Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Soft Tissue Sarcoma: An Insight on Biomarkers at Molecular, Metabolic and Cellular Level

Simple Summary

Soft tissue sarcoma is a rare mesenchymal malignancy. Despite the advancements in the fields of radiology, pathology and surgery, these tumors often recur locally and/or with metastatic disease. STS is considered to be a diagnostic challenge due to the large variety of histological subtypes with clinical and histopathological characteristics which are not always distinct. One of the important clinical problems is a lack of useful biomarkers. Therefore, the discovery of biomarkers that can be used to detect tumors or predict tumor response to chemotherapy or radiotherapy could help clinicians provide more effective clinical management.

Abstract

Soft tissue sarcomas (STSs) are a heterogeneous group of rare tumors. Although constituting only 1% of all human malignancies, STSs represent the second most common type of solid tumors in children and adolescents and comprise an important group of secondary malignancies. Over 100 histologic subtypes have been characterized to date (occurring predominantly in the trunk, extremity, and retroperitoneum), and many more are being discovered due to molecular profiling. STS mortality remains high, despite adjuvant chemotherapy. New prognostic stratification markers are needed to help identify patients at risk of recurrence and possibly apply more intensive or novel treatments. Recent scientific advancements have enabled a more precise molecular characterization of sarcoma subtypes and revealed novel therapeutic targets and prognostic/predictive biomarkers. This review aims at providing a comprehensive overview of the most relevant cellular, molecular and metabolic biomarkers for STS, and highlight advances in STS-related biomarker research.

Database Independent Automated Structure Elucidation of Organic Molecules Based on IR, 1H NMR, 13C NMR, and MS Data

Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, 1H and 13C NMR, and MS tabular data. The algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available (http://schmarnica.si).

Machine Learning Enhanced Spectrum Recognition Based on Computer Vision (SRCV) for Intelligent NMR Data Extraction

A machine learning enhanced spectrum recognition system called spectrum recognition based on computer vision (SRCV) for data extraction from previously analyzed ¹³C and ¹H NMR spectra has been developed. The intelligent system was designed with four function modules to extract data from three areas of NMR images, including ¹³C and ¹H chemical shifts, the integral, and the range of the shift values. During this study, three machine learning models were pretrained for number recognition, which is the key procedure for NMR data extraction. The k nearest neighbor (kNN) method was selected with optimized k (k = 4), which displayed a 100% recognition rate. Subsequently, the performance of SRCV was tested and validated to have high accuracy with a short processing time (11-21 s) for each NMR spectral image. Our spectrum recognizer enables high-throughput ¹³C and ¹H NMR data extraction from abundant spectra in the literature and has the potential to be used for spectral database construction. In addition, the system may be applicable to be developed for data import to computer-assisted structure elucidation systems, which would automate this procedure significantly. SRCV can be accessed in GitHub (https://github.com/WJmodels/SRCV).