In silico

In particular, in silico methods are expected to speed up the drug discovery process, to provide a quicker and cheaper alternative to in vitro tests, and to reduce the number of compounds with unfavorable pharmacological properties at an early stage of drug development. Bad ADMET profiles are a reason for attrition of new drug candidates during the development process [9, 10]. The major reasons for attrition of new drugs are ... 

The reliability of the in silico models will be improved and their scope for predictions will be broader as soon as more reliable experimental data are available. However, there is the paradox of predictivity versus diversity. The greater the chemical diversity in a data set, the more difficult is the establishment of a predictive structure-activity relationship. Otherwise, a model developed based on compounds representing only a small subspace of the chemical space has no predictivity for compounds beyond its boundaries. 

The term virtual screening or in silico screening" is defined as the selection of compounds by evaluating their desirability in a computational model. The desirability comprises high potency, selectivity, appropriate pharmacokinetic properties, and favorable toxicology. 

R and M M Hann 2000. The In Silico World of Virtual Libraries. Drug Discovery Today 5 326-336. R and I D Kuntz 1990. Conformational Analysis of Flexible Ligands in Macromolecular eptor Sites. Journal of Computational Chemistry 13 730-748. 

Computational biology Computational molecular biology Biocomputing in silico biology... 

If structural information of the protein target is available, e.g., a crystal structure, in silico screening of huge virtual compound libraries can be conducted by the use of docking simulations. Based on identified primary hits, structural variations of the ligand can be evaluated by computational modeling of the ligand-protein complex. 

Moreover, there is a clear and obvious need for experimental work to be conducted in support of the development of accurate in silico methods. Bioinformaticians, like all other scientists physical or biological or social, need... 

Flower DR. Towards in silico prediction of immunogenic epitopes. Trends Immunol 2003 24 667-74. 

Defranoux NA, Stokes CL, Young DL, Kahn AJ. In silico modeling and simulation of bone biology a proposal. / Bone Miner Res 2005 20 1079-84. 

Figure 11.2 A decision tree, based on an associated inflnence diagram, can help organize and integrate information about risks and the way in which research work buys better information that allows choice of the options most likely to succeed. This example describes the relationship between in silico predictions and in vitro assay results for the same compound structures.

The visualization of trade-offs involving risk and uncertainty is clearly one such powerful aid to insight. Questions frequently encountered are Where should in silico and other predictive technologies best be applied within the R D process What workflows involving such technologies add most value What should be the approach to selecting cutoffs ... 

A further insight is that the best workflow depends on a combination of factors that can in many cases be expressed in closed mathematical form, allowing very rapid graphical feedback to users of what then becomes a visualization rather than a stochastic simulation tool. This particular approach is effective for simple binary comparisons of methods (e.g., use of in vitro alone vs. in silico as prefilter to in vitro). It can also be extended to evaluation of conditional sequencing for groups of compounds, using an extension of the sentinel approach [24]. 

Synthetic constraints—such as difficulty, yield, management of starting materials, and intermediates—will naturally restrict the diversity of compounds that are made [7]. In silico designs with scaffolds that utilize similar synthetic steps will naturally be favored over those that are not. These pressures to make a small number of compounds with limited scaffold variability require computational methods to make exquisitely accurate predictions The... 

Even as the computational prediction error rate is reduced to acceptable levels, many cases will be encountered in which the predictions are indistinguishable to within error. In a scenario in which several different in silico designs are given equivalent but favorable activity predictions, the end user s medicinal experience may help decide which to promote to synthesis. The quality of that decision at this point will be strongly influenced by how easy it is to understand the different contributions to the computational predictions. Interpretability is thus critical for synergistically utilizing the experience of the end user. 

Grzybowski BA, Ishchenko AV, Shimada J, Shakhnovich El. From knowledge-based potentials to combinatorial lead design in silico. Acc Chem Res 2002 35 261-9. 

To date, many of the reported ADME/Tox models have been rule based. For example, some research groups have used relatively simple filters like the rule of 5 [93] and others [94] to limit the types of molecules evaluated with in silico methods and to focus libraries for HTS. However, being designed as rapid computational alert tools aimed at a single property of interest, they cannot offer a comprehensive picture when it comes to understanding ADME properties. 

Schuffenhauer A, Zimmermann J, Stoop R,van derVyver JJ, Lecchini S, Jacoby E. An ontology for pharmaceutical ligands and its application for in silico screening and library design. J Chem Inf Comput Sci 2002 42 947-55. 

Van de Waterbeemd H, Gifford E. ADMET in silico modelling towards prediction paradise Nat Rev Drug Discov 2003 2 192-204. 

Balakin KV, Ivanenkov YA, Skorenko AV, Nikolsky YV, Savchuk NP, Ivashchenko AA. In silico estimation of DMSO solubility of organic compounds for bioscreening. J Biomol Scr 2004 9 22-31. 

Ekins S. Predicting nndesirable drng interactions with promiscnons proteins in silico. Drug Discov Today 2004 9 276-85. 

Ekins S, Wrighton SA. Application of in silico approaches to predicting drng-drug interactions a commentary. J Pharm Tox Methods 2001 44 1-5. 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. In silico insights chemical and structural characteristics associated with uridine diphosphate-glucuronosyltransferase substrate selectivity. Clin Exp Pharmacol Physiol 2003 30 836-40. 

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