A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods
| Autor: | Malancha Karmakar, Michael Silk, Wandré N. P. Veloso, Carlos H M Rodrigues, Joicymara S Xavier, Douglas E. V. Pires, Pâmela M Rezende, Yoochan Myung, João P V Linhares, David B. Ascher, Stephanie Portelli |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Specific protein Molecular interactions Mutation Computer science Drug discovery Ligand Graph based Molecular binding food and beverages medicine.disease_cause Chemical synthesis 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Drug development Pharmacokinetics medicine Biochemical engineering 030217 neurology & neurosurgery Protein ligand |
| Zdroj: | Methods in Molecular Biology ISBN: 9781071602690 |
| DOI: | 10.1007/978-1-0716-0270-6_7 |
| Popis: | High-throughput computational techniques have become invaluable tools to help increase the overall success, process efficiency, and associated costs of drug development. By designing ligands tailored to specific protein structures in a disease of interest, an understanding of molecular interactions and ways to optimize them can be achieved prior to chemical synthesis. This understanding can help direct crucial chemical and biological experiments by maximizing available resources on higher quality leads. Moreover, predicting molecular binding affinity within specific biological contexts, as well as ligand pharmacokinetics and toxicities, can aid in filtering out redundant leads early on within the process. We describe a set of computational tools which can aid in drug discovery at different stages, from hit identification (EasyVS) to lead optimization and candidate selection (CSM-lig, mCSM-lig, Arpeggio, pkCSM). Incorporating these tools along the drug development process can help ensure that candidate leads are chemically and biologically feasible to become successful and tractable drugs. |
| Databáze: | OpenAIRE |
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