Tool Compounds

Tool compounds can span a wide range of drug-likeness. Potency and selectivity are not the most critical parameters for tool compounds, and EC₅₀ and IC₅₀ values in the micromolar range are tolerated as long as they are compatible with the compound's solubility. Some already possess excellent parameters and are legitimate drug candidates. Others, however, exceed a molecular weight of 500, contain numerous charged groups that would significantly affect bioavailability, or are metabolically unstable (e.g., containing ester or phosphate groups).

Unfortunately, this is where the problems start. Often, undesired functionalities or the sheer size of compounds are necessary to even interact with the target structure. Take ATP, for example: the triphosphate chain is crucial for binding to various targets, but its rotatable bonds and charges make it unsuitable for oral use. Reactive Blue 2 (shown left) promiscuously binds to many of the same structures as ATP, most likely due to the negatively charged sulfonic acid groups, which behave as bioisosters to the phosphate groups of ATP. Removing these groups, however, causes the compound to lose all biological activity at the targets.

To transition from a compound to a drug-like structure, it requires several sophisticated strategies to preserve the essence of the pharmacophore and key interactions, while escaping the limitations of the original compound’s velocity.

The simplest yet most challenging method is structure-based virtual screening. Tool compounds can provide extraordinary assistance by helping identify the binding pocket and key interactions. By refining the docking pose, such as through MD simulations, both the ligand and the binding pocket can adapt to one another (induced fit), leading to a more accurate prediction of the binding mode. If analogs of the tool compound are available, it becomes possible to develop a strong working hypothesis via SARs, which can provide insights into the properties a drug-like compound should possess.

The unique strength of virtual screening lies in the incorporation of 3D information, which takes into account interactions within the binding site. Additionally, pharmacophore constraints can be applied to ensure that generated poses include the identified key interactions, increasing the likelihood of successful confirmation as active compounds. The inclusion of 3D interactions enables the discovery of compounds that may have low Tanimoto similarity to the tool compound (low 2D similarity), indicating the presence of novel scaffolds. By carefully selecting the molecule set, it is possible to focus on compounds that already exhibit drug-like properties.

Virtual screening does not necessarily require a target structure. Ligand-based methods utilize a known binder (in our case, the tool compound) to align it with a molecule set and identify compounds that display 3D similarity to the ligand structure.

It is worth noting that information about interactions, including potential clashes within the binding site, is not considered in this approach. While this might seem like a significant loss of information, it is important to remember that structure-based approaches often rely on a single binding mode hypothesis, which is typically generated through docking. Multifunctional tool compounds may exhibit multiple potential binding modes, and without a series of compounds, there may not be enough data to establish coherent SARs. This inherent fuzziness of ligand-based methods can sometimes be an advantage, still leading to the identification of active hits.

BioSolveIT software for 3D LBDD:

• SeeSAR's Similarity Scanner Mode: Can perform ligand-based virtual screening using a tool compound as query. Pharmacophore constraints can be applied to improve and fine-tune the results.

Command-line tools for 3D LBDD:

• FlexS: 3D compound alignment.

If you want to take abstraction one step further, you can utilize 2D ligand-based methods that infer the similarity of a molecule based on its structural formula. Interestingly, this approach again provides an opportunity to escape the velocity of the tool compound when searching for specific types of similarity. Substructure and fingerprint searches often retrieve closely related compounds; however, these methods may encounter similar optimization limitations as the query compound. This is because the pharmacophore must be treated very rigidly to ensure that retrieved molecules remain relevant and rank highly

Fuzzy pharmacophore screening allows for greater variance in results while retaining the essential 'flavor' of the molecule. It is a scaffold-hopping approach that can also be extended to the pharmacophore of a tool compound.
Coupled with ultra-large Chemical Spaces, this creates an entirely new method for discovering compounds that resemble tool compounds in their pharmacophores.