Computer Chemistry

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The software sales market for traditional software packages in chemistry is at least M p.


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This market is dominated by a number of high-end suites of programs. Accelrys and Schroedinger Inc. In , Accelrys had a revenue ca. The purchase price for industrial agents for these softwares range from USD 2k — 50k. In the quantum area, Schroedinger is the most innovative and is recently moving into the cloud area with early elements of automation as well. However, there remains a large usage gap, where quantum chemistry continues to be regarding as a primarily academic tool.

Indeed, the vast majority of the market is dominated by molecular mechanics force field based approaches that focus on molecular structure. With quantum chemistry, the what and the how can be readily addressed earlier, quantitatively, thus ensuring greater intellectual property protection and more efficient product development.

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The barrier to implementation of quantum is the poor ease of use and difficult interpretation of quantum methods as well as the steep infrastructure and software hurdles required to execute computations on largescale distributed computing clusters. As the technological gap between structure and reactivity has not been adequately addressed to date, discovery researchers have found other practical solutions. Building on a typical fragment based approach, particular attention is directed towards fragments that can form known synthons for reactive assembly into larger molecular structures.

Ideally, the fragments themselves are commercially available, or available through robust and well characterized short synthetic pathways.

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This will lead to earlier toxicity testing and eventually a higher rate of clinical trial entry. See the offering of Bioblocks Inc. While this RFBLD approach reduces the pain in the potential design of impossible to synthesize molecules, legacy modelling softwares are unable to accurately assess reactivity and thus subtleties in the reactivities, essential for prioritizing the hit lists, are not available and the technological gap remains.

Thus, the generated hit libraries, which have been made with their reactivity in mind, cannot be further assessed for reactivity from a computational perspective leaving only cheminformatics approaches to provide qualitative suggestions, rather than the highly prized quantitative answers. Cheminformatics has been forcibly applied in the chemical reaction-forecasting as pioneered by E.


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Corey domain for decades. Reaction forecasting has primarily employed a rules-based approach to induce how molecules may react based on their molecular connectivity, relying exclusively on prior experience rules of known reactions or transforms, often supplemented with experimental knowledge of known reactions.

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This data is commonly accessed through large databases of chemical literature such as Scifinder and Reaxys. As these solutions use reported chemical data and do not require computation and are therefore able to deliver results very cheaply. However, the inaccuracy is a severe limitation.

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If you are a chemist and you have tried a reaction you already know that structural similarity based on literature searching is merely prescriptive. Such approaches using experimental information as the base data type require increasingly complex rules to discern the differing reactivity between structural similar molecules. Small changes in molecular structure have dramatic and, often unforeseen, consequences on reactivity and as such, reaction forecasting is limited to non-quantitative suggestions.

More recent advances in rules-based machine-learning treat more fundamental molecular features including atomic charge, resonance etc. By necessity, I had to install very broad spam filters and your regular email is oftern rejected or even worse: it goes to a junk bin without telling you or me about it. If I was rejecting the spam, I would be spamming innocent victims whose addresses were used on the From: line by the scum or the Internet.

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