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LeadBuilder – Virtual Hit Screening

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To identify small molecule starting points for a drug discovery program, Domainex has successfully used its LeadBuilder virtual screening platform across a large number of programs; against targets as diverse as kinases, proteases, protein-protein interactions, methyltransferases and ion channels.  Many of these programs are now at the Candidate Drug stage, with a selection currently in the clinic. 

The LeadBuilder platform has also been used successfully to design peptidomimetics on behalf of Ark Therapeutics Plc. and drug-like replacements to natural products on behalf of Hypha Discovery Ltd.

Domainex's LeadBuilder hit identification approach delivers and is highly efficient. The LeadBuilder screening approach was designed with the need to quickly and cost-effectively identify developable hits. Domainex’s approach to the virtual screening of these compounds differentiates it from others.

Domainex's LeadBuilder approach typically results in focussed hit-like compound screening sets of 1,000-10,000 small molecules, ready for biochemical testing. 

This approach is highly efficient (only 3-4 months to deliver hits) and when coupled to Domainex's Combinatorial Domain Hunting Platform, results is significant savings in early discovery timelines; cutting typical timelines of 18 months down to only 8-9 months from target amino acid sequence to hit series (Pammolli et al., 2011). In addition, the resultant hits from LeadBuilder are derived from Domainex’s pre-filtered compound screening database, so are developable and already exhibit hit-like or lead-like properties; resulting in reduced hit-to-lead phase timelines.

LeadBuilder has been successfully used by Domainex to identify focussed screening sets which have a high hit-rate in biological assays.  A typical random screen might have a hit-rate in a biochemical assay of 0.01-0.1%, whereas we often find that LeadBuilder affords screening sets with hit-rates of between 1-10%.  In the two cases where our clients made side-by-side comparisons of LeadBuilder with other vendors’ proprietary focussed libraries, LeadBuilder set gave 10-fold higher hit-rates.  Furthermore, the excellent property profile of LeadBuilder hits means that they are suitable for rapid progression towards candidate drugs, and we have several examples where this has been achieved within two years.
LeadBuilder integrates the computational capabilities of Domainex into three key areas: compound Library design and selection (LibraryBuilder); protein modelling (StructureBuilder); and virtual screening (ScreenBuilder)

The LeadBuilder platform encapsulates many aspects of Domainex scientists’ expertise in successful drug-hunting. This know-how and experience is the key to the success of the LeadBuilder approach, which is enabled by a state-of-the-art combination of modelling tools and commercial software from Accelrys and CCDC, and our databases of commercially available compounds which total over 15 million distinct chemical entities. 


The LibraryBuilder module contains several databases of commercially-available compounds.  We use sophisticated algorithms to calculate molecular properties and to predict the physicochemical, pharmacokinetic and toxicity profiles for these compounds.  We can then use these assessments to rank or filter them according to their lead-like characteristics. 

Perhaps the most important of our databases is the “NICE” virtual collection of over 1.5 million commercially- available compounds that have been carefully filtered to meet all of our criteria for an “ideal” screening hit: they have very favourable molecular properties and good predicted ADME and toxicity profiles.


Using our ScreenBuilder methodology we are able to interrogate our databases with in silico screens that we can develop from knowledge of the target protein, and/or any known ligands.  This means that we can start with information on the target protein (including homology models), or knowledge of one or more known ligands, or with data on both protein and ligand.  ScreenBuilder can use a number of search protocols, including pharmacophore screens, docking, and privileged fragment recognition.



Fabio Pammolli, Laura Magazzini & Massimo Riccaboni (2011). The productivity crisis in pharmaceutical R&D. Nature Reviews Drug Discovery, 10, 428-438