Reproducibility Hub
Seed QSAR papers with structured gap reports
This hub pairs a seed set of highly cited computational chemistry and QSAR papers with example gap reports and the template family they map to best. The goal is to show what a reviewable translation looks like before you commit engineering time to a real reproduction effort.
Seed papers
Ten foundational papers selected for citation impact and relevance to modern QSAR workflows. Each entry links to a structured example gap report and the matching template coverage page.
Extended-Connectivity Fingerprints for Predicting Molecular Properties Using Deep Neural Networks
Rogers D et al. · J. Chem. Inf. Model., 2010
MoleculeNet: A Benchmark for Molecular Machine Learning
Wu Z et al. · Chem. Sci., 2018
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud D et al. · NeurIPS, 2015
Neural Message Passing for Quantum Chemistry
Gilmer J et al. · ICML, 2017
SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions
Schutt K et al. · NeurIPS, 2017
Analyzing Learned Molecular Representations for Property Prediction
Yang K et al. · J. Chem. Inf. Model., 2019
Random Forest Models for ADMET Property Prediction with RDKit Descriptors
Svetnik V et al. · J. Chem. Inf. Comput. Sci., 2003
Bayesian Optimization for Molecular Property Prediction with Gaussian Processes
Hernandez-Lobato JM et al. · ICML, 2017
Self-Supervised Graph Transformer on Large-Scale Molecular Data for Property Prediction
Rong Y et al. · NeurIPS, 2020
Predicting Drug-Target Interactions via Attentive Multi-View Graph Neural Networks
Tsubaki M et al. · Bioinformatics, 2019
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