DNA-Encoded Libraries with ML-Supervised Data Processing

Explore the potential of DNA-encoded libraries, a method that gives access to screening up to 3B compounds in a single tube! Our services leverage the full potential of this approach, enabling efficient screening of diverse compounds, and streamlining the path to novel therapeutic breakthroughs. DEL-ML-CS Schema Here at Chemspace, we offer numerous services connected with DEL screening. The workflow starts with the ability to pick a library from our vendors or create a custom DNA-encoded library based on your project goal. Different types of screening are available. The classical approach results in data analysis and off-DNA synthesis of the proposed hits, while the DEL-ML-CS workflow opens the door to finding more diverse compounds from the compound collection of choice (Enamine REAL Space, stock compounds, etc.) harnessing the power of machine learning. Both workflows can be performed on a single DEL screening.
Library options
  • HitGen OpenDEL 4.0
  • 3B compounds HitGen Open DEL 3.0
  • 2B compounds AlphaMa DEL kit
  • 1B compounds Covalent DEL kit
  • 9M compounds Custom DELs
Types of screening
  • Affinity selection
  • Photo-crosslinking selection
  • Covalent library screening
  • Target RNA

Classical Workflow

classical workflow schema
By screening DEL libraries against a target of interest, researchers can identify lead compounds that bind to the target with high affinity and selectivity. The compound selection is done based on the data analysis of the screening results.
Advantages:
  • The ability to rapidly screen large numbers of compounds in a single experiment;
  • Reduced cost of screening;
  • A high degree of chemical diversity.
classical workflow schema
del-ml-cs workflow schema

DEL-ML-CS Workflow

del-ml-cs workflow schema
Chemspace offers a service that combines DNA-encoded libraries (DEL), Machine learning (ML), and large chemical spaces (CS) to speed up the discovery process and reduce its cost while maintaining a high hit rate. This idea was first introduced by Kevin McCloskey et al.[1], who used machine learning on DEL to select compounds from commercial spaces. This approach is perfect for projects where you have access to purified protein but lack crystal structure or any knowledge about its activity.
Advantages over traditional DEL screening:
  • Saving time and effort. The off-DNA synthesis step remains the time and resource-limiting factor;
  • Cost-effectiveness. Compounds for testing are selected from commercially available spaces, their price is significantly lower compared to custom synthesis;
  • Exploration of chemical space. Upon request, we can provide all the compounds with high predicted scores for you to explore internally.
Advantages over traditional DEL screening:
  • Saving time and effort. The off-DNA synthesis step remains the time and resource-limiting factor;
  • Cost-effectiveness. Compounds for testing are selected from commercially available spaces, their price is significantly lower compared to custom synthesis;
  • Exploration of chemical space. Upon request, we can provide all the compounds with high predicted scores for you to explore internally.

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References
  1. McCloskey, K.; Sigel, E. A.; Kearnes, S.; Xue, L.; Tian, X.; Moccia, D.; Gikunju, D.; Bazzaz, S.; Chan, B.; Clark, M. A.; Cuozzo, J. W.; Guié, M.-A.; Guilinger, J. P.; Huguet, C.; Hupp, C. D.; Keefe, A. D.; Mulhern, C. J.; Zhang, Y.; Riley, P. Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding. J. Med. Chem. 2020, 63 (16), 8857–8866. https://doi.org/10.1021/acs.jmedchem.0c00452