ArticleBiomedicines2022
PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 36 citations in OpenAlex.
- Design of novel PI3Kα and PI3Kγ inhibitors for cancer treatment using pharmacophore, protein-ligand contacts, and machine learning methods.Journal of computer-aided molecular design · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Tracking protein kinase targeting advances: integrating QSAR into machine learning for kinase-targeted drug discovery.Future science OA · 2025Review
- Ensemble techniques for predictive modeling of leishmanial activity via molecular fingerprints.BMC medical informatics and decision making · 2025Article
- General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.Molecular diversity · 2025Article
- Integrated AI and machine learning pipeline identifies novel WEE1 kinase inhibitors for targeted cancer therapy.Molecular diversity · 2025Article
- Fused pyrido[3,4-D]pyrimidine moiety with phthalazinone ring accelerate dual inhibition of PARP1 and CDK4 in triple-negative breast cancer: a hybrid design with computational investigation through molecular modeling and quantum mechanics.Journal of molecular modeling · 2025Article
- Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research.Current issues in molecular biology · 2025Review
- In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors AgainstPharmaceuticals (Basel, Switzerland) · 2025Article
- Perturbation-theory machine learning for mood disorders: virtual design of dual inhibitors of NET and SERT proteins.BMC chemistry · 2025Article
- Ligand-Based Approach for Multi-Target Drug Discovery: PTML Modeling of Triple-Target Inhibitors.Current topics in medicinal chemistry · 2025Article
- AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Article
- MVGNet: Prediction of PI3K Inhibitors Using Multitask Learning and Multiview Frameworks.ACS omega · 2024Article
- Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer.ACS omega · 2024Article
- Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning.ACS omega · 2024Article
- VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search.Journal of chemical information and modeling · 2023Article
- Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles.ACS omega · 2022Article
- Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?International journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.
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.