Computational Materials & Modelling
Aihua Zhang; Shi Qiu
Abstract
Herbal medicine (HM) is a fruitful source of chemical substances that has contributed greatly to the pharmaceutical industry and novel therapeutics. Natural products derived from HM continue to be a rich source of lead compounds because of their high structural diversity and potent bioactivities. However, ...
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Herbal medicine (HM) is a fruitful source of chemical substances that has contributed greatly to the pharmaceutical industry and novel therapeutics. Natural products derived from HM continue to be a rich source of lead compounds because of their high structural diversity and potent bioactivities. However, despite the success of active ingredients derived from HM in drug discovery, compatibility issues that make huge challenging for extended timelines of effective evaluation, chemical composition identification, active ingredient screening and target confirmation. However, some approaches solely cannot effectively elucidate the overall effect and action mechanism due to complexity muti-components of HM. Thus, integration strategies combining modern analytical techniques with HM are increasingly being developed in the era of big data and omics. The updated mass spectrometry has been used to identify natural product structure and their mode of action on biological processes. Their molecular properties are validated through the use of recent high-throughput multi-omics including transcriptomic, proteomic and metabolomic tools and bio-informatics, molecular docking, network pharmacology techniques that enable to accelerate natural product discovery. We summarized several important omics technical platforms and multi-omics-based integration approach as powerful strategies to demystify HM and discover new bioactive molecules.
Computational Materials & Modelling
Maosheng Zheng; Yi Wang; Haipeng Teng
Abstract
Multi object optimization in material selection involves the satisfaction of optimizing the multi attributes simultaneously, which analogically corresponds to the simultaneous appearance of the event of the multi attributes in the viewpoint of probability theory, thus the optimization of multi – ...
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Multi object optimization in material selection involves the satisfaction of optimizing the multi attributes simultaneously, which analogically corresponds to the simultaneous appearance of the event of the multi attributes in the viewpoint of probability theory, thus the optimization of multi – object becomes the assessment of the “joint probability” of these multi – attribute problem. Furthermore, the preferential degree of the candidate material in the material selection is reflected by the concept of preferential probability, and a quantitative approach for evaluating the partial preferential probability of each material attribute indicator and the total (joint) preferential probability of candidate material in the material selection is proposed on basis of probability theory correspondingly. In the approach, all material attribute indicators are divided into beneficial or unbeneficial types; each material attribute indicator of the candidate contributes one partial preferential probability linearly to its authorized material upon its nature of whether beneficial or unbeneficial type merely; the product of all partial preferential probabilities of a candidate makes its total preferential probability, which is the final unique index in the material selection decisively; the candidate materials can be ranked according to their total preferential probabilities, which determines the result of the selection. Furthermore, the condition of discrete input variables and the objects is extended to the case of continuous input variables and the objects. Some examples are given in detail, satisfied results are obtained.
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