New methods in computer-aided drug design

Sheridan, Robert P.; Venkataraghavan, R.

doi:10.1021/ar00141a002

Cited by 49 publications

(17 citation statements)

References 25 publications

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“…Beside this, the designed lead compounds have to comply with drug‐like properties to ensure bioavailability, which in turn are defined by the Lipinski rules: (i) molecular weight (≤500 g/mol), (ii) lipophilicity (Log P ; ≤5), (iii) number of hydrogen bond donors (≤5), and (iv) hydrogen bond acceptors (≤10) . However, this approach consumes significant resources and has a success rate below 0.001% . During the 1990s, robotic HTS was performed on several libraries of molecules to find relevant anti‐TB candidates to be used in clinical trials (Table ).…”

Section: Experimental Strategies Commonly Used To Find New Anti‐tb Drugsmentioning

confidence: 99%

In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs

2017

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Tuberculosis (TB) is one of the most important public health problems around the world. The emergence of multi-drug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis strains has driven the finding of alternative anti-TB targets. In this context, P-type ATPases are interesting therapeutic targets due to their key role in ion homeostasis across the plasma membrane and the mycobacterial survival inside macrophages. In this review, in silico and experimental strategies used for the rational design of new anti-TB drugs are presented; in addition, the chemical space distribution based on the structure and molecular properties of compounds with anti-TB and anti-P-type ATPase activity is discussed. The chemical space distribution compared to public compound libraries demonstrates that natural product libraries are a source of novel chemical scaffolds with potential anti-P-type ATPase activity. Furthermore, compounds that experimentally display anti-P-type ATPase activity belong to a chemical space of molecular properties comparable to that occupied by those approved for oral use, suggesting that these kinds of molecules have a good pharmacokinetic profile (drug-like) for evaluation as potential anti-TB drugs.

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Section: Experimental Strategies Commonly Used To Find New Anti‐tb Drugsmentioning

confidence: 99%

In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs

2017

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“…(The CASE system [3] has a similar philosophy.) The trend vector, used with the descriptors described above, has proved to be useful in a number of applications [4][5][6][7]. One application is the evaluation of chemical structures as candidates for synthesis.…”

Section: Introductionmentioning

confidence: 99%

“…One application is the evaluation of chemical structures as candidates for synthesis. These structures can be sketched by the user [4] or stochastically assembled from fragments [5].…”

Section: Introductionmentioning

confidence: 99%

“…Given a trend vector from these data, one can predict the activity of tens or hundreds of thousands of compounds in a corporate database. Upon testing the compounds with the highest predicted activities, one typically finds several-fold more actives per compound tested than in the random screening; often some of the actives will be from novel chemical classes [1,4,6].…”

Section: Introductionmentioning

confidence: 99%

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Extending the trend vector: The trend matrix and sample-based partial least squares

Sheridan

Nachbar

Bush

1994

J Computer-Aided Mol Des

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Trend vector analysis [Carhart, R.E. et al., J. Chem. Inf. Comput. Sci., 25 (1985) 64], in combination with topological descriptors such as atom pairs, has proved useful in drug discovery for ranking large collections of chemical compounds in order of predicted biological activity. The compounds with the highest predicted activities, upon being tested, often show a several-fold increase in the fraction of active compounds relative to a randomly selected set. A trend vector is simply the one-dimensional array of correlations between the biological activity of interest and a set of properties or 'descriptors' of compounds in a training set. This paper examines two methods for generalizing the trend vector to improve the predicted rank order. The trend matrix method finds the correlations between the residuals and the simultaneous occurrence of descriptors, which are stored in a two-dimensional analog of the trend vector. The SAMPLS method derives a linear model by partial least squares (PLS), using the 'sample-based' formulation of PLS [Bush, B.L. and Nachbar, R.B., J. Comput.-Aided Mol. Design, 7 (1993) 587] for efficiency in treating the large number of descriptors. PLS accumulates a predictive model as a sum of linear components. Expressed as a vector of prediction coefficients on properties, the first PLS component is proportional to the trend vector. Subsequent components adjust the model toward full least squares. For both methods the residuals decrease, while the risk of overfitting the training set increases. We therefore also describe statistical checks to prevent overfitting. These methods are applied to two data sets, a small homologous series of disubstituted piperidines, tested on the dopamine receptor, and a large set of diverse chemical structures, some of which are active at the muscarinic receptor. Each data set is split into a training set and a test set, and the activities in the test set are predicted from a fit on the training set. Both the trend matrix and the SAMPLS approach improve the predictions over the simple trend vector. The SAMPLS approach is superior to the trend matrix in that it requires much less storage and CPU time. It also provides a useful set of axes for visualizing properties of the compounds. We describe a randomization method to determine the optimum number of PLS components that is very much faster for large training sets than leave-one-out cross-validation.

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“…Isosteres but containing relatively non-polar 0 II groups, for example, ureas -NHCNH in place of NH II guanidines -NHCNH, were also synthesized to validate the theory of high polarization restricting access to the CNS. Computer-aided molecular modelling techniques are now well established as methods of defining pharmacophores (Sheridan & Venkataraghavan 1987). Such programs were used to establish a 5-HT2 pharmacophore based on 10, structurally diverse, 5-HTz antagonist ligands.…”

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confidence: 99%

Chemical Design of Peripherally Acting Compounds

Jackson

Copp

Cullen

et al. 1992

Clin Exp Pharma Physio

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1. Some guanidines, related in structure to mianserin and to 2-methyl-1,2,9,13b-tetrahydro-3H-dibenz[c,f]imadazo[1,5a] azepin-3-imine hydrobromide (WAL 801), have been synthesized and shown to be peripherally acting 5-HT2 antagonists. Structurally related compounds but not bearing a charged ionic group have been shown to have central activity. 2. Computer-aided molecular modelling has been used to establish a 5-HT2 pharmacophore. 3. The principle of exclusion from the CNS by incorporating a highly polar group to a biologically active molecule has been extended to the design and synthesis of a peripherally acting analgesic.

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New methods in computer-aided drug design

Abstract: Recently, work has begun to reconcile the results from topological and modeling efforts.12

Cited by 49 publications

References 25 publications

In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs

In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs

Extending the trend vector: The trend matrix and sample-based partial least squares

Chemical Design of Peripherally Acting Compounds

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