2026-10-12T17:14:04Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34050872 2026-10-02T00:06:22Z category_1 category_4 category_19 category_873 category_734 category_931 category_134 portal_63 item_type_3 month_year_10_2026

Predicting Chemical Stressor Effects on Conjugative Plasmid Transfer Rates Using Machine Learning Ana Rey-Sogo (25160093) David Kneis (7848118) Alan Xavier Elena (23024040) Uli Klümper (21454330) Itziar Alkorta (2283460) Biophysics Biochemistry Pharmacology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Virology xgb ), achieving total predictive weight shap analysis revealed rp4 plasmid transfer quaternary ammonium compounds including physicochemical properties functional use categories containing compounds associated conjugative plasmid transfer aware test set antibiotic resistance dissemination shape descriptors ). anticipates chemical contributions extracting compound identity 2 </ sup morgan fingerprints account morgan fingerprints >< sup vitro </ q </ molecular descriptors extended descriptors chemical stressors chemical modulation work highlights scalable framework risk assessment results demonstrate ochem platform machine learning maccs keys experimental metadata exhaustive evaluation dragon 7 curated data clinical settings 319 within Chemical stressors in environmental and clinical settings can alter conjugative plasmid transfer, which is a key process driving antibiotic resistance dissemination. However, predicting these effects across chemically diverse compounds is challenging. Here, we compiled and curated data on the effect of chemical stressors on RP4 plasmid transfer, extracting compound identity, concentration, conjugation frequency, and experimental metadata from 73 <i>in vitro</i> studies. Chemicals were characterized by functional use categories and molecular descriptors, including physicochemical properties, MACCS keys, Morgan fingerprints, and extended descriptors from the OCHEM platform (AlvaDesc, Dragon 7, and 3D-shape descriptors). An exhaustive evaluation of 1022 feature set combinations across 3066 models showed that chemical concentration alone provides no meaningful predictive signal (<i>Q</i>²ext <0), whereas integrating Morgan fingerprints substantially improves predictive performance. The best-performing model combined concentration, Morgan fingerprints, and experimental conditions using extreme gradient boosting (XGB), achieving <i>Q</i>²ext = 0.373 on a held-out compound-aware test set and a median <i>Q</i>²ext of 0.319 within the defined applicability domain across 20 independent splits. SHAP analysis revealed that Morgan fingerprints account for 69% of total predictive weight, with quaternary ammonium compounds and long-chain alkyl structures associated with inhibition of conjugation and sulfonamide and amine-containing compounds associated with promotion. These results demonstrate that chemical structural features provide critical information for predicting the chemical modulation of conjugative plasmid transfer. This work highlights that combining chemically informed descriptors with machine learning (ML) anticipates chemical contributions to antibiotic resistance dissemination, providing a scalable framework for the prioritization and risk assessment of chemicals. 2026-10-01T00:00:00Z Dataset Dataset 10.1021/acs.est.6c02828.s002 https://figshare.com/articles/dataset/Predicting_Chemical_Stressor_Effects_on_Conjugative_Plasmid_Transfer_Rates_Using_Machine_Learning/34050872 CC BY-NC 4.0