Next generation drug discovery service platform
Access to chemical matter is a major bottleneck in drug discovery.
Established industry standards and screening methods often fail to deliver molecular starting points for drug design. Despite accumulating large collections of compounds and screening millions of molecules, the industry struggles to unlock chemical matter. Biologically validated targets with high therapeutic potential are deemed as “undruggable”.
As a result, projects stagnate, milestones are missed, and patients are not reached.
CrystalsFirst is disrupting the access to structural data for development of small molecule therapeutics for challenging protein targets.
Our proprietary technology SmartSoak® enables an up to 10X accelerated process in crystallography by solving the problems of crystal soaking. SmartSoak-enabled crystallographic screens deliver hit rates up to 30 %.
It’s application is independent from the modality: Â it can be used for non-covalent and covalent compounds.Â
The technology is independent from the nature of the protein and has been successfully applied for over 30 different protein targets.
The FastForward is an automated pipeline for processing and smart refinement of crystallographic data.
The standardization and automation of the process from raw data to refined structure, enables detection of up to 25% more fragments hits as compared to manual refinement.
FastForward as a flexible pipeline can be parallelized for multiple projects delivering a data flow from high throughput crystallographic screenings fast.
FragAIÂ is an automated workflow for generation of active molecules for therapeutics development using chemoinformatics, bioinformatics and machine learning.
Our methodology uses the data flow from SmartSoak and FastForward output. The step of fragment hit expansion and evolution builds on validated fragment hits. The sampled chemical space can be adapted flexibly. Also, FragAI follows the paradigm of putting “structural data first” to identify bioactive compounds leading to shorter computational time and in parallel to higher success rates.
