Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

Chinese herbal medicine has recently gained worldwide attention. The curative mechanism of Chinese herbal medicine is compared with that of western medicine at the molecular level. The treatment mechanism of most Chinese herbal medicines is still not clear. How do we integrate Chinese herbal medicine compounds with modern medicine? Chinese herbal medicine drug-like prediction method is particularly important. A growing number of Chinese herbal source compounds are now widely used as drug-like compound candidates. An important way for pharmaceutical companies to develop drugs is to discover potentially active compounds from related herbs in Chinese herbs. The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method. In this paper, we focus on the prediction methods for the medicinal properties of Chinese herbal medicines. We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method. Finally, we present the prospect of the joint application of various methods.

[1]  Chung F. Wong,et al.  Flexible receptor docking for drug discovery , 2015, Expert opinion on drug discovery.

[2]  David L. Brautigan,et al.  Discovery and characterization of small molecules that target the GTPase Ral , 2014, Nature.

[3]  Hoi-Jun Yoo,et al.  An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[4]  Keun Woo Lee,et al.  Pharmacophore modeling, virtual screening, molecular docking studies and density functional theory approaches to identify novel ketohexokinase (KHK) inhibitors , 2015, Biosyst..

[5]  Chen Wang,et al.  PDID: database of molecular-level putative protein-drug interactions in the structural human proteome , 2016, Bioinform..

[6]  Barry A. Bunin,et al.  Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery , 2015, PLoS neglected tropical diseases.

[7]  Zhihai Liu,et al.  Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark , 2018, Nature Protocols.

[8]  Saroj Verma,et al.  Topological and physicochemical characteristics of 1,2,3,4-Tetrahydroacridin- 9(10H)-ones and their antimalarial profiles: a composite insight to the structure-activity relation. , 2013, Current computer-aided drug design.

[9]  Guohua Huang,et al.  The Advances and Challenges of Deep Learning Application in Biological Big Data Processing , 2017, Current Bioinformatics.

[10]  Mohammad Shahid,et al.  A Robust Framework for Rapid Deployment of a Virtual Screening Laboratory , 2009, HealthGrid.

[11]  Asher Mullard,et al.  The drug-maker's guide to the galaxy , 2017, Nature.

[12]  Yu Kang,et al.  Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products , 2018, J. Chem. Inf. Model..

[13]  Mubashir Hassan,et al.  Extending the scope of amantadine drug by incorporation of phenolic azo Schiff bases as potent selective inhibitors of carbonic anhydrase II, drug‐likeness and binding analysis , 2018, Chemical biology & drug design.

[14]  Quan Zou,et al.  O‐GlcNAcPRED‐II: an integrated classification algorithm for identifying O‐GlcNAcylation sites based on fuzzy undersampling and a K‐means PCA oversampling technique , 2018, Bioinform..

[15]  Kari Salokas,et al.  Ultrafast protein structure-based virtual screening with Panther , 2015, Journal of Computer-Aided Molecular Design.

[16]  Zhihai Liu,et al.  Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions. , 2017, Accounts of chemical research.

[17]  Abdulmujeeb T. Onawole,et al.  Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring , 2017, Comput. Biol. Chem..

[18]  Georgia Melagraki,et al.  Development of a Predictive Pharmacophore Model and a 3D-QSAR Study for an in silico Screening of New Potent Bcr-Abl Kinase Inhibitors. , 2017, Mini reviews in medicinal chemistry.

[19]  Richard J. Epstein,et al.  Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach , 2016, BMC Cancer.

[20]  A. Hameed,et al.  Semicarbazone derivatives as urease inhibitors: Synthesis, biological evaluation, molecular docking studies and in-silico ADME evaluation. , 2018, Bioorganic chemistry.

[21]  Y. Singh.  Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance , 2017, Healthcare informatics research.

[22]  Y. Bao,et al.  Virtual screening and experimental validation of novel histone deacetylase inhibitors , 2016, BMC Pharmacology and Toxicology.

[23]  Wonpil Im,et al.  G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design , 2016, Protein science : a publication of the Protein Society.

[24]  Mariko Okada,et al.  Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment , 2017, Bioinform..

[25]  Shi-Hua Zhang,et al.  DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank , 2016, Bioinform..

[26]  Mubashir Hassan,et al.  Synthesis and Molecular Docking Studies of (E)-4-(Substituted-benzylideneamino)-2H-Chromen-2-one Derivatives: Entry to New Carbonic Anhydrase Class Of Inhibitors , 2018, Drug Research.

[27]  Jie Li,et al.  Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints , 2017, BMC Bioinformatics.

[28]  Jinan Wang,et al.  Systems pharmacology exploration of botanic drug pairs reveals the mechanism for treating different diseases , 2016, Scientific Reports.

[29]  Rebekah L. Bower,et al.  Amylin structure–function relationships and receptor pharmacology: implications for amylin mimetic drug development , 2016, British journal of pharmacology.

[30]  J. Majoral,et al.  Present drug-likeness filters in medicinal chemistry during the hit and lead optimization process: how far can they be simplified? , 2018, Drug discovery today.

[31]  Noam Shental,et al.  Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine , 2017, Science.

[32]  Ting Chen,et al.  Global optimization-based inference of chemogenomic features from drug-target interactions , 2015, Bioinform..

[33]  Ali Masoudi-Nejad,et al.  Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR , 2017, Iranian journal of pharmaceutical research : IJPR.

[34]  S. Yarmoluk,et al.  ASK1 pharmacophore model derived from diverse classes of inhibitors. , 2014, Bioorganic & medicinal chemistry letters.

[35]  Shuai Lu,et al.  Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds , 2015, Molecular Diversity.

[36]  HaiXia Long,et al.  Deep Convolutional Neural Networks for Predicting Hydroxyproline in Proteins , 2017 .

[37]  C. Lipinski,et al.  The rule of five should not impede anti-parasitic drug development , 2017, International journal for parasitology. Drugs and drug resistance.

[38]  Yao Chen,et al.  Discovery of new acetylcholinesterase inhibitors with small core structures through shape-based virtual screening. , 2015, Bioorganic & medicinal chemistry letters.

[39]  Q. Zou,et al.  RicyerDB: A Database For Collecting Rice Yield-related Genes with Biological Analysis , 2018, International journal of biological sciences.

[40]  C. Selvam,et al.  Performance evaluation of structure based and ligand based virtual screening methods on ten selected anti-cancer targets. , 2015, Bioorganic & medicinal chemistry letters.

[41]  Zhi Wei,et al.  PAK Signaling Drives Acquired Drug Resistance to MAPK Inhibitors in BRAF-mutant Melanomas , 2017, Nature.

[42]  Wei Zheng,et al.  Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths , 2017, Bioinform..

[43]  Identification of influenza PA-Nter endonuclease inhibitors using pharmacophore- and docking-based virtual screening. , 2018, Bioorganic & medicinal chemistry.

[44]  V. Dubey,et al.  Molecular docking and structure-based virtual screening studies of potential drug target, CAAX prenyl proteases, of Leishmania donovani , 2016, Journal of biomolecular structure & dynamics.

[45]  Abhigyan Nath,et al.  Maximizing lipocalin prediction through balanced and diversified training set and decision fusion , 2015, Comput. Biol. Chem..

[46]  Ruixin Zhu,et al.  In silico design of novel proton-pump inhibitors with reduced adverse effects , 2019, Frontiers of Medicine.

[47]  Diogo Santos-Martins,et al.  Receptor-based virtual screening protocol for drug discovery. , 2015, Archives of biochemistry and biophysics.

[48]  Tingjun Hou,et al.  Drug and Drug Candidate Building Block Analysis , 2010, J. Chem. Inf. Model..

[49]  Scott R. Granter,et al.  AlphaGo, Deep Learning, and the Future of the Human Microscopist. , 2017, Archives of pathology & laboratory medicine.

[50]  A. Marcus.  Pay up or retract? Drug survey spurs conflict. , 2017, Science.

[51]  Wei Zhang,et al.  When drug discovery meets web search: Learning to Rank for ligand-based virtual screening , 2015, Journal of Cheminformatics.

[52]  Tae-Wook Kang,et al.  Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis. , 2018, Journal of ethnopharmacology.

[53]  G. Bemis,et al.  Properties of known drugs. 2. Side chains. , 1999, Journal of medicinal chemistry.

[54]  Lise Getoor,et al.  A probabilistic approach for collective similarity-based drug-drug interaction prediction , 2016, Bioinform..

[55]  Antti Poso,et al.  Virtual screening approach of sirtuin inhibitors results in two new scaffolds. , 2015, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[56]  Sydney M. Shaffer,et al.  Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance , 2017, Nature.

[57]  Abraham Yosipof,et al.  Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category , 2018, Front. Chem..

[58]  Christopher A Lipinski,et al.  Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. , 2016, Advanced drug delivery reviews.

[59]  W. L. Jorgensen.  The Many Roles of Computation in Drug Discovery , 2004, Science.

[60]  Krister Wennerberg,et al.  Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach , 2017, Bioinform..

[61]  U. C. Abdul Jaleel,et al.  GPURFSCREEN: a GPU based virtual screening tool using random forest classifier , 2016, Journal of Cheminformatics.

[62]  Q. Zou,et al.  Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods , 2015, BioMed research international.

[63]  Krister Wennerberg,et al.  Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression , 2017, Bioinform..

[64]  Vladimir B. Bajic,et al.  DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches , 2017, Bioinform..

[65]  Liuting Zeng,et al.  Exploring the Pharmacological Mechanism of Danzhi Xiaoyao Powder on ER-Positive Breast Cancer by a Network Pharmacology Approach , 2018, Evidence-based complementary and alternative medicine : eCAM.

[66]  N. Huang,et al.  [Computational chemistry in structure-based drug design]. , 2013, Yao xue xue bao = Acta pharmaceutica Sinica.

[67]  Tao Huang,et al.  Uncovering the Mechanisms of Chinese Herbal Medicine (MaZiRenWan) for Functional Constipation by Focused Network Pharmacology Approach , 2018, Front. Pharmacol..

[68]  A. Bekhit,et al.  Anti-leishmanial click modifiable thiosemicarbazones: Design, synthesis, biological evaluation and in silico studies. , 2018, European journal of medicinal chemistry.

[69]  Jijun Tang,et al.  Prediction of human protein subcellular localization using deep learning , 2017, J. Parallel Distributed Comput..

[70]  J. Mochel,et al.  Model‐Based Reverse Translation Between Veterinary and Human Medicine: The One Health Initiative , 2017, CPT: pharmacometrics & systems pharmacology.

[71]  Yucong Duan,et al.  70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features , 2018, BMC Syst. Biol..

[72]  Sheena McGowan,et al.  From crystal to compound: structure-based antimalarial drug discovery. , 2014, The Biochemical journal.

[73]  Tahmeena Khan,et al.  Exploring the novel heterocyclic derivatives as lead molecules for design and development of potent anticancer agents. , 2018, Journal of molecular graphics & modelling.

[74]  Xu Zhou,et al.  ncDR: a comprehensive resource of non-coding RNAs involved in drug resistance , 2017, Bioinform..

[75]  P. Fong,et al.  Experimental and In Silico Analysis of Cordycepin and its Derivatives as Endometrial Cancer Treatment. , 2019, Oncology research.

[76]  Wei Zhang,et al.  Major achievements of evidence-based traditional Chinese medicine in treating major diseases. , 2017, Biochemical pharmacology.

[77]  Cangzhi Jia,et al.  4mCPred: machine learning methods for DNA N4‐methylcytosine sites prediction , 2018, Bioinform..

[78]  M. Athar,et al.  Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling , 2018, Molecular Diversity.

[79]  Jie Li,et al.  PDB-wide collection of binding data: current status of the PDBbind database , 2015, Bioinform..

[80]  Stefano Alcaro,et al.  A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins , 2015, J. Chem. Inf. Model..

[81]  Huan Hu,et al.  Study on the Mechanisms of Active Compounds in Traditional Chinese Medicine for the Treatment of Influenza Virus by Virtual Screening , 2018, Interdisciplinary Sciences: Computational Life Sciences.