Design and Development of a Technology Platform for DNA-Encoded Library Production and Affinity Selection

DNA-encoded libraries (DELs) have emerged as an efficient and cost-effective drug discovery tool for the exploration and screening of very large chemical space using small-molecule collections of unprecedented size. Herein, we report an integrated automation and informatics system designed to enhanc...

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Bibliographic Details
Published in:SLAS discovery 2018-06, Vol.23 (5), p.387-396
Main Authors: Castañón, Jesús, Román, José Pablo, Jessop, Theodore C., de Blas, Jesús, Haro, Rubén
Format: Article
Language:English
Subjects:
Automation - methods
Databases, Factual
DNA - chemistry
Drug Discovery - methods
Gene Library
High-Throughput Nucleotide Sequencing - methods
Machine Learning
Small Molecule Libraries - chemistry
Software
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Summary:DNA-encoded libraries (DELs) have emerged as an efficient and cost-effective drug discovery tool for the exploration and screening of very large chemical space using small-molecule collections of unprecedented size. Herein, we report an integrated automation and informatics system designed to enhance the quality, efficiency, and throughput of the production and affinity selection of these libraries. The platform is governed by software developed according to a database-centric architecture to ensure data consistency, integrity, and availability. Through its versatile protocol management functionalities, this application captures the wide diversity of experimental processes involved with DEL technology, keeps track of working protocols in the database, and uses them to command robotic liquid handlers for the synthesis of libraries. This approach provides full traceability of building-blocks and DNA tags in each split-and-pool cycle. Affinity selection experiments and high-throughput sequencing reads are also captured in the database, and the results are automatically deconvoluted and visualized in customizable representations. Researchers can compare results of different experiments and use machine learning methods to discover patterns in data. As of this writing, the platform has been validated through the generation and affinity selection of various libraries, and it has become the cornerstone of the DEL production effort at Lilly.
ISSN:2472-5552
2472-5560
DOI:10.1177/2472555217752091