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Real-time toxicity prediction of Aconitum stewing system using extractive electrospray ionization mass spectrometry

Acta Pharmaceutica Sinica B 2020年第5期10卷 903-912页

作者： Zi-Dong Qiu Jin-Long Chen Wen Zeng Ying Ma Tong Chen Jin-Fu Tang Chang-Jiang-Sheng Lai Lu-Qi Huang State Key Laboratory of Natural Medicines China Pharmaceutical UniversityNanjing 210009China National Resource Center for Chinese Materia Medica China Academy of Chinese Medical SciencesState Key Laboratory Breeding Base of Dao-di HerbsBeijing 100700China

Due to numerous obstacles such as complex matrices,real-time monitoring of complex reaction systems(e.g.,medicinal herb stewing system)has always been a challenge though great values for safe and rational use of ***,facilitated by the potential ability on the tolerance of complex matrices of extractive electrospray ionization mass spectrometry,a device was established to realize continuous sampling and real-time quantitative analysis of herb stewing system for the first time.A complete analytical strategy,including data acquisition,data mining,and data evaluation was proposed and implemented with overcoming the usual difficulties in real-time mass spectrometry *** complex Fuzi(the lateral root of Aconitum)-meat stewing systems were real-timely monitored in150 min by qualitative and quantitative analysis of the nine key alkaloids *** results showed that the strategy worked perfectly and the toxicity of the systems were evaluated and predicated *** with trotters effectively accelerated the detoxification of Fuzi soup and reduced the overall toxicity to 68%,which was recommended to be used practically for treating rheumatic arthritis and enhancing *** established strategy was versatile,simple,and accurate,which would have a wide application prospect in real-time analysis and evaluation of various complex reaction systems.

关键词： Real-time extractive electrospray ionization mass spectrometry Toxic alkaloids Data mining Aconitine Aconitum--meat stewing system toxicity prediction

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In silico toxicity prediction by support vector machine and SMILES representation-based string kernel

In silico toxicity prediction by support vector machine and ...

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第十一届全国计算（机）化学学术会议

作者：曹东升许青松梁逸曾 Research center of modernization of traditional Chinese medicines Central South UniversityChangsha 410083P.R.China School of Mathematical Sciences and Computing Technology Central South UniversityChangsha 410083P.R.China

＜正＞There is a great need to assess the harmful effects or toxicities of chemicals to which man is *** the present paper,the simplified molecular input line entry specification（SMILES） representation-based string kernel,together with the state-of-the-art support vector machine（SVM） algorithm,were developed to classify the toxicity of chemicals from Distributed Structure-Searchable toxicity（DSSTox） database *** this method,the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds（double,triple,and stereochemistry）.Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the *** model validation approaches,five-fold cross-validation and independent validation set,were used for assessing the predictive capability of our developed models（see Figure 1）.The results obtained indicate that SVM based on SMILES string kernel can be regarded as a very promising and alternative modeling approach for potential toxicity prediction of *** datasets can be downloaded from the website: http://***/p/data-share/.

关键词： toxicity prediction structure-toxicity relationship(STR) support vector machine(SVM) simplified molecular input line entry specification(SMILES) string kernel

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QSAR Models for Predicting Additive and Synergistic Toxicities of Binary Pesticide Mixtures on Scenedesmus Obliquus

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Chinese Journal of Structural Chemistry 2022年第3期41卷 166-177,I0011页

作者： MO Ling-Yun YUAN Bai-Kang ZHU Jie QIN Li-Tang DAI Jun-Feng College of Environmental Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology Guilin University of TechnologyGuilinGuangxi 541004China Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area MNRNanning 530023China Guangxi Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of TechnologyGuilin 541004China

Pesticides released into the environment may pose potential risks to the ecological system and hu-man ***,existing toxicity data on pesticide mixtures still lack,especially regarding the toxic interac-tions of their *** study aimed to determine the toxic interactions of binary mixtures of pesticides on Scenedesmus Obliquus(***)and to build quantitative structure-activity relationship models(QASR)for predicting the mixture *** applying direct equipartition ray method to design binary mixtures of five pes-ticides(linuron,dimethoate,dichlorvos,trichlorfon and metribuzin),the toxicity of a single pesticide and its mix-ture was tested by microplate toxicity analysis on *** QASR models were built for combined toxicity of binary mixtures of pesticides at the half-maximal effective concentration(EC_(50)),30%maximal effective concen-tration(EC_(30))and 10%maximal effective concentration(EC_(10)).The results showed that the single toxicity follows:metribuzin>linuron>dichlorvos>trichlorfon>*** mixtures of linuron and trichlorfon,dichlorvos and metribuzin,dimethoate and metribuzin induced synergetic effects,while the remaining binary mixtures exhib-ited *** developed QSAR models were internally validated using the leave-one-out cross-validation(LOO),leave-many-out cross-validation(LMO),bootstrapping,and y-randomization test,and externally validated by the test *** three QSAR models satisfied well with the experimental values for all mixture toxicities,and presented high internally(R^(2)and Q^(2)>0.85)and externally(Q^(2)_(F1),Q^(2)_(F2),and Q^(2)_(F3)>0.80)predictive *** developed QSAR models could accurately predict the toxicity values of EC_(50),EC_(30)and EC_(10)and were superior to the concentration addition model's results(CA).Compared to the additive effect,the QSAR model could more ac-curately predict the binary mixture toxicities of pesticides with synergistic effects.

关键词： pesticide QSAR toxicity prediction binary mixture algae

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AquaticTox:A Web-Based Tool for Aquatic toxicity Evaluation Based on Ensemble Learning to Facilitate the Screening of Green Chemicals

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Environment & Health 2024年第4期2卷 202-211页

作者： Xing-Xing Shi Zhi-Zheng Wang Yu-Liang Wang Fan Wang Guang-Fu Yang National Key Laboratory of Green Pesticide Central China Normal UniversityWuhan 430079China

The widespread use of chemical products inevitably brings many side effects as environmental *** assessment of compounds to aquatic life plays an important role in protecting the environment from their ***,in vivo animal testing approaches for aquatic toxicity evaluation are timeconsuming,expensive,and ethically limited,especially when there are a great number of *** silico modeling methods can effectively improve the toxicity evaluation efficiency and save ***,we present a web-based server,AquaticTox,which incorporates a series of ensemble models to predict acute toxicity of organic compounds in aquatic organisms,covering Oncorhynchus mykiss,Pimephales promelas,Daphnia magna,Pseudokirchneriella subcapitata,and Tetrahymena *** predictive models are built through ensemble learning algorithms based on six base *** ensemble models outperform all corresponding single models,achieving area under the curve(AUC)scores of 0.75−*** to the best single models,the average precisions of the ensemble models have been increased by 12−22%.Additionally,a self-built knowledge base of the structure-aquatic toxic mode of action(MOA)relationship was integrated into AquaticTox for toxicity mechanism ***,the user-friendly tool(https://***/ccb/server/AquaticTox);could facilitate the identification of aquatic toxic chemicals and the design of green molecules.

关键词： ecotoxicity aquatic toxicity water environment protection toxicity prediction structure−toxicity relationship deep learning

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Commentary: Unexpected Novel Chemical Weapon Agents Designed by Innocuous Drug-Development AI (Artificial Intelligence) Algorithm

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Pharmacology & Pharmacy 2022年第7期13卷 225-229页

作者： Robert B. Raffa Joseph V. Pergolizzi Jr. Thomas Miller Daniel Motto School of Pharmacy (Emeritus Prof.) Temple University Philadelphia PA USA College of Pharmacy (Adjunct) University of Arizona Tucson AZ USA Neumentum Summit NJ USA Enalare Therapeutics Princeton NJ USA NEMA Research Naples FL USA Sydney Kimmel Medical College Thomas Jefferson University Philadelphia PA USA

Recent publications reveal the disturbing information that a minor edit to an algorithm being used for designing legitimate drug candidates redirected the program in a way that resulted in the surprising design of novel chemical warfare agent candidates. Although this outcome was not the result of nefarious intent, and appropriate chemical defense authorities were notified, the potential implications of some misapplication of a drug-design algorithm for nefarious purposes are clear. This Commentary summarizes how otherwise benign Artificial Intelligence (AI) algorithms used for drug discovery can be easily reversed to design novel chemical warfare agents for which no effective antidote will be available, or perhaps even envisioned.

关键词： Artificial Intelligence Drug Discovery Chemical Weapons Machine Learning Generative Model toxicity prediction

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