Our Innovative in silico drug design

In silico drug design became widely used in the past dedicate but it was not often employed in the real life, i.e. from the big pharma companies. This has been mainly changed during the last few years by the introduction of FEP+ technology by Schrödinger. Many projects are under development now in which most of the big pharmaceutical companies were involved. In this way, many other in silico approaches became also well recognized. The main reason for this progress is the new computation capability and in particular the use of modern GPUs instead CPUs for the calculations, which makes possible an acceleration of about 30-100 times. Unfortunately, although the cosmetics active substance and drug discovery are similar in their initial phase the in silico design is rare used by cosmetics companies.

Our innovative approach includes all modern approaches but also our own innovations. For instance, it is not secret that the initial compounds pose prediction and scoring is very important step and without a good starting ligand conformation and equilibrated protein structure in solution all other computational steps may fail. Moreover, it has been recently established that more and more of the drug candidate compounds have a mutable binding modes in a solution. Thus, we have developed a new strategy for both the binding mode and free energy scoring prediction based on the modified accelerated molecular dynamics (aMD). By our approach even the binding constants such as kin and koff can be more easily calculated with help of the computational power of the new GPUs such as Nvidia Pascal P100 Tesla generation. Further, we greatly improved the in silico hit compounds identification from an average of 10-20% success rate up to 70-80% by our new combination of structural based pharmacophore and docking virtual screen. This is in a great help during the initial stage of any drug discovery pipeline. Finally, although the replica exchange solute tempering (REST) method is indeed a good sampling approach and has been already introduced in the FEP+ calculations during the lead optimization process, the short FEP/REST simulation time does not guarantee impressive results neither in a case of flexible receptor binding sites nor homology modelling/MD derived structures. Thus we recently improved the FEP+ sampling protocol and achieved much better predictions of the binding affinities which will be of help in any hit to lead drug discovery project. Moreover, the ligands binding modes are often not well established thus our preliminary MD simulations are of great help. Because of the fact that the lead optimization is the most expensive step in the drug discovery we combine the FEP approach and the above mentioned our own method to achieve much better results.

Many companies claim that provide in silico drug discovery but a close view of the technologies used shows that only some steps of this complicate process are covered and mainly not well established methods are employed. Instead, we cover all steps by all sophisticated technology available. We can screen a databases with millions chemical compounds and precisely predict the lead drug candidates by many scoring ways. Further, for the lead optimization step the FEP methodology is used but the results are also supposed by our own developments.

Our technology combines but is not limited to following methods:
Artificial Intelligence (AI)
FEP+
Docking
Pharmacophore
Scoring by all available advanced functions and methods
Molecular dynamics
Accelerated Molecular dynamics
New core recognizing
Free energy perturbation (FEP)
GPU calculations

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In silico drug design helps to greatly reduce both the time and cost of your project without any compromise of the accuracy.

One of the main challenges facing modern drug design is to find new biologically active lead compounds, i.e. those which can bind to a target protein, and to reduce the price for such a project. For instance, if one tries to screen a library of 200 000 compounds, one will have to spend about 20 million US dollars only for these chemicals and much more for the other necessary reagents. Even screening of an inefficiently small collection of 2000 compounds will cost about 200K dollars for the chemicals. Further, the lead optimization process is the most expensive part of any drug discovery project as an individual step. It costs more than 400 millions dollars and the prediction of ligands activity, selectivity and water solubility is in of great help reducing dramatically both the time and cost of the project. More medicinal chemists relay on these predictions now which mainly based on the free energy (FEP) calculations. It has been shown in many real life drug discovery projects that the activity of the lead compound can be easy optimized in only 2 round calculations from micro to low nanomol activity. Many examples in regard to the optimization of ligand selectivity and water solubility are available now.

Thus, in silico drug design helps to greatly reduce both the time and cost of your project. It comprises a vast number of advanced scientific computational techniques and resources. Therefore, in silico drug design is not only a powerful tool for discovering novel potential drugs, but it can also lead to a huge cut in the required funding for this goal. Correspondingly, it is widely employed nowadays in big pharmaceutical companies. Many corporations suggest in silico methodologies either for screening new lead compounds or for an examination of the potential toxicity of already known chemicals.

However, most of their services are limited to one or few approaches, such as, for example, QSAR methods, which results in inefficient detection of the compounds activity, small possible structural diversity, and many other scientific problems.

We, at Micar21 Ltd., provide a modern scheme for the screening of millions of compounds and the discovery of new leads, based on the combination of the most advanced approaches nowadays, which ensures the success of your project!

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In silico drug design - screening for biological active compounds

For the in silico screening discovery of biological active compounds, we offer a combination of the most advanced approaches currently employed for such kind of projects. For the last few years it has become more evident that the combination of pharmacophore (ligand based) and docking screening (structure based) approaches is more successful than the use of only one of them. Thus, as an initial step in the screening of millions of compounds (for example ZINC database), as shown in the figure on the right, we combine the techniques above to reduce the potential active compounds to a few hundreds only. Docking is a method which predicts the preferred orientation of a molecule to a protein, when bound to each other to form a stable complex, as well as the strength of the association between them. Pharmacophore models explains how structurally diverse ligands can bind to a common receptor site. Both Docking and Pharmacophore are commonly used in screening novel bioactive ligands. Further, we re-dock the compounds in a more accurate way to the target proteins and then apply diverse advanced Molecular Dynamics (MD) methods to find the most promising bioactive chemicals. Finally, we send the selected compounds for experimental testing and, if requested, lead optimization. It is not a secret that the lead optimization is the most expensive step in any drug design project. Thus, the prediction of the ligands affinity in the frame of about 1 kcal/mol is an important factor. To meat these standards our team provide state of art approaches based on Free Energy of Perturbation (FEP) method. The FEP method has been greatly improved for the last year and can be applied now to most of the industrial projects. It is also well known that the FEP is the most precise method that gather an information for both the ligand selectivity and in the case of drug resistance. The combination of the much improved force field and the speed up of the computations on GPU's are the core of the recent achievements of this precise method.

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