The SistematX Web Portal of Natural Products: An Update

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Updating and profiling the natural product-likeness of Latin American compound libraries

Compound databases of natural products play a crucial role in drug discovery and development projects and have implications in other areas, such as food chemical research, ecology and metabolomics. Recently, we put together the first version of the Latin American Natural Product database (LANaPDB) as a collective effort of researchers from six countries to ensemble a public and representative library of natural products in a geographical region with a large biodiversity. The present work aims to conduct a comparative and extensive profiling of the natural product-likeness of an updated version of LANaPDB and the individual ten compound databases that form part of LANaPDB. The natural product-likeness profile of the Latin American compound databases is contrasted with the profile of other major natural product databases in the public domain and a set of small-molecule drugs approved for clinical use. As part of the extensive characterization, we employed several chemoinformatics metrics of natural product likeness. The results of this study will capture the attention of the global community engaged in natural product databases, not only in Latin America but across the world.

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries.

Elucidating the molecular mechanisms of essential oils' insecticidal action using a novel cheminformatics protocol

Essential oils (EOs) are a promising source for novel environmentally safe insecticides. However, the structural diversity of their compounds poses challenges to accurately elucidate their biological mechanisms of action. We present a new chemoinformatics methodology aimed at predicting the impact of essential oil (EO) compounds on the molecular targets of commercial insecticides. Our approach merges virtual screening, chemoinformatics, and machine learning to identify custom signatures and reference molecule clusters. By assigning a molecule to a cluster, we can determine its most likely interaction targets. Our findings reveal that the main targets of EOs are juvenile hormone-specific proteins (JHBP and MET) and octopamine receptor agonists (OctpRago). Three of the twenty clusters show strong similarities to the juvenile hormone, steroids, and biogenic amines. For instance, the methodology successfully identified E-Nerolidol, for which literature points indications of disrupting insect metamorphosis and neurochemistry, as a potential insecticide in these pathways. We validated the predictions through experimental bioassays, observing symptoms in blowflies that were consistent with the computational results. This new approach sheds a higher light on the ways of action of EO compounds in nature and biotechnology. It also opens new possibilities for understanding how molecules can interfere with biological systems and has broad implications for areas such as drug design.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

Natural Products from Annonaceae as Potential Antichagasic Agents

Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non-endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand-based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T. cruzi. Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13-Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T. cruzi, in addition to having in vitro activity against the epimastigote form.

Phytochemicals Targeting Oxidative Stress, Interconnected Neuroinflammatory, and Neuroapoptotic Pathways Following Radiation

The radiation for therapeutic purposes has shown positive effects in different contexts; however, it can increase the risk of many age-related and neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD). These different outcomes highlight a dose-response phenomenon called hormesis. Prevailing studies indicate that high doses of radiation could play several destructive roles in triggering oxidative stress, neuroapoptosis, and neuroinflammation in neurodegeneration. However, there is a lack of effective treatments in combating radiation-induced neurodegeneration, and the present drugs suffer from some drawbacks, including side effects and drug resistance. Among natural entities, polyphenols are suggested as multi-target agents affecting the dysregulated pathogenic mechanisms in neurodegenerative disease. This review discusses the destructive effects of radiation on the induction of neurodegenerative diseases by dysregulating oxidative stress, apoptosis, and inflammation. We also describe the promising effects of polyphenols and other candidate phytochemicals in preventing and treating radiation-induced neurodegenerative disorders, aiming to find novel/potential therapeutic compounds against such disorders.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity

Schistosomiasis is a chronic parasitic disease caused by trematodes of the genus Schistosoma; it is commonly caused by Schistosoma mansoni, which is transmitted by Bioamphalaria snails. Studies show that more than 200 million people are infected and that more than 90% of them live in Africa. Treatment with praziquantel has the best cost–benefit result on the market. However, hypersensitivity, allergy, and drug resistance are frequently presented after administration. From this perspective, ligand-based and structure-based virtual screening (VS) techniques were combined to select potentially active alkaloids against S. mansoni from an internal dataset (SistematX). A set of molecules with known activity against S. mansoni was selected from the ChEMBL database to create two different models with accuracy greater than 84%, enabling ligand-based VS of the alkaloid bank. Subsequently, structure-based VS was performed through molecular docking using four targets of the parasite. Finally, five consensus hits (i.e., five alkaloids with schistosomicidal potential), were selected. In addition, in silico evaluations of the metabolism, toxicity, and drug-like profile of these five selected alkaloids were carried out. Two of them, namely, 11,12-methylethylenedioxypropoxy and methyl-3-oxo-12-methoxy-n(1)-decarbomethoxy-14,15-didehydrochanofruticosinate, had plausible toxicity, metabolomics, and toxicity profiles. These two alkaloids could serve as starting points for the development of new schistosomicidal compounds based on natural products.