Combinatorial chemistry
CTB-Core
CTB-Core
The CTB-Core platform links combinatorial chemistry and virtual screening at the Auer Lab. It encompasses many aspects of cheminformatics, virtual screening and modelling for compounds on solid support. Specifically designed to complement our CONA technology, the CTB-Core platform aids in library planning, building block selection, and hit optimisation. Integrating a number of technologies, we are able to operate on targets with or without known structure and known ligands. A decision tree based on the available information of a target’s 3D-structure, binders/inhibitors and the representation of the scaffold of interest in the CTB-Core-Knowledge Base defines the strategy of identification of hits for most targets. Current developments in machine learning techniques are addressing protein sequence driven library design, allowing operation on targets with no known active compounds or x-ray structure. An exciting application of the CTB-Core platform is library reuse/repurposing; drawing on our expertise in drug repurposing and bioisostere replacement, this has been applied in a library context and allows reuse of libraries for optimum efficiency and impact. With a database of enumerated scaffolds, and the combinatorial explosion of unique small molecules possible, we have developed an approach known as “Representative Sets”, in which the core scaffold of a molecule is evaluated in a computationally affordable manner, without sacrificing diversity afforded by a normally inaccessibly large number of possible R-groups.
The CTB-Core platform links combinatorial chemistry and virtual screening at the Auer Lab. It encompasses many aspects of cheminformatics, virtual screening and modelling for compounds on solid support. Specifically designed to complement our CONA technology, the CTB-Core platform aids in library planning, building block selection, and hit optimisation. Integrating a number of technologies, we are able to operate on targets with or without known structure and known ligands. A decision tree based on the available information of a target’s 3D-structure, binders/inhibitors and the representation of the scaffold of interest in the CTB-Core-Knowledge Base defines the strategy of identification of hits for most targets. Current developments in machine learning techniques are addressing protein sequence driven library design, allowing operation on targets with no known active compounds or x-ray structure. An exciting application of the CTB-Core platform is library reuse/repurposing; drawing on our expertise in drug repurposing and bioisostere replacement, this has been applied in a library context and allows reuse of libraries for optimum efficiency and impact. With a database of enumerated scaffolds, and the combinatorial explosion of unique small molecules possible, we have developed an approach known as “Representative Sets”, in which the core scaffold of a molecule is evaluated in a computationally affordable manner, without sacrificing diversity afforded by a normally inaccessibly large number of possible R-groups.
