solidwaste-web/AR-SSRF-Framework-Main-Pipeline-for-molecular-property-prediction: Release for Manuscript Review

Zenodo (CERN European Organization for Nuclear Research) 2026 Open access

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Authors: solidwaste-web
DOI: 10.5281/zenodo.18950483
Cited by: 0

Abstract

This repository contains the full implementation of the Autoregressive Semi-Supervised Random Forest (AR-SSRF) framework for molecular property prediction. Contents Data preprocessing Molecular feature extraction Feature engineering Base classifier evaluation AR-SSRF semi-supervised learning Requirements Python 3.8+ See requirements.txt

Fields of Study & Subjects

Computational Drug Discovery Methods Machine Learning in Materials Science Advanced Chemical Physics Studies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
11 March 2026
Language
EN