ArticlePloS one2021
Evaluating Deep Learning models for predicting ALK-5 inhibition.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Identification of YES1 Inhibitors From Approved Drugs Using a Multimodal Deep Learning Framework.Molecular informatics · 2026Article
- From Descriptor Learning to Binding Stability: An Explainable Machine Learning Pipeline for EGFR Double-Mutant Inhibitor Discovery.International journal of molecular sciences · 2026Article
- A Data-Driven Approach for Interpretable and Efficient Predictive Modeling: A Case Study in SARS-CoV-2 Protease Inhibitor Discovery Through Feature Selection.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.Molecular diversity · 2025Article
- Discovery of SARS-CoV-2 Nsp14-Methyltransferase (MTase) Inhibitors by Harnessing Scaffold-Centric Exploration of the Ultra Large Chemical Space.ACS pharmacology & translational science · 2025Article
- ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.Molecular informatics · 2024Article
- Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning.Analytical and bioanalytical chemistry · 2024Article
- Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies.Scientific reports · 2022Article
- Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.Molecular diversity · 2022Article
- Article
- Multifaceted targeting strategies in cancer against the human notch 3 protein: a computational study.In silico pharmacology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the performance of Deep Learning models in comparison to Random Forest, and Support Vector Regression for predicting the biological activity (pIC50) of ALK-5 inhibitors as candidates to treat cancer. The generalization power of the models was assessed by internal and external validation procedures. A deep neural network model obtained the best performance in this comparative study, achieving a coefficient of determination of 0.658 on the external validation set with mean square error and mean absolute error of 0.373 and 0.450, respectively. Additionally, the relevance of the chemical descriptors for the prediction of biological activity was estimated using Permutation Importance. We can conclude that the forecast model obtained by the deep neural network is suitable for the problem and can be employed to predict the biological activity of new ALK-5 inhibitors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.