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Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

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Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure‐activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology
Adsorption of the herbacide atrazine on functionalized graphenic surfaces. (a) Free energy as a function of distance between an atrazine molecule and hydroxylated and polymer‐conjugated graphene surfaces. (b) Mass density of polymer and water as a function of distance from the surface in the absence of atrazine. (c–e) Representative simulation snapshots of atrazine adsorbed on graphene surfaces conjugated with PE, PEG, and PVP, respectively. Obtained with permission from Comer, Chen, Poblete, Vergara‐Jaque, & Riviere ()
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Analysis of Dendrimer/peptide complex at 1:1 molar ratio. (a–c) represent the radial distribution function of each peptide as a function of the center of mass of the dendrimer. (e–g) represent the dendrimer/peptide complexes. Mean number of contacts established between the dendrimer and each aminoacid of H) p24 peptide, (i) gp160 peptide, (j) NEF peptide, during the last 10 ns of MD trajectory. (k) Solvent coverage for each peptide in presence of the dendrimer, obtained as a percentage from surface solvent accessible surface area values (SASA) along the trajectory and normalized by SASA of each peptide. (l) Binding energy of each dendrimer/peptide complex obtained from MM‐GBSA method. Obtained with permission from Vacas‐Córdoba et al. ()
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Kinetic DHA‐peptides micelles formation from (column 1) homogeneous state (pH >6.0) and the structural transformation of the micelles from (column 2) pH >6.0 to (column 3) pH < 6.0. The number of histidine residue is (a) 0, (b) 5, and (c) 10. DOX is drawn in yellow. Obtained with permission from Wang et al. (()
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Computational strategies and techniques in drug discovery, ligand based
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Summary of structure‐based methods discussed in this review
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"(a) Whole‐exome sequencing (WES) is traditionally used to detect sequence variants following the pattern of inheritance of a disease phenotype in related individuals (disease carriers represented in yellow). Currently, WES is best suited to elucidate rare Mendelian disorders. (b) Genome‐wide association studies (GWAS) are used to identify the disease loci of more common complex disorders from a cohort of unrelated individuals. Using genetic markers; single nucleotide polymorphisms, as proxies for a given region of the genome, GWAS measure the differences in allelic frequency at each locus for markers in cases and controls. This can be represented graphically with a Manhattan plot"
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Nanotechnology Approaches to Biology > Nanoscale Systems in Biology
Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease
Therapeutic Approaches and Drug Discovery > Emerging Technologies

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