It's "well known" that we all need to find the stronger inhibitors against any specific therapeutic target as possible. The motivation is that the strongest inhibitors are most probably less toxic. It's so obvious and thus needs to be checked.

To do so we took drug cards from the DrugBank and attempted to correlate the activity against the specific therapeutic targets (in log units) with LD50 (in log units as well).

The results are impressive on its own. In any activity range the toxicity of the compounds is very uniform. Let me repeat: there is the same probability to hit a highly toxic compound in pKI range, say 8-10 and 6-8. Once again: the toxicity is the ability of a compound to interact with a number of allimportant targets and has nothing to do with the ability of a compound to bind to a specific target of a therapeutic interest. What matters is of course the so called therapeutic window, which is the ratio of activity vs. toxicity.

We use a few chemical compounds vendors in our drug discovery programs (let us call them "Provider I", "Provider II", and "Provider III"). Nowadays chemicals providers normally claim about 1M of drug-like compounds readily available for shipping at a very reasonable cost. A few dozen of providers, with up to 10 of them very large overall could give up to a 10M of distinct compounds. How many compounds are out there?

To optimize our in-silico operations we use clustering to collect molecules of similar structure. The cluster centroids represent the clusters in the lead identification process and hence the number of clusters is a good measure of chemical diversity of a library. To cluster  the compounds we use Tanimoto similarity criterion, since the metrics lets us use fingerprint sorting to avoid much of pair-distance calculations. The number of common structures is a measure of (dis)similarity between a two chemical libraries. With this in mind we performed a co-clusterisation of the three vendor libaries (see the Graph on the left). It appears that the vendors II and III are roughly of the same "size" (diversity), whereas the vendor I has the most diverse collection of the compounds. What's remarkable, is that number of the compounds is the largest in the collection II and the smallest in the collection III.

Now we are ready to see what the claimed "drug likeness" of the compounds might mean. There are two great online libraries containing all the (small molecules) drugs (Drug Bank) and a large collection of the compounds with identified activity against specific molecular (proteins) targets. To see how the compound libraries and the biologically active compounds relate, we co-clustered each of the vendor libraries with those obtained from the DrugBank and BindingDB databases:

The results are remarkable in a few ways. First of all, all the three vendor libraries are very similar in their properties. Each of them contains roughly the half of the similarity classes representing the known drugs. This means that half of the current drugs is not "drug like enough" to be picked up by the modern "drug like" compound selection algorithms. Still, the number of stable compounds of reasonable size is about one or two orders of magnitude larger than the size of the modern vendor libraries. This means that we still have a long way to go on a chemical synthesis progress road to cover the chemical diversity enough at least to "rediscover" just the already known drugs!

There is also another remarkable conclusion: about 30% of the compounds classes overlap with the biologically active compounds from the BindingDB and therefore up to a one third of the compounds is biologically active! This may be a signature of a major library construction flaw: the compounds where selected to be "drug like", meaning rather similarity to compounds with known biological activity. In practice such promiscuity could mean a lot of side effects and toxicity.

An ideal drug cures a decease and does not kill a patient (or even lab animals in the course of preclinical testing). Usual drug discovery paradigm is based on studying a compound's properties against a specific, normally decease-related (protein) target. The ability of a compound to bind (inhibit) a specific target is called efficacy.

Even if the efficacy is good, another important property of a compound is its toxicity. Toxicity is related to the compound physical properties, such as solubility etc, as well by its ability to bind to and hence inhibit various vital human proteins (and may be even DNA and RNA).

Common sense suggests that an ideal compound binds its specific drug related target and does not bind to anything else. Anything in between is toxic, at least to a some extent. For example, most of important properties utilize ATP molecules, which means that human body contains a lot of ATP-bindig proteins. If you make a drug attacking an ATP-binding site of a "bad" protein, most probably, a lot of "good" and useful proteins will be also affected. In that case your compound should be toxic. This is indeed the case for many cancer drugs attacking ATP-binding sites of kinases.

The latter statement is the foundation of our approach. Although it's quite conceptually simple, it's useless unless it can be supplemented by a meaningful mathematical model. Let us dwell into some more details to see how the whole thing can be made working.

Let us overview important properties of a drug candidate. First there is a bunch of physical properties, such as solubility, differential solubility, LogP (namely the difference between water and lipid solubility) etc. These quantities are easy to measure, are of direct physical meaning and can be pretty easily calculated (with or without QUANTUM software).

Another set of characteristics defines a compound ability to penetrate through cell membranes and its biochemical in liver. These are quantities deturmining bioavailability, half life, volume of distribution etc. None of such quantities can be evaluated using the simple physical properties alone. For example, drug absorbtion depends on the molecule interaction with proteins actively transporting the molecules through the cell membranes.

The bottom line: bioavailability and other quantities require understanding of a compound binding properties to a selected number of proteins participating in a compound transport and metabolism.

So the conclusion is that IF YOU KNOW WHICH PROTEINS ARE IMPORTANT, AND IF YOU CAN CALCULATE HOW YOUR COMPOUND BINDS TO THEM, YOU KNOW THE COMPOUND PHARMACOLOGICAL AND TOXICOLOGICAL PROPERTIES

Now the only problem how to identify those "important" proteins.

Fortunately, there are thousands of molecules with known properties. What we can do is the following:

- take a molecule
- calculate its binding to any human protein with known 3d structure
- use the obtained binding affinities (numbers) as a molecule's binding profile fingerprint (the Biological Spectrum), characterizing the ability of the molecule to interact with the whole human proteome

Now assume we know such Biological Spectra for 1000s molecules with well known properties. This means we can now datamine the fingerprints->known properites relations. The basic premise is, of course, that the molecules with similar fingerprints have similar properties.

We have a number of proofs of such technology working. The most recent one is the prediction of active transport drug absorption properties for drug like molecues based on the binding data against human brain hexokinase type I-related protein. We prove that the binding energy of a compound against the protein may serve to distinguish between the passively and actively transported molecules and even help to calculated the drug absorbtion quantitatevely.

Prediction of toxic properties of small drug like molecules is a big challenge both from theoretical and practical points of view. Quantitatively people use different measures of toxicity such as Maximum Recommended Daily Dose (MRDD) or Lethal Dose (LD50).

Accurate prediction of such endpoints is only possible if both quantities are "physical" characteristics of a compound, rather than signatures of ever changing views of regulating agencies.

The plot on the left represents the "correlation" between experimental values of MRDD (according to FDA) and LD50 (rat) taken from different sources. As you can see, both quantities have a reasonable degree of correlation for low or intermediate toxicity levels. As soon as toxic compounds are considered, the correlation is lost and apparently no good prediction starting from physical properties of a molecule can be done.

For a moderately toxic molecule we can derive an approximate relation:
-LogMRDD = -LogLD50+2.
In "a human language": the lethal and the maximum recommended dose are roughly two orders of magnitude different; a concentration killing a mice is in fact the maximum recommended for a human being.

QUANTUM hERG (q-hERG) screening assays is a unique and innovative computational approach, which allows you to predict from a molecule structures of compounds their inhibition constants (IC50) for hERG channels.

q-hEARG features:

• Output is pIC50 values (-logIC50) for the molecules. The accuracy of prediction is 1.1 pIC50 units;
• No training sets or QSAR methods applied;
• hERG inhibition prediction is made by docking of compound on Quantum Pharmaceuticals’ Proprietary Flexible 3D structure of hERG;
• Docking is based on quantum and molecular physics (see Quantum Science Core for an overview);
• Average correlation has RMSD=1.18 pIC50 unit, and correlation coefficient = 0.82;
• Easy to use user interface, no special hardware requirements, both Linux/Windows supported;
• You can also request services based on QUANTUM hERG Screening Assays.

q-hERG is an independent software module, sharing the user interface and basic usage concepts with our q-ADME: ADME/PK properties prediction software, q-Mol: physico-chemical properties calculator, and q-Tox: toxicological profiling software. More information, including q-hERG product booklet can be obtained from the Quantum Pharmaceuticals products site.

Obtaining Q-Albumin software:

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