Computational Methods in Drug Discovery

Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens; Lowe, Edward W.

doi:10.1124/pr.112.007336

Cited by 1,547 publications

(1,139 citation statements)

References 544 publications

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“…Once the structure of a target or a co‐crystal structure of a target together with a bound ligand is available, structure‐based design can be applied both rationally and creatively in order to tailor the properties of a drug molecule. Fragment‐based approaches are used for the identification of chemical starting points for lead optimization, virtual docking allows in silico screening, and de novo approaches generate new chemotypes for drug molecules 4, 5, 6, 7, 8…”

Section: Introductionmentioning

confidence: 99%

Targeting Antibiotic Resistance

2016

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Finding strategies against the development of antibiotic resistance is a major global challenge for the life sciences community and for public health. The past decades have seen a dramatic worldwide increase in human‐pathogenic bacteria that are resistant to one or multiple antibiotics. More and more infections caused by resistant microorganisms fail to respond to conventional treatment, and in some cases, even last‐resort antibiotics have lost their power. In addition, industry pipelines for the development of novel antibiotics have run dry over the past decades. A recent world health day by the World Health Organization titled “Combat drug resistance: no action today means no cure tomorrow” triggered an increase in research activity, and several promising strategies have been developed to restore treatment options against infections by resistant bacterial pathogens.

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Section: Introductionmentioning

confidence: 99%

Targeting Antibiotic Resistance

2016

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“…As a result, computational methods are employed to make predictions of numerous properties including solubility. 9,17,18 There are now numerous methods to predict solubility, with most varying in how the solvation/hydration step is modeled. Many of these solvation/hydration methodologies have been summarized in a recent review by Skyner et al 19 Commonly, QSPR methods are employed due to their speed, convenience and accuracy, when provided with a suitable training dataset.…”

Section: Introductionmentioning

confidence: 99%

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

McDonagh

Palmer

Mourik

et al. 2016

J. Chem. Inf. Model.

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We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and QSPR models generated by both machine learning (Random Forest and Support Vector Machines) and regression (Partial Least Squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes. Previous work has suggested that the major source of error in solubility prediction schemes involving a thermodynamic cycle via the solid state is in the modeling of the free energy change away from the solid state. Yet contrary to this conclusion other work has found that the inclusion of terms such as the enthalpy of sublimation in QSPR methods 2 does not improve the predictions of solubility. We suggest the use of theoretical chemistry terms, detailed explicitly in the methods section, as descriptors for the prediction of the enthalpy and free energy of sublimation. A dataset of 158 molecules with experimental sublimation thermodynamics values and some CSD refcodes has been collected from the literature and is provided with their original source references.

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“…Virtual screening is a computational method used to identify novel chemical structures that selectively bind to the target protein using standard protocols [75,76]. A grid was generated at the active site region using the Glide module of the Schrödinger suite.…”

Section: Virtual Screening Studiesmentioning

confidence: 99%

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery–a structure-based approach

et al. 2016

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Cancer progression is a global burden. The incidence and mortality now reach 30 million deaths per year. Several pathways of cancer are under investigation for the discovery of effective therapeutics. The present study highlights the structural details of the ubiquitin protein 'Ubiquitin-conjugating enzyme E2D4' (UBE2D4) for the novel lead structure identification in cancer drug discovery process. The evaluation of 3D structure of UBE2D4 was carried out using homology modelling techniques. The optimized structure was validated by standard computational protocols. The active site region of the UBE2D4 was identified using computational tools like CASTp, Q-site Finder and SiteMap. The hydrophobic pocket which is responsible for binding with its natural receptor ubiquitin ligase CHIP (C-terminal of Hsp 70 interacting protein) was identified through proteinprotein docking study. Corroborating the results obtained from active site prediction tools and protein-protein docking study, the domain of UBE2D4 which is responsible for cancer cell progression is sorted out for further docking study. Virtual screening with large structural database like CB_Div Set and Asinex BioDesign small molecular structural database was carried out. The obtained new ligand molecules that have shown affinity towards UBE2D4 were considered for ADME prediction studies. The identified new ligand molecules with acceptable parameters of docking, ADME are considered as potent UBE2D4 enzyme inhibitors for cancer therapy.

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Computational Methods in Drug Discovery

Cited by 1,547 publications

References 544 publications

Targeting Antibiotic Resistance

Targeting Antibiotic Resistance

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery–a structure-based approach

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