Confluence of Technologies Can Bring “Virtual Pharmacology” to the Next Level

by Andrii Buvailo 

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In 1970-80s, the idea of virtual screening was regarded as a conceptual way to substitute costly and time-consuming experimental “screen-everything-you-have” approaches with a much faster and cheaper predictive modelling to cherry-pick only the best molecules for subsequent synthesis and validation in a lab. A great number of computational tools and approaches emerged, aiming at “pre-screening” new promising molecules, so called “hits”, or augmenting experimental screening programs to optimize efforts.

One of the most popular and powerful methods of this kind is molecular docking, which has been widely used ever since the early 1980s. The idea of molecular docking is essentially to identify a perfect “key” among many diverse options, fitting to a “lock”, a biological target of choice -- using computational 3D representations of the interacting moieties.  

With all the early promise, molecular docking stumbled upon several fundamental issues, peculiar for the early decades of “digital revolution”: a lack of sufficient computing power, imperfect predictive algorithms, a limited choice of available molecules for virtual screening, and a small number of structurally characterized biological targets of sufficient pharmaceutical significance.

Last but not least, the virtual screening approach contained substantial “synthesizability risk” of not being able to physically get to all the hits, picked by a computer, in a cost-efficient and timely manner. As Asher Mullard, a freelance journalist at Nature, described it: “Many computational approaches also have an annoying habit of suggesting candidates that are nightmares to cook up in a lab.”

 

A lucky confluence of technologies opens doors in universe of synthesizable “hits”

Over the past several decades enormous progress has been achieved in many technologies, essential for a truly successful virtual screening effort. This finally allowed to open doors in large-scale "virtual pharmacology" of the 21st century -- with the emergence of the largest docking-friendly database of synthetically accessible compounds, outlined in a recent influential article in Nature: “Ultra-large library docking for discovering new chemotypes”.

The new virtual pharmacology platform, developed by scientists from the University of California San Francisco (UCSF) in collaboration with their colleagues from (UNC), currently contains hundreds of millions of 3D representations of novel synthetically accessible drug-like molecules, immediately suitable for large-scale docking studies, promising to change the landscape of modern hit discovery.

To illustrate the immediate practical benefit of the new “virtual pharmacology” platform, the scientists docked 99 million compounds against the AmpC, a bacterial enzyme, beta-lactamase, which is involved in antibiotic resistance, and another 138 million compounds -- against another unrelated target, the D4 dopamine receptor of brains cells, known to be involved in psychosis and addictive behavior.

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Comments:

  • Eliana Bryson 2019/02/21, 06:26 AM

    Seems pretty good idea but how we would be able to see reactions of different drugs on biological systems while everything is virtual, especially those drugs which have not so far been tested. I personally believe that it is a good idea for reducing cost of learning pharmacology as there is acute shortage of pharmacology skills and resources in our nurses industry. For professionals who are active on researching on new drugs and their reactions it might bring some benefits but we would need to enhance the circle of different complex mixture of drugs and their reactions, if we integrate artificial intelligence which provides with great number of possible reactions to the practitioner then it can save us great deal of effort and time.

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  • Maurizio Recanatini 2019/02/22, 13:10 PM

    I agree on the scenario of technological development emerging from the article. Very clear, indeed. I'd just like to observe that we should also manage to exploit such an impressive increase of computational potency to explore the complexity inherent to the biological systems. In other words, it is not sufficient to increase the number of compounds virtually tested, if we don't improve the description (simulation) of the target system. Again, it may be a matter of quantity and quality. I am convinced that machine learning, network science and in prospective AI will allow us to build integrated platforms of "virtual pharmacology", where ultra-large libraries will be virtually tested on cell, tissue or even whole organism in silico models.

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    • BiopharmaTrend 2019/02/22, 15:19 PM

      @Maurizio Recanatini, I am in a complete agreement with this viewpoint of yours. Creating this kind of "end-to-end" modeling pipelin will take considerably longer time, though... Considering enourmous complexity of biological systems, and the fact that we do not yet understand even DNA completely, I am afraid to say I do not quite believe it will happen anywhere closer than 20-30 years from now... But hopefully, some degree of predictive modeling on the level of cell-wide or intercellular-wide virtual screening will be achieved earlier. Who really knows...?

      -- Andrii

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  • Dr. Rick Sayegh 2019/11/22, 07:26 AM

    Thank you so much !

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