Universal Fragment Library Design Platform

The success of chemoproteomic screenings depends on the screening technique and quality of the screening libraries. Existing covalent and noncovalent fragment libraries are typically selected by diversity analysis of available fragments that possess desirable properties. Our fragment library design platform is an assembly of several key technologies required to revolutionize the design of fragment libraries. We deliver exclusive in silico libraries on a first-come-first-serve basis.

AI Services

ChemPass in collaboration with Professor Gyorgy Keseru’s laboratory has developed all the necessary technologies that enable us to revolutionize fragment library design via our new platform. Instead of traditional diversity analysis, our universal fragment design platform evaluates the combinations of all experimentally observed pharmacophore multiplets and growth vectors as well as synthetic feasibility, warhead reactivity, and spacer diversity for covalent fragments sets. The result is a universally applicable covalent or noncovalent fragment library that delivers good success in both easy and challenging screening campaigns. Due to the built-in customization capabilities of the platform, the library design can target specific protein classes, and the library size and the synthetic cost can also be minimized. The concept has been validated by screening a test library against a validated and a hard target producing a hit rate and binding site coverage against the hard target much superior than results obtained with a standard fragment library.

AI Services
AI Services

Traditional fragment libraries designed by diversity analyses have been shown to include a large number of redundant pharmacophore and spacer motifs resulting in high hit rates of redundant rings for certain targets and few-to-no hits against pockets of difficult targets. Classical fingerprints do not work well with tiny fragment size molecules and fingerprints in general represent pharmacophore concepts poorly. Furthermore, potential growth vectors for warheads or substituents are not taken into account by traditional library design approaches despite evidence that most binding sites will only accommodate a few growth vectors. The overall result is a highly variable experimental hit rate and high risk of missed opportunities.

AI Services

ChemPass in collaboration with Professor Gyorgy Keseru’s laboratory has developed all the necessary technologies that enable us to revolutionize fragment library design via our new platform. Instead of traditional diversity analysis, our universal fragment design platform evaluates the combinations of all experimentally observed pharmacophore multiplets and growth vectors as well as synthetic feasibility, warhead reactivity, and spacer diversity for covalent fragments sets. The result is a universally applicable covalent or noncovalent fragment library that delivers good success in both easy and challenging screening campaigns. Due to the built-in customization capabilities of the platform, the library design can target specific protein classes, and the library size and the synthetic cost can also be minimized. The concept has been validated by screening a test library against a validated and a hard target producing a hit rate and binding site coverage against the hard target much superior than results obtained with a standard fragment library.

AI Services

Traditional fragment libraries designed by diversity analyses have been shown to include a large number of redundant pharmacophore and spacer motifs resulting in high hit rates of redundant rings for certain targets and few-to-no hits against pockets of difficult targets. Classical fingerprints do not work well with tiny fragment size molecules and fingerprints in general represent pharmacophore concepts poorly. Furthermore, potential growth vectors for warheads or substituents are not taken into account by traditional library design approaches despite evidence that most binding sites will only accommodate a few growth vectors. The overall result is a highly variable experimental hit rate and high risk of missed opportunities.