PDF In silico medicinal chemistry : computational methods to support drug design

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Wikberg, Chanin Nantasenamat Alarcon-Riquelme, Pedro Carmona-Saez Rosa, Makedonka Mitreva Section 3. Examples and Case Studies Niteshkumar U. Sahu, Chetan P. Shah, Janvhi S. Machhar, Prashant S. Kharkar Shanthi, K. Ramanathan Sabaraglini, Lucas N.

De Novo Design of Ligands Using Computational Methods

Alberca, Juan I. Alice, Alan Talevi Section 4. Tools and databases He also serves as a member of Editorial Boards of several International Journals. We are always looking for ways to improve customer experience on Elsevier. We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit. If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.

Cited By. This article is cited by 80 publications. Journal of Chemical Information and Modeling , 59 9 , DOI: Journal of Chemical Information and Modeling , 57 4 , Journal of Chemical Information and Modeling , 56 2 , Journal of Chemical Information and Modeling , 54 1 , Shana L.

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Plummer, Jeremy Starr, Charlene R. Miller, and William R. Journal of Medicinal Chemistry , 55 2 , Journal of Chemical Information and Modeling , 51 5 , David Hecht and Gary B. Journal of Chemical Information and Modeling , 49 4 , Nicolaou, Joannis Apostolakis and Costas S.

Journal of Chemical Information and Modeling , 49 2 , Christos A. Mascini, M. Sergi, D. Monti, M. Del Carlo and D.

7 Steps to Drug Discovery

Analytical Chemistry , 80 23 , Fabian Dey and, Amedeo Caflisch. Journal of Chemical Information and Modeling , 48 3 , Journal of Medicinal Chemistry , 49 3 , Biomolecules , 8 2 , Ribonucleotide reductase as a drug target against drug resistance Mycobacterium leprae : A molecular docking study. Infection, Genetics and Evolution , 60 , Sanjoy S. Ningthoujam, Anupam D.

Talukdar, Satyajit D. Sarker, Lutfun Nahar, Manabendra D. Athanasios I. Linkers: The key elements for the creation of efficient nanotherapeutics. Journal of Controlled Release , , Scheffczyk, P.

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Fleitmann, J. Thien, C. Redepenning, K. Leonhard, W. Marquardt, A. Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases. William J. Allen, Brian C. Fochtman, Trent E. Balius, Robert C. Journal of Computational Chemistry , 38 30 , European Journal of Medicinal Chemistry , , Developing an in silico pipeline for faster drug candidate discovery: Virtual high throughput screening with the Signature molecular descriptor using support vector machine models.


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Chemical Engineering Science , , When applied to the very beginning of the ligand-based discovery process, it can be the basis of a rationally designed virtual library. One of the characteristic limitations of HTS is the library it is based on. These libraries tend to be variations on a scaffold.

That is to say, HTS can explore chemical space deeply along the axis of a given scaffold, but it is this same underpinning of library construction which makes the search space narrow. In addition, libraries of this sort are often not pre-screened for toxicity. The subsequent HTS search may reveal compounds with nanomolar activities at the level of individual cells that are toxic at the level of the whole organism. Thus, a high definition pharmacophore can be used to design focused virtual libraries and remove toxic hits from the search space before HTS resources are wasted on them.

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In Silico Medicinal Chemistry (RSC Publishing) Nathan Brown

After initial identification of hits, hit confirmation can also benefit from computational methods. Cross-screening assays can be more accurately and efficiently selected if the toxicophoric points of a hit have already been well characterised. Hit expansion can also be done in a more rationally directed way. This can save significant resources when applied to large scale HTS campaigns.


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Fragment-based drug design is an approach that is inherently structurally based. Crystallography of fragments bound to targets and SAR-by-NMR are experimental techniques that yield highly relevant structural data, such as the binding pose of a given fragment. The next step of FBDD is to link these fragments into a larger therapeutic molecule. This is another stage where computational methods can be used to guide design and evaluate molecular properties. While FBDD by definition builds the atomic skeleton by fragments, it does so by adding linking moieties that do not participate directly in binding to join the binding moieties together.

The resulting molecule is not simply the sum of its parts, but rather a new chemical entity, which may have unexpected properties. These constructs must be evaluated for other parameters relevant to a drug, such as stability, reactivity, solubility and toxicity.

Optimisation of these properties can be performed at this key stage of development in silico. Medicinal chemistry resources need not be wasted on synthesising F2L fragment-to-lead candidates that can already be pruned out for toxicity or other pharmacophoric features.

Robust computational methods are uniquely adapted to dealing with the problems of chemical space. Experimental methods are essential for the generation of new structural information. However, on the scale of industrial drug discovery programmes, complementary computational techniques are necessary to focus experimental resources efficiently.