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.

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.

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.

Privileged Scaffold Analysis of Natural Products with Deep Learning-based Indication Prediction Model.

Natural products play a vital role in the drug discovery and development process as an important source of reliable and novel lead structures. But the existing criteria for drug leads were usually developed for synthetic compounds and cannot be directly applied to identify lead scaffolds from natural products. To solve this problem, we propose a method to predict indications and identify privileged scaffolds of natural products for drug design. A deep learning model was built to predict indications for natural products. Entropy-based information metrics were used to identify the privileged scaffolds for each indication and a Privileged Scaffold Dataset (PSD) of natural products was constructed. The PSD could serve as a novel source of lead compounds and circumvent existing drug patents. This method could be generalized by replacing the training set, the prediction algorithm, and the compound set, to obtain more personalized-PSDs.

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.

Comprehensive analysis of human IgG Fc N-glycopeptides and construction of a screening model for colorectal cancer.

Currently, analyzing intact glycopeptides remains a challengeable task. Considerable progress has been achieved in the knowledge of immunoglobulin G (IgG) glycans in patients with colorectal cancer (CRC), whereas data on IgG Fc N-glycopeptides are scarce in the literature. To fill this gap in knowledge, we developed a rapid and effective method to obtain and analyze IgG Fc N-glycopeptides in the plasma from 46 CRC patients and 67 healthy individuals using chitosan@poly (glycidyl methacrylate) @iminodiacetic acid (CS@PGMA@IDA) nanomaterial in combination with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS). A total of 29 N-glycopeptides were detected and analyzed. Compared with healthy individuals, CRC patients had increased levels of N-acteylglucosamine, yet decreased levels of galactosylation, fucosylation and sialylation. Further, a multivariate logistic regression model was developed using the levels of IgG Fc N-glycopeptides to distinguish CRC patients from healthy individuals, and the prediction performance was good, with an average AUC of the ROC curves of 0.893. SIGNIFICANCE: In this study, we proposed a strategy for obtaining and analyzing IgG glycopeptides using CS@PGMA@IDA nanomaterial in combination with MALDI-TOF-MS. Using this strategy, IgG Fc N-glycopeptides were analyzed in the plasma of CRC patients, and our findings indicated that glycosylation levels in the IgG Fc region were closely related to CRC. By using the IgG N-glycopeptide enrichment method and screening model designed in this study, early large-scale colorectal cancer screening can be implemented easily and fast.