China
Africa
Explore chapters and articles related to this topic
Insights into interactomics-driven drug repurposing to combat COVID-19
Published in Sanjeeva Srivastava, Multi-Pronged Omics Technologies to Understand COVID-19, 2022
Amrita Mukherjee, Ayushi Verma, Ananya Burli, Krishi Mantri, Surbhi Bihani
Three-dimensional structures are available in RCSB Protein Data Bank (PDB), which are solved mainly by X-ray or NMR crystallography. Similarly, information about small molecules or drugs is available in diverse databases such as PubChem, Drugbank, ZINC, and ChEMBL. PubChem contains structural and functional information for more than 60 million compound structures, whereas Drugbank offers multiple levels of drug target information (DTI) for more than 10,000 drugs. In PubChem, we can find both 3D and 2D structures of a small molecule. Sometimes instead of 3D structures (e.g., SDF format), the SMILES (simplified molecular-input line-entry system) are available, which have to be converted to either SDF or PDB format. All these structures can be retrieved from these databases and manually curated before starting further computational experiments. Recently, a customized SARS-CoV-2 drug interaction database has been created, named CORDITE (Curated CORona Drug InTERactions Database for SARS-CoV-2) (Martin et al. 2020). This platform is very much helpful in conducting a curated literature search for COVID-19.
Advancing Computational Methods in Chemical Engineering and Chemoinformatics
Published in Francisco Torrens, A. K. Haghi, Tanmoy Chakraborty, Chemical Nanoscience and Nanotechnology, 2019
Many compounds kept in databases have already been explored for multiple aims as portion of drug-discovery programs. Excavating this information can provide experimental evidence useful for structuring pharmacophores to determine the main pharmacological groups of the compound. Predictor model and DrugBank predictor model dataset was built using data collected from the ChEMBL database by means of a probabilistic method. The model can be used to forecast both the primary target and off-targets of a compound based on the circular fingerprint methodology, one of the technology developed by cheminformatic method. The study of off-target connections is now known to be as important as to recognize both drug action and toxicology. These molecular structures are drug targets in the treatment neurological diseases such as Alzheimer’s disease, obsessive disorders, and Parkinson’s disease and depression. In future, developing these multitargeted compounds with selection and chosen ranges of cross-reactivity can report disease in a more subtle and effective ways and will be a key pharmacological concept in future.
A Shifting Paradigm of a Chemistry Methods Approach: Cheminformatics
Published in Alexander V. Vakhrushev, Omari V. Mukbaniani, Heru Susanto, Chemical Technology and Informatics in Chemistry with Applications, 2019
Heru Susanto, Ching Kang Chen, Teuku Beuna Bardant, Arief Amier Rahman Setiawan
Many compounds kept in databases have already been explored for multiple aims as a part of drug discovery programs. Excavating this information can provide experimental evidence useful for structuring pharmacophores to determine the main pharmacological groups of the compound.15,16 Predictor model and DrugBank predictor model dataset were built using data collected from the ChEMBL database by means of a probabilistic method. The model can be used to forecast both the primary target and off-targets of a compound based on the circular fingerprint methodology, one of the technologies developed by cheminformatics method. The study of off-target connections is now known to be as important as to recognize both drug action and toxicology. These molecular structures are the drug targets in the treatment of neurological diseases such as Alzheimer’s disease, obsessive disorders, Parkinson’s disease, and depression. In future, developing these multi-targeted compounds with selection and chosen ranges of cross-reactivity can report disease in a more subtle and effective way and will be a key pharmacological concept.
Deep learning for predicting toxicity of chemicals: a mini review
Published in Journal of Environmental Science and Health, Part C, 2018
Weihao Tang, Jingwen Chen, Zhongyu Wang, Hongbin Xie, Huixiao Hong
ChEMBL is another open database containing a large quantity of compound bioactive data extracted from the published literature.74,75 This database consisted of more than 1.6 million chemical structures with 14 million activity points collected from over 1.2 million assays at the end of 2016.19