylinositol 3-kinase enhancer in the nucleus and plays a critical role in epidermal growth factor-induced proliferation of oral squamous cell carcinoma (OSCC) cells. To determine whether p120 suppresses OSCC proliferation and tumor growth via inhibiting PLC-γ1, we examined effects of p120 knockdown or p120 and PLC-γ1 double knockdown on proliferation of cultured OSCC cells and tumor growth in xenograft OSCC in mice. The results showed that knockdown of p120 reduced levels of PLC-γ1 in the plasma membrane and increased levels of PLC-γ1 and its signaling in the nucleus in OSCC cells and OSCC cell proliferation as well as xenograft OSCC tumor growth. However, double knockdown of p120 and PLC-γ1 or knockdown of PLC-γ1 alone did not have any effect. Immunohistochemical analysis of OSCC tissue from patients showed a lower expression level of p120 and a higher expression level of PLC-γ1 compared with that of adjacent noncancerous tissue. These data indicate that p120 suppresses OSCC cell proliferation and tumor growth by inhibiting signaling mediated by nuclear PLC-γ1.

Functional Characterization and Structural Basis of an Efficient Di-C-glycosyltransferase from Glycyrrhiza glabra.

A highly efficient di-C-glycosyltransferase GgCGT was discovered from the medicinal plant Glycyrrhiza glabra. GgCGT catalyzes a two-step di-C-glycosylation of flopropione-containing substrates with conversion rates of >98%. To elucidate the catalytic mechanisms of GgCGT, we solved its crystal structures in complex with UDP-Glc, UDP-Gal, UDP/phloretin, and UDP/nothofagin, respectively. Structural analysis revealed that the sugar donor selectivity was controlled by the hydrogen-bond interactions of sugar hydroxyl groups with D390 and other key residues. The di-C-glycosylation capability of GgCGT was attributed to a spacious substrate-binding tunnel, and the G389K mutation could switch di- to mono-C-glycosylation. GgCGT is the first di-C-glycosyltransferase with a crystal structure, and the first C-glycosyltransferase with a complex structure containing a sugar acceptor. This work could benefit the development of efficient biocatalysts to synthesize C-glycosides with medicinal potential.

Precision and accuracy of commercial assays for vancomycin therapeutic drug monitoring: evaluation based on external quality assessment scheme.

BACKGROUND: Vancomycin remains a mainstay of the treatment of Gram-positive bacterial infections. It is crucial to accurately determine vancomycin serum concentration for adequate dose adjustment. OBJECTIVES: To evaluate the precision and accuracy of commercial assay techniques for vancomycin concentration and to assess the comparability of vancomycin detection methods in Chinese laboratories. METHODS: Human serum samples spiked with known concentrations of vancomycin were provided to laboratories participating in the external quality assessment scheme (EQAS). Assay methods included chemiluminescence, enzyme immunoassay (EIA) and so on. The dispersion of the measurements was analysed and the robust coefficient of variation (rCV), relative percentage difference (RPD) and satisfactory rate for method groups were calculated. Moreover, performance of the Chinese laboratories was assessed. RESULTS: A total of 657 results from 75 laboratories were collected, including 84 samples from 10 Chinese laboratories. The median rCV, median RPD and satisfactory rates classified by methods ranged from 1.85% to 15.87%, -14.75% to 13.34% and 94.59% to 100.00%, respectively. Significant differences were seen in precision, between kinetic interaction of microparticles in solution (KIMS) and other methods, and in accuracy, between enzyme-multiplied immunoassay technique (EMIT), fluorescence polarization immunoassay (FPIA) and other techniques. Vancomycin detection in China mainly depended on the chemiluminescence and EMIT methods, which tended to result in lower measurements. CONCLUSIONS: Although almost all assays in this study achieved an acceptable performance for vancomycin serum concentration monitoring, obvious inconsistencies between methods were still observed. Chinese laboratories were more likely to underestimate vancomycin concentrations. Thus, recognizing inconsistencies between methods and regular participation in vancomycin EQAS are essential.

Engineering a bacterial sialyltransferase for di-sialylation of a therapeutic antibody.

Terminal α-2,6-sialylation of N-glycans is a humanized glycosylation that affects the properties and efficacy of therapeutic glycoproteins. Fc di-sialylation (a biantennary N-glycan with two α-2,6-linked sialic acids) of IgG antibodies imparts them with enhanced anti-inflammatory activity and other roles. However, the microheterogeneity of N-glycoforms presents a challenge for therapeutic development. Therefore, controlled sialylation has drawn considerable attention, but direct access to well-defined di-sialylated antibodies remains limited. Herein, a one-pot three-enzyme protocol was developed by engineering a bacterial sialyltransferase to facilitate the modification of therapeutic antibodies with N-acetylneuraminic acid or its derivatives towards optimized glycosylation. To overcome the low proficiency of bacterial sialyltransferase in antibody remodeling, the Photobacterium sp. JT-ISH-224 α-2,6-sialyltransferase (Psp2,6ST) was genetically engineered by terminal truncation and site-directed mutagenesis based on its protein crystal structure. With the optimized reaction conditions and using activity-based screening of various Psp2,6ST variants, a truncated mutant Psp2,6ST (111-511)-His6 A235M/A366G was shown to effectively improve the catalytic efficiency of antibody di-sialylation. Herceptin and the donor substrate promiscuity allow the introduction of bioorthogonal modifications of N-acetylneuraminic acid into antibodies for site-specific conjugation. 2-AB hydrophilic interaction chromatography analysis of the released N-glycans and intact mass characterization confirmed the high di-sialylation of Herceptin via the optimized one-pot three-enzyme reaction. This study established a versatile enzymatic approach for producing highly di-sialylated IgG antibodies. It provides new insights into engineering bacterial sialyltransferase for adaptation to the enzymatic glycoengineering of therapeutic antibodies and the glycosite-specific conjugation of antibodies.

DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning.

The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as their core structures is an efficient way to obtain potential drug candidates. We propose a scaffold-based molecular generative model for drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including Bemis-Murcko scaffolds, cyclic skeletons, and scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. The generated compounds were evaluated by molecular docking in DRD2 targets, and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.

TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network.

Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is anticipated that the integration of deep learning would also contribute to the prosperity of 3D fingerprints. Here, we unprecedentedly introduce deep learning into 3D small molecule fingerprints, presenting a new one we termed as the three-dimensional force fields fingerprint (TF3P). TF3P is learned by a deep capsular network whose training is in no need of labeled data sets for specific predictive tasks. TF3P can encode the 3D force fields information of molecules and demonstrates the stronger ability to capture 3D structural changes, to recognize molecules alike in 3D but not in 2D, and to identify similar targets inaccessible by other 2D or 3D fingerprints based on only ligands similarity. Furthermore, TF3P is compatible with both statistical models (e.g., similarity ensemble approach) and machine learning models. Altogether, we report TF3P as a new 3D small molecule fingerprint with a promising future in ligand-based drug discovery. All codes are written in Python and available at https://github.com/canisw/tf3p.