Computer-assisted discovery of compounds with insecticidal activity against Musca domestica and Mythimna separata

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Discovery of novel natural products for mosquito control

Vector control plays a key role in reducing the public health burden of mosquito-borne diseases. Today's vector control strategies largely rely on synthetic insecticides that can have a negative environmental impact when applied outdoors and often become inefficient because of the mosquitoes' ability to develop resistance. An alternative and promising approach to circumvent these challenges involves the implementation of insecticides derived from nature (biopesticides) for vector control. Biopesticides can constitute naturally occurring organisms or substances derived from them that have lifespan-shortening effects on disease vectors such as mosquitoes. Here we present the discovery and evaluation of natural product-based biological control agents that can potentially be developed into biopesticides for mosquito control. We screened a natural product collection comprising 390 compounds and initially identified 26 molecules with potential ability to kill the larval stages of the yellow fever mosquito Aedes aegypti, which is responsible for transmitting viruses such as dengue, Zika, chikungunya and yellow fever. Natural products identified as hits in the screen were further evaluated for their suitability for biopesticide development. We show that a selection of the natural product top hits, bactobolin, maytansine and ossamycin, also killed the larval stages of the malaria-transmitting mosquito Anopheles gambiae as well as the adult form of both species. We have further explored the usefulness of crude extracts and preparations from two of the best candidates' sources (organisms of origin) for mosquitocidal activity, that is extracts from the two bacteria Burkholderia thailandensis and Streptomyces hygroscopicus var. ossamyceticus.

Revealing the molecular interplay of curcumin as Culex pipiens Acetylcholine esterase 1 (AChE1) inhibitor

Emergence of vector borne diseases has continued to take toll on millions of lives since its inception. The use of insecticides began as vector control strategy in the early 1900's but the menace of insects is still prevalent. Additionally, the inadequate use of organophosphates and carbamates which target acetylcholine esterase (AChE), are known to develop resistance amongst vectors of transmission and are toxic to humans. In this study, extensive computational screening was performed using homology modelling, molecular docking, molecular dynamics (MD) simulation and free energy change calculation, which highlighted curcumin as a lead molecule out of ~ 1700 phytochemicals against Culex pipiens AChE. In vivo larvicidal activity was carried out along with in vivo and in vitro AChE inhibition assay to determine the biochemical efficacy of curcumin. Our study reveals that curcumin induces mortality in Cx. pipiens at an early stage of its life cycle by AChE inhibition. This also underlines the use of curcumin as a coming-age natural product insecticide.

Simplex representation of molecular structure as universal QSAR/QSPR tool

We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.