3D Conformer Overlay Screening for DEL Binder Selection
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Solution Overview
Problem
DNA-encoded libraries (DELs) contain a significant proportion of artefactual compounds, leading to high false-positive rates in small molecule drug discovery, which wastes resources and time due to inefficient confirmatory testing.
Innovation Solution
A computer-implemented method and system that identifies small molecule binders by generating and comparing three-dimensional (3D) conformers within and across compound groups to find consensus overlays based on similarity metrics, reducing false-positive rates and enabling reliable virtual screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional statistical methods are used to select compounds from DEL datasets, then the selection process is simple and intuitive, but the false-positive rate is high
Solution Approach 1:
The patent transitions from two-dimensional chemical structure analysis to three-dimensional conformer analysis. By generating multiple 3D conformers for each compound and analyzing their spatial arrangements, the method adds a dimensional perspective that enables identification of consensus overlays, thereby reducing false positives while maintaining operational simplicity
Solution Approach 2:
The patent performs preliminary computational analysis by generating 3D conformers and identifying consensus overlays before experimental confirmatory testing. This preliminary virtual screening step filters out false positives in advance, ensuring that only high-confidence compounds proceed to expensive and time-consuming wet lab validation
2Reliability
If sophisticated statistical pipelines are used to decrease false-positive rate, then the false-positive rate decreases, but the assumption that higher signal strength correlates with genuine binding remains limiting
Solution Approach 1:
The patent changes the fundamental parameters used for compound evaluation from signal strength metrics to three-dimensional structural consensus metrics. By analyzing the spatial arrangement and overlay of conformers across multiple compounds, the method identifies genuine binders based on structural consistency rather than signal intensity, overcoming the limitations of sophisticated statistical pipelines
3Reliability
If experimental approaches like polynucleotide affinity tags are used, then the false-positive rate decreases, but new artefacts are introduced and costs significantly increase
Solution Approach 1:
The patent replaces physical experimental modifications (polynucleotide affinity tags, orthogonal selections, additional sequencing runs) with computational methods. By using 3D conformer generation and consensus overlay analysis on existing DEL data, the method achieves false-positive reduction without introducing new artefacts or incurring additional experimental costs
Solution Approach 2:
The patent creates virtual copies of compounds in three-dimensional space by generating multiple conformers. These computational models serve as proxies for physical compounds, allowing virtual screening and consensus analysis to be performed in silico before any experimental validation, thereby avoiding the costs and artefacts associated with physical experimental approaches
4Reliability
If confirmatory testing is performed on all DEL hits, then genuine binders are identified, but time and resources are wasted on artefactual compounds
Solution Approach 1:
The patent performs preliminary computational screening by generating 3D conformers and identifying consensus overlays before experimental confirmatory testing. This pre-filtering step identifies high-confidence genuine binders and excludes false positives in advance, ensuring that limited experimental resources are focused only on the most promising compounds, thereby reducing overall time and resource consumption
Data Source
AI summary
Disclosed is a computer-implemented method for identifying small molecule binders within a DNA-encoded library (DEL) dataset. Said method comprises: obtaining a DEL dataset comprising enriched compounds; grouping enriched compounds into one or more compound groups based on a grouping criterion selected from at least one of: chemotype, library, enrichment level, or linear feature; generating a plurality of three-dimensional (3D) conformers, for each enriched compound in each compound group; comparing generated 3D conformers within one or across two or more compound groups to identify consensus overlays based on similarity metrics exceeding a predefined threshold; and selecting small molecule compounds forming said cross-group consensus overlays as small molecule binders having affinity for a target of interest. Disclosed also is a system for identifying small molecule binders within a DEL dataset. Said system comprising: a processor configured to perform aforementioned steps of method and an interface for displaying selected small molecule binders.