PhytoExtractQSAR: An Automated Bioprocess Informatics Pipeline for Literature-Mined Prediction of Phytochemical Extraction Outcomes in Food Processing — Dataset Assembly, Machine Learning Benchmark, and the Plant Matrix Challenge in Cross-Compound Generalization

Abstract

<h2>Overview</h2> <strong>PhytoExtractQSAR</strong> is an end-to-end open-source bioprocess informatics pipeline for automated assembly and machine learning modelling of phytochemical extraction data from the food science literature. The pipeline integrates PubMed literature mining, full-text PDF extraction, molecular structure curation, physicochemical and bioprocess-domain feature engineering, principled feature selection, and multi-target QSPR modelling with rigorous multi-level cross-compound validation. This is <strong>Version 2</strong>, submitted alongside a manuscript revision for <em>Food and Bioprocess Technology</em> (Springer). It addresses all reviewer comments from the initial <em>Journal of Cheminformatics</em> submission and adds five new methodological capabilities. <hr /> <h2>Dataset</h2> <ul> <li><strong>1,877</strong> literature-verified extraction records with full data provenance tracking (<code>_data_source</code>, <code>_is_duplicate</code> flags)</li> <li><strong>94</strong> unique phytochemical compounds (flavonoids, phenolic acids, terpenes, alkaloids)</li> <li><strong>15</strong> extraction methods (UAE, maceration, Soxhlet, MAE, PLE, SFE, and others)</li> <li><strong>306</strong> unique source DOIs across 4 target variables</li> <li>Assembled from <strong>810 full-text PDFs</strong> and <strong>3,322 PubMed abstracts</strong></li> <li>Four target variables: Crude Yield (%), Total Phenolic Content (mg GAE/g), Total Flavonoid Content (mg QE/g), Antioxidant Activity (IC50, µg/mL)</li> <li>Plant part assignment automated for <strong>93.3%</strong> of records; solid-to-liquid ratio (SLR) present in 0.4% — identified as the primary missing engineering parameter</li> </ul> <hr /> <h2>Key Results (Version 2)</h2> <table> <thead> <tr> <th>Target</th> <th>Best model</th> <th>CV Q²</th> <th>Test Q²</th> <th>LOCO Q²</th> <th>DOI-stratified Q²</th> </tr> </thead> <tbody> <tr> <td>Yield (%)</td> <td>Extra Trees</td> <td>0.328</td> <td>−0.155</td> <td>−0.234</td> <td>−0.808</td> </tr> <tr> <td>TFC (mg QE/g)</td> <td>Extra Trees</td> <td>0.372</td> <td>0.030</td> <td>−0.055</td> <td>−1.003</td> </tr> <tr> <td>TPC (mg GAE/g)</td> <td>Random Forest</td> <td>0.159</td> <td>0.009</td> <td>+0.011</td> <td>−0.214</td> </tr> <tr> <td>IC50 (µg/mL)</td> <td>KNN</td> <td>0.109</td> <td>−0.054</td> <td>+0.015</td> <td>−76.2 †</td> </tr> </tbody> </table> <em>† IC50 DOI-stratified Q² driven by single extreme fold due to inter-laboratory DPPH/ABTS protocol variation.</em> <strong>Primary finding:</strong> Four independent validation strategies — compound-stratified external test set, leave-one-compound-out (LOCO) CV, multi-task joint modelling, and DOI-stratified CV — all produce Q² ≤ 0 for yield and TFC. This provides the first quantitative benchmark of the <em>plant matrix challenge</em>: molecular descriptors of a trace target compound cannot encode the cell wall porosity, tissue morphology, and solid-to-liquid ratio that govern bulk extraction yield in food-grade solid-liquid extraction. <hr /> <h2>New in Version 2</h2> <ol> <li><strong>Molecular structure curation</strong> — full RDKit MolStandardize pipeline (salt removal, charge neutralization, tautomer canonicalization); 7/28 SMILES modified</li> <li><strong>RFECV feature selection</strong> — Recursive Feature Elimination with Cross-Validation; selected 9–30 of 45 features per target</li> <li><strong>Compound-stratified external test set</strong> — 80/20 split before all modelling; preprocessor fitted on training partition only</li> <li><strong>Plant matrix proxy features</strong> — automated plant part assignment (leaf, root, bark, fruit, seed, flower, peel, aerial) with mass transfer efficiency and cell wall rigidity indices</li> <li><strong>SLR audit</strong> — systematic coverage check; 0.4% coverage identified as highest-priority data gap</li> <li><strong>Glycerol temperature-corrected properties</strong> — dielectric, viscosity (Segur-Oberstar), density at 25–80°C for clean-label extraction</li> <li><strong>Multi-task learning</strong> — MultiOutputRegressor and RegressorChain (ExtraTrees) for joint TPC/TFC/Yield modelling; Q² ≤ 0 under compound-grouped CV confirms gap is not addressable by model architecture</li> <li><strong>DOI-stratified cross-validation</strong> — GroupKFold by source study across 290–306 unique DOIs; Q² ≤ 0 for all targets confirms no inter-laboratory generalization</li> <li><strong>Williams plots</strong> — log-scale x-axis + residual histogram panel for applicability domain assessment</li> <li><strong>Figure 5</strong> — cross-compound generalization gap visualization (record-level vs GroupKFold vs LOCO Q²)</li> <li><strong>Table S1</strong> — best hyperparameter settings for all 10 models × 4 targets (40 configurations)</li> <li><strong>FABT manuscript generator</strong> — Times New Roman 10pt, combined Results and Discussion, APA 7 Name-Year citations, bioprocess engineering framing</li> </ol> <hr /> <h2>Repository Structure</h2> <pre> PhytoExtractQSAR/ ├── scripts/ │ ├── 01_verify_compounds.py SMILES validation, PubChem lookup │ ├── 02_mine_pubmed.py PubMed Entrez API (38 queries) │ ├── 03_mine_pdfs.py PDF full-text extraction (810 PDFs) │ ├── 05_quality_control.py Range filters, deduplication │ ├── 06_compute_descriptors.py RDKit descriptor computation │ ├── 10_feature_engineering.py 31 engineered bioprocess features │ ├── 11_revised_qsar_pipeline.py Main v4 QSAR pipeline (RFECV, LOCO) │ ├── 12_fabt_pipeline_updates.py Matrix proxies, SLR, glycerol, │ │ multi-task, DOI-stratified CV │ ├── generate_manuscript_figures_v2.py Revised figures (9 total) │ └── generate_manuscript_fabt.py FABT-compliant Word manuscript ├── data/ │ ├── processed/dataset_engineered.xlsx 1,877-record modelling dataset │ └── raw/ Original mined data ├── output/ │ ├── qsar_results_v4_*/ Tables 1–4, S1, SHAP, AD, models │ └── fabt_updates_*/ Matrix proxies, DOI-CV, multi-task ├── figures_v2/ Figures 1–5 and S1–S4 (PNG + PDF) ├── requirements.txt ├── CITATION.cff └── LICENSE (MIT) </pre> <hr /> <h2>Software Requirements</h2> Python 3.12 · scikit-learn 1.8 · XGBoost 3.2 · SHAP 0.50 · RDKit 2023.09 · Biopython 1.86 · pandas 3.0 · numpy · matplotlib 3.10 · reportlab 4.4 · python-docx 1.2 <pre>pip install -r requirements.txt</pre> <hr /> <h2>How to Reproduce</h2> <pre> # 1. Clone git clone https://github.com/sharhabileltahi r/PhytoExtractQSAR # 2. Install dependencies pip install -r requirements.txt # 3. Run main QSAR pipeline (produces Tables 1–4, S1, SHAP, Williams plots) python -X utf8 scripts/11_revised_qsar_pipeline.py # 4. Generate revised figures python -X utf8 scripts/generate_manuscript_figures_v2.py # 5. Run FABT pipeline updates (matrix proxies, multi-task, DOI-CV) python -X utf8 scripts/12_fabt_pipeline_updates.py # 6. Generate FABT manuscript (Word .docx) python -X utf8 scripts/generate_manuscript_fabt.py </pre> <hr /> <h2>Citation</h2> Eltahir, S. A. (2026). <em>PhytoExtractQSAR: Automated bioprocess informatics pipeline for literature-mined prediction of food phytochemical extraction outcomes</em> [Software and dataset, Version 2]. Zenodo. https://doi.org/10.5281/zenodo.18779393 Preprint: Eltahir, S. A. (2026). PhytoExtractQSAR: An automated pipeline for literature-mined modeling of phytochemical extraction outcomes with transparent automated pipeline for literature-mined modeling of phytochemical extraction outcomes with transparent generalization assessment. <em>Research Square</em>. https://doi.org/10.21203/rs.3.rs-8973111/v1 <hr /> <strong>License:</strong> MIT (code) · CC-BY 4.0 (dataset)<br/> <strong>AI declaration:</strong> Claude (Anthropic) assisted manuscript structuring and code organisation. All results, analyses, and conclusions are the author's own responsibility.