ArticleJournal of cheminformatics2018
Evaluating parameters for ligand-based modeling with random forest on sparse data sets.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.In silico pharmacology · 2025Article
- Deciphering Molecular Embeddings with Centered Kernel Alignment.Journal of chemical information and modeling · 2024Article
- A novel interpretable machine learning model approach for the prediction of TiOScientific reports · 2024Article
- Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network.Molecular informatics · 2022Article
- A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.Foods (Basel, Switzerland) · 2022Article
- Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning.Journal of cheminformatics · 2021Article
- Assessing the calibration in toxicological in vitro models with conformal prediction.Journal of cheminformatics · 2021Article
- Graph-based machine learning interprets and predicts diagnostic isomer-selective ion-molecule reactions in tandem mass spectrometry.Chemical science · 2020Article
- SYBA: Bayesian estimation of synthetic accessibility of organic compounds.Journal of cheminformatics · 2020Article
- Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.International journal of molecular sciences · 2019Article
- DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance.Frontiers in bioengineering and biotechnology · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementation. Our results showed that unhashed fingerprints yield significantly better accuracy than hashed fingerprints ([Formula: see text]), with no pronounced deterioration in modeling time and memory usage. Furthermore, the fast execution and low memory usage of the FEST algorithm suggest that it is a good alternative for large, high dimensional sparse data. Both support vector machines and random forest performed equally well but results indicate that the support vector machine was better at using the extra information from larger values of the Morgan fingerprint's radius.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.