The fact is, computational modelling do provides an insight into the possible outcome but, wetlab is equally essential as well. Without wetlab, we can't say that clearly.

I agree with Rajesh modelling is only half the process without the wetlab data it can be difficult to rationalize the simulation data. It is also very beneficial to have a co-crystal structure to carry out the docking of substrate analogues. One problem which can often occur is that drug binding sites have a large degree of flexibility, loop movements etc... which are difficult to model by computational means, resulting in false negative scores due to steric clashing. There is a nice paper on this here for the ENR enzyme.

Cohen, E. M. L.; Machado, K. S.; Cohen, M.; de Souza, O. N. Effect of the explicit flexibility of the InhA enzyme from Mycobacterium tuberculosis in molecular docking simulations. BMC Genomics 2011, 12, S7.

The programs are very powerful and the ability to screen millions of drugs via the computer simulation rather than in a lab based assay can save a lot of time and money. It can be useful to screen via the computer and then test the positive results in an assay, if its available. The use of NMR and crystallography to validate the modelling is always beneficial. Not sure that this totally answers your question but may help.

in a nut shell, we can say that computational aided molecular drug designing is saving time, cost as well as throws light on the mechanism by which the drug is active under a given circumstances....

A lot of work remains to be done though. Simple docking calculations can often miss the essential physics in intermolecular interactions. Free energy calculations that probe the problem more thoroughly are generally much more reliable in predicting binding energies (free energy has contributions from entropy as well as enthalpy, after all) but can be computationally intense. When screening a large number of molecules for binding affinities, free energy calculations that take on the order of hours or days can be too complex and not worth the cost. Therefore, one of the key problems in computer aided drug design is to find the appropriate balance between speed and realism. As long as the computational piece errs on the side of expedience, good structures may be missed or bad structures pass through the screening step. As algorithms become more clever and incorporate more entropic components (not just moving a molecule around but also allowing for molecular flexibility and conformational change, etc.) without sacrificing much speed, the predictive power of computational drug discovery will likewise increase. This all assumes a force-field that accurately describes the enthalpic contributions to the free energy, a tricky claim to make.

CADD and Molecular modelling are the tools which helps in drug discovery. Supporting domain like biochemistry, physiology and pharmacokinetics are important element which makes these tools as cost saving modes in expensive projects.

It is very important, you can read published papers in this prospecitve.

I sincerely believe that computational chemistry is developing methods for drug discovery. Perhaps the question would be: how accurate are these responses? the answer is simple, simulations access information that experiments have no resolution. Applying statistical mechanics or quantum mechanics, because we are unable to treat many-body systems simplified and therefore it is necessary simulated experiments.

computational chemistry is highly useful at the lead optimisation stage for the design of analogues of the lead compound, with optimum physico-chemical properties for target binding and/or ADMET profiles

Dear All:

I have built a 9 question (not 10 as promised by Survey Monkey) survey to help us see where QM is in industry. Each question has an "other" section so please feel free to fill in. Once I have at least 50 respondents I will publish the metrics here on LinkedIn.

Here is the link:

https://www.surveymonkey.com/r/35QL9ZH

It will take a few minutes and should be rewarding to all.

I look forward to your responses. For data scientists I think a more formulaic and quantifiable approach makes sense.

Best Regards,

Peter