HTS/DEL Library Screening Results Analysis

AI-Enhanced Hit Triaging, SAR Mapping & Chemoinformatics Clustering. From Raw Screening Data to Validated Lead Series.
ML-Trained Hit Scoring DEL Deconvolution Wet-Lab Validation

Raw HTS/DEL data is a goldmine that most teams cannot refine. MagHelix™ deploys AI-trained triaging and chemoinformatics clustering to convert screening output into experimentally validated hit series.

Why HTS/DEL Data Analysis Is the Critical Bridge Between Screening and Synthesis?

A seed-stage biotech runs one DEL screen but lacks the informatics to decode DNA barcodes into structures or filter PAINS. A pharma team generates 1 billion data points but cannot connect sequencing reads to medicinal chemistry decisions. The MagHelix™ platform closes this gap: we decode DEL barcodes, triage HTS hits with ML models calibrated on our internal SPR/BLI/ITC database, and hand off prioritized series to Hit to Lead with full structural rationale.

What Sets the Analysis Platform Apart

AI-Trained Hit Triaging

ML models trained on internal biophysical data (R² > 0.75) classify true binders vs. PAINS, aggregators, and fluorescent artifacts. ADMET Prediction & Modeling flags liabilities before prioritization.

DEL-Specific Deconvolution

Proprietary pipelines decode split-and-pool DNA barcodes, calculate enrichment scores with confidence intervals, and map Structure-Enrichment Relationships (SER).

Wet-Lab Validation Loop

Ranked hits proceed directly to Hit Biophysical Characterization, Molecular Docking Services, or Co-crystallization.

The HTS/DEL Analysis Suite

Contents

HTS Data Analysis & Hit Triaging

From Raw Plate Reads to Prioritized Hit Lists

DNA-encoded library deconvolution workflow showing sequencing reads mapped to chemical structures via barcode decoding.
  • Data QC & Normalization — Z'-factor calculation, signal-to-noise assessment, and plate-edge correction across multi-million-compound campaigns.
  • ML False-Positive Filtering — Random Forest and graph neural network classifiers trained on confirmed binders vs. PAINS/aggregators.
  • Multi-Parameter Optimization — Consensus scoring integrating activity, selectivity, ADMET flags, and synthetic accessibility.

For virtual biotechs without cheminformatics teams, our triaging delivers a Tier-1 hit list within 48 hours. For pharma, protocols integrate with Pharmacophore Modeling & Screening and Structure-Based Virtual Screening (SBVS).

DEL Deconvolution & SER Mapping

DNA Barcode Decoding to Chemical Intelligence

SAR heatmap matrix displaying R-group substitution effects on compound potency across a congeneric series.
  • Sequencing Data Processing — FASTQ parsing, barcode error correction, and copy-number normalization for Illumina output.
  • Enrichment Scoring — Z-score, effect-size, and Poisson-based calculations with FDR control per building-block combination.
  • SER Mapping — Identification of privileged scaffolds and synergistic building-block pairs driving target affinity.

Most DEL providers return raw sequencing files. Our pipeline translates DNA counts into enrichment maps, enabling direct comparison with Ligand-Based Virtual Screening (LBVS) and QSAR Analysis.

SAR Analysis & AI Hit Expansion

Chemical Intelligence for Medicinal Chemistry

Machine learning model architecture for DEL screening data classification, showing neural network layers processing molecular fingerprints.
  • R-Group Decomposition — Automated fragmentation to identify potency-driving functional groups.
  • Activity Cliff Detection — Structural alerts where minor modifications cause large affinity shifts.
  • AI Generative Expansion — VAE and GAN models propose novel analogs in underexplored chemical space.

For Fragment-to-Lead programs, SAR mapping reveals which fragments merit scale-up. AI-expanded analogs feed directly into De Novo Drug Design on the MagHelix™ AI-Based Drug Discovery (AIDD) Platform.

Platform Instrumentation

Software / System Core Capability
KNIME / Pipeline Pilot Automated HTS/DEL data workflows, plate-map integration, and multi-source data fusion.
RDKit / OpenEye Chemical informatics, fingerprint generation, and molecular property calculation.
Scikit-learn / XGBoost / ChemProp ML model training: Random Forest, SVM, MLP, and graph neural networks for hit classification.
Biacore 8K / S200 High-throughput SPR binding confirmation and kinetics for AI-prioritized hits.
Octet RED96e Label-free BLI screening for rapid hit validation and affinity ranking.
GROMACS 2023 / AMBER 22 All-atom MD validation of AI-predicted hit poses and stability assessment.
CDD Vault / Dotmatics ELN-integrated data management and handoff to Hit Biophysical Characterization.

Standardized Workflow

Project Workflow

A standardized, milestone-driven execution system. From raw screening data to validated lead series—managed by a single computational project team, tracked in real time.

01 Data Ingestion & QC Week 1
02 Hit Triaging & Clustering Week 1–2
03 SAR Analysis & AI Expansion Weeks 2–3
04 Prioritization & Validation Weeks 3–4
05 Report & Handoff Week 4–5

01 Data Ingestion & QC

  • Receive HTS plate data or DEL FASTQ files.
  • Structure curation and QC metrics.
  • Z'-factor calculation, signal-to-noise assessment.

Deliverable: Curated dataset + QC report.

02 Hit Triaging & Clustering

  • Activity thresholding.
  • ML false-positive filtering.
  • Chemical clustering.

Deliverable: Ranked hit list with ML confidence scores.

03 SAR Analysis & AI Expansion

  • SAR matrix construction.
  • SER mapping.
  • AI generative expansion.

Deliverable: SAR deck + AI-expanded proposals.

04 Prioritization & Validation

  • Multi-parameter ranking.
  • Docking pose prediction.
  • Wet-lab validation plan.

Deliverable: Validation set with poses and experimental plan.

05 Report & Handoff

  • Tiered hit list, data package.
  • Handoff to Hit to Lead.
  • Transition plan.

Deliverable: Final report + data package + transition plan.

Sample Requirements

Requirement Details
HTS data Plate-level activity values; compound structures (SDF/SMILES); assay protocol.
DEL data Raw FASTQ/BAM or provider tables; library encoding scheme; synthesis chemistry.
Reference compounds Known actives for ML calibration and retrospective benchmarking.
Project scope Hit Identification, Fragment-to-Lead, or scaffold-hopping; target class and affinity range.
Prior data Any SPR/BLI/ITC or ADMET flags for constraint design.

Standard Deliverables

Frequently Asked Questions

Case Study

Case Study: Cross-DEL and Cross-ML Assessment for CK1α/δ Hit Discovery

Published Evidence:
Iqbal S, et al. Evaluation of DNA encoded library and machine learning model combinations for hit discovery. Nat Commun. 2025;16:3434.

Key Findings:

  • Three DNA-encoded libraries (MS10M, HG1B, DD11M) were screened against CK1α/δ. Chemical diversity, not library size, drove ML generalizability.
  • Five ML models were trained per DEL and tested on 140,000 blind compounds. ChemProp achieved 16% confirmed hit rate; HG1B-trained models delivered the best out-of-library predictions.
  • SPR validation of 808 compounds yielded 80 confirmed binders (10% hit rate) and 94% true-negative confirmation. Two nanomolar binders were discovered.

Industrial Translation:
The Broad Institute study demonstrates that DEL + ML extends hit discovery beyond the original library chemical space. For seed-stage biotechs, this eliminates costly off-DNA resynthesis by using ML-trained models to screen commercially available collections. For pharma teams, the cross-DEL ensemble approach maximizes hit diversity while minimizing false positives. Our MagHelix™ platform operationalizes this peer-reviewed paradigm within an audit-ready pipeline, pairing DEL deconvolution with ADMET Prediction & Modeling and Hit Biophysical Characterization to deliver chemistry-ready hit series.

Figure 1. Schematic of the DEL + ML workflow for hit identification. (Iqbal S, et al. 2025)

Reference

  1. Iqbal S, et al. Evaluation of DNA encoded library and machine learning model combinations for hit discovery. Nat Commun. 2025;16:3434.

Need AI-enhanced HTS/DEL data analysis to transform your screening campaign into validated leads? Our team can design a customized pipeline tailored to your library type, target class, and milestones. Contact our scientific team today.