Hiring in life sciences? Share your open positions with our professional community. Read more Close

Advertisement

Geometry-Aware Molecular Representation Learning for Approximating NORMAN-Predicted Retention Time Indices in Nontargeted Screening.

Created on 09 Nov 2026

Authors

Hui Wang, Shifa Zhong, Bin Dong, Zuxin Xu

Published in

Journal of chemical information and modeling. Volume 66. Issue 21. Pages 13902-13912. Nov 09, 2026.

Abstract

Retention information provides chromatographic evidence complementary to mass spectrometric data in LC-HRMS nontargeted screening (NTS), yet many existing models rely mainly on 1D SMILES or 2D molecular graphs and make limited use of three-dimensional structure. Here, we developed ViSRTI (Vector-Interaction-Spatial Retention Time Index), a ViSNet-based geometry-aware framework that integrates 3D molecular representations with multimodal descriptors to model the precomputed positive- and negative-mode RTI labels in NORMAN SusDat. On fixed held-out test sets, ViSRTI-G outperformed all three baselines. Relative to the best-performing baseline for each metric, R2 increased by 0.022 (vs GNN-RT) and 0.025 (vs SMILES-BiLSTM), while MAE decreased by 5.39 and 6.56 RTI units (both vs SMILES-BiLSTM) in positive and negative modes, respectively. Incremental feature integration and SHAP-guided retraining identified ViSRTI-GP with KOC and LogP masked for positive mode and ViSRTI-GPT for negative mode, achieving R2 values of 0.9433 and 0.9258 and MAEs of 40.42 and 40.76 RTI units, respectively. Under Bemis-Murcko scaffold-disjoint 5-fold cross-validation, R2 remained at 0.9114 ± 0.0126 and 0.8937 ± 0.0117, indicating substantial agreement for unseen scaffold groups. Laboratory-specific RT-RTI calibration using 21 standards yielded R2 values of 0.8575 and 0.9525 in positive and negative modes. In a benzo[a]pyrene/benzo[e]pyrene case study, calibration-derived expected RTs differed from measured values by only 0.28 and 0.11 min, respectively, and reproduced the observed elution order. Overall, ViSRTI provides a geometry-aware surrogate for NORMAN-predicted RTI and enables laboratory-specific conversion of predicted RTI into complementary retention-time constraints for candidate evaluation in NTS.

PMID:
43001934
Bibliographic data and abstract were imported from PubMed on 09 Nov 2026.

Read full publication at:
Please sign in to see all details.

Advertisement

Stats

  • Community rating n/a 0 votes
  • Reviewers' rating n/a 0 votes
  • Your rating

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

  • Recommendations n/a n/a positive of 0 vote(s)
  • Views 9
  • Comments 0

Recommended by

  • No recommendations yet.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.

Advertisement