seen from Chile
seen from Netherlands
seen from Netherlands
seen from Switzerland
seen from China
seen from United States
seen from United Kingdom
seen from Australia
seen from Pakistan
seen from Jordan
seen from China
seen from China
seen from Switzerland
seen from China
seen from China
seen from United States
seen from United States
seen from United States
seen from Malta
seen from United States
Kim and I touring container structures on our way to NPSA conference. Great visit. Kim bought up the store. #moleculedesign #santafe #ciscocontainers
#building #ciscocontainers #container #containermodification #shippingcontainer #containerhouse #storage #cargo (at Molecule Design)
April 2016 release of new Spark databases
The new release of Spark comes with new and updated fragment and reagent databases. These are designed to give you the widest sources of inspiration for your projects, whilst also enabling a close link between Spark’s suggestions and the chemistry that is available to you.
http://www.cresset-group.com/2016/04/april-2016-release-of-new-spark-databases/
Spark V10.4 released
A new version of Spark, our scaffold hopping and bioisostere replacement tool, is now released. V10.4 includes many new or improved features and gives access to new and updated chemical diversity.
The development of our applications is guided by our customers and this release is bursting with new features and science that you have asked for. A few of these are described below, however, I suggest that you use the software for yourself to discover the other features and see them in action.
http://www.cresset-group.com/2016/04/spark-v10-4-released/
Displacing crystallographic water molecules with Spark
Spark was used to carry out a water displacement experiment, starting from the published X-ray crystal structure of a selective inhibitor of Bruton’s tyrosine kinase (Btk). Spark was able to displace the water molecule bridging the interaction between the inhibitor and the P-loop backbone residues of Btk, correctly identifying the same alternative bicyclic indazole fragments found in the original publication which makes a direct interaction with the protein. The availability of a sufficiently diverse source of reagents was crucial in demonstrating the feasibility of this approach.
http://www.cresset-group.com/wp-content/uploads/2015/10/Displacing-crystallographic-water-molecules-with-Spark.pdf
New KNIME node and Pipeline Pilot (V2.4.0) components released
We are delighted to announce the release of the latest set of workflow components for KNIME™ and Pipeline Pilot™environments. This release introduces nodes for Activity Atlas, a component of Forge, and kNN plus a number of enhancements to existing components.
New example workflows and protocols have been created for both KNIME and Pipeline Pilot to illustrate the usage of these new components.
Read more: http://www.cresset-group.com/2015/10/new-knime-node-and-pipeline-pilot-v2-4-0-components-released/
September news from Cresset
http://www.cresset-group.com/2015/09/september-2015-newsletter/
Analyzing data in Torch V10.4 to help you choose the best new molecule to progress into synthesis
Torch is designed to provide medicinal and synthetic chemists with an application that enables the rapid 3D design of new molecules in an easy to use and easy to learn application. We understand that your molecules need more than activity and have always presented a range of physicochemical properties (TPSA, logP, MW etc.) for every molecule. Thelatest release of Torch includes many more features to help you understand the properties of new molecules. It expands the range of data analysis techniques that you can use to decipher SAR or choose the best new molecule to progress into synthesis.
In this blog post I explore some of the new additions and improvements to some old favorites. Almost all of these features are also present in TorchLite – the free version of Torch. You can download TorchLite for free.
Choosing the best design: Multi-parameter optimization
The choice of which molecule(s) to progress into synthetic chemistry depends on a number of properties – potential for activity, logP, MW, flexibility etc.. The problem faced by most project teams is one of balance in that although there is usually an ideal range of values for each property, not every property is equally important. For example, the ideal may be to have MW<500 and logP<3 but there may be more tolerance for a high MW if logP is well within range.
In the previous release of Torch radial plots were introduced as a simple way to display how multiple properties conform to a project profile. The new version of Torch has a modified radial plot properties window to enable a weight to be applied to each property independently. These weights are then used to combine the fit of each property to the overall project profile and provide a single score for how ‘good’ the molecule is. Additionally, we give the option to color the values in the results window by how well each property fits the profile that you have given. The result is a colorful and informative results table of many properties that can be sorted so that the ‘best’ molecules are placed together at the top.
Torch 10.4 Molecules table with radial plot and coloring of cells based on fit to project profile.
Radial plot configuration includes weighting of properties.
Organizing molecules
TAGS
Molecule roles and tags can be used to divide and label the dataset
One of the most popular inclusions in Torch V10.4 is the ability to tag molecules with an arbitrary label. First introduced in Spark as a voting mechanism between colleagues (each tags their favorite molecules with their name), this feature has wide utility in Torch. It could be used as a way of labeling different series, marking sets of molecules as interesting or highlighting different substitution patterns as well as identifying the molecules to progress to synthesis. Filtering the dataset on the tags is a powerful method of visualizing, selecting and examining subsets of molecules.
ROLES
Each molecule in Torch exists in a single ‘Role’. Previously there were only three Roles – ‘Reference molecules’, ‘Protein molecules’, and ‘Molecules’. In V10.4 you have the option to create additional Roles to store your molecules. Unlike tags, a molecule can have only one Role and hence it is a higher level division of your dataset. There is no limit to the number of Roles or how you use them, so you can divide your molecules in a way that works for you. For example, we often use Roles to keep track of different series, or for separating designed molecules from those that have been sent for synthesis and those that have been biologically tested! Once created, molecules can be dragged and dropped into a new Role or all the molecules in a single Role can be selected with a single click. This will further the improve the analysis of larger numbers of compounds and help you focus your attention on those that matter to you today.
New views
Alongside the new ways to organize your molecules come new ways to view your molecules.
TILES
The new tiles view of molecules enables you to see more molecules at a time than can be presented in the spreadsheet approach of the ‘molecules table’. Each tile can be configured with exactly the data that you are interested in, be it the radial plot of physical properties, the molecule tags or the structure. Tiles are still organized into Roles, can be sorted by any of the numerical columns in the project and are colored to match the compound in the 3D window.
Tiles configured to show structures, tags and radial plots and colored according to the radial plot score.
Uncolored tiles showing tags and selected data only.
GRAPHS
To aid analysis of data trends the graphing capabilities of Torch have been improved to include histograms, multiple series and some statistics. All graphs are interactive in that selection in one graph displays the compounds in the 3D window and selects the corresponding points in the other graphs. Using these simple tools greatly enhances your ability to interrogate your molecules to find the properties that matter to your project and then to use these in the design of new compounds.
Histograms and scatter plots are linked so that selection in one graph highlights the same molecules in all other graphs and plots.
Conclusion
Torch V10.4 is a powerful 3D design tool that includes SAR and data analysis capabilities to help get the right molecule into your project. Try it for yourself or contact us if you have specific questions about Torch V10.4.
Bioisosterism – harder than you might think
A common concept in medicinal chemistry is the bioisostere. The exact definition of a bioisostere is rather fuzzy, but the Wikipedia entry is as good as any: ‘bioisosteres are chemical substituents or groups with similar physical or chemical properties which produce broadly similar biological properties to another chemical compound.’ Bioisosteric replacements come in two general classes: core replacements, where the centre of a molecule is changed, creating a new chemical series; and leaf replacements where a replacement is sought for part of the molecule on the periphery, keeping it in the same series. Core replacements can get you out of a difficult ADMET or IP situation or can be a useful stepping stone to developing a backup series. Leaf replacements are more common as part of the day-to-day work of lead optimization – having discovered a substituent that works, the obvious thing to do is to try more things like it!
There are many software products available to search for bioisosteric replacements, both commercial and freely-available. The proliferation of methods is largely because searching for bioisosteres looks so easy. Look at the piece you want to replace, search a database for something largely the same size and geometry, and present the results. Simple, right? However, if you delve into it, the whole process is much more complex than it appears …
The first issue for a bioisostere replacement method involves deciding whether a fragment is physically the right size – are the connection points in the right place and at the right angle? Again this seems a trivially easy question to address until we consider that this is a 3D question. You need to consider the 3D geometry of the fragment – what conformation is it in? We chose to use a mixed approach to this: we provide both fragments derived from the CSD where the conformation comes directly from the small molecule crystal structure and large databases of fragments where we provide a small distribution of low energy conformations. As a result, you can just search experimental small molecule crystal conformations if you wish, but you can augment that search with a much larger and richer conformation database if you so desire.
The second criterion is pharmacophoric: a bioisosteric replacement must have ‘broadly similar biological properties’ and hence its interactions with the protein must be similar to the original molecule. Again, there’s a subtlety here. If I’m looking for a replacement for a triazolothiazole, then when I’m presented with a candidate fragment the obvious thing to do is ask “how similar is it to triazolothiazole?” This approach is flawed because the properties of the candidate fragment (and the initial triazolothiazole!) depend on their environment i.e. the rest of the molecule. This is especially true if you are interested in molecular electrostatics, as the electrostatic potential of a molecule is a global property that cannot generally be piecewise decomposed. That is why Spark performs all scoring in product space, not in fragment space (Figure 1). However, even if you are using a more crude method such as shape or pharmacophore similarity, treating the fragment in isolation can be very misleading.
Figure 1: Merging a new fragment can (subtly) change the electrostatics of the rest of the molecule. Red=positive, Blue=negative regions.
Working in product space brings advantages in steric considerations as well as electronic. Even though a particular fragment might have a beautiful geometric fit, and match the original core very well in terms of shape/electrostatics/color/pharmacophores/whatever, you still need to assess whether the fragment is compatible with the conformation of the original molecule. That assessment cannot be done in fragment space! In Spark we handle this in two ways. Firstly, all product molecules are minimized prior to scoring. If, due to steric or electronic effects, the candidate fragment just is not compatible with the desired conformation of the original molecule, then this minimization will distort the invariant parts and lead to a very low similarity score. Fragments which cause steric clashes or lone pair-lone pair repulsions are thus automatically filtered out (Figure 2). Secondly, we follow this up with an explicit assessment of strain energy around the newly-formed bonds, so the user can immediately see whether there are any causes for concern.
Figure 2: Toluyl is similar to phenyl, except when it causes a steric clash. Whether it does or not depends on what R is!
Another thing that can be drastically affected by the environment is the hybridization and charge state of the fragment (and the rest of the molecule). The main culprit here is nitrogen. Is an amine basic or not? Is the nitrogen pyramidal or trigonal planar? Both these questions depend strongly on what the nitrogen is attached to and hence you cannot assess the suitability of a fragment without reference to the chemical environment that you are going to place it in. These questions turn out to be surprisingly difficult to handle in a completely robust way: there is a large amount of code in Spark which assesses the local chemical environment around the newly-formed bonds and determines whether any hybridization or formal charge changes are required. It turns out you cannot just recharge the product molecule, as in some cases the user may have assigned particular charge states to parts of the molecule (based on experimental knowledge of the charge state when bound to the protein, for example) and you don’t want to undo that.
The final complication to performing the scoring properly (i.e. in product space) is accounting for the available degrees of freedom. If you are doing a core replacement, then the positon of the core is generally completely determined by the attachment points. However, if you are doing a leaf replacement, then the question arises as to what rotamer to choose around the attachment bond. The only solution is to perform a limited conformation search around that bond and score each conformer, which has the potential to get very slow although there are some computational tricks you can do to reduce the search space. It’s not just leaf groups that have to be searched: replacing the centre of a molecule might require a rotation scan if there are two attachment points which happen to be collinear.
So, our nice and simple bioisosterism calculation has become rather more complicated. Rather than just see if a fragment fits into the hole in the original molecule, we need to merge it in properly, assess any hybridization or formal charge state changes that are required, minimize the resulting molecule, perform a rotation scan around any underspecified degrees of freedom, recompute the electrostatic potentials, align to the original molecule and calculate the steric and electrostatic similarities. Only then can we decide whether the fragment is any good or not! Luckily, Spark does all of that for you, so as a user you can concentrate on the results, rather than on the complicated calculations required to produce those results.
Try it for yourself – download a free evaluation of Spark.
