ArticleBriefings in bioinformatics2022
Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.Molecular informatics · 2025Review
- Article
- Anti-cancer activity elucidation of geissolosimine as an MDM2-p53 interaction inhibitor: An in-silico study.PloS one · 2025Article
- Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Frontiers in pharmacology · 2025Article
- Transparent Machine Learning Model to Understand Drug Permeability through the Blood-Brain Barrier.Journal of chemical information and modeling · 2024Article
- Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches.International journal of molecular sciences · 2024Review
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.Protein science : a publication of the Protein Society · 2024Article
- Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules.Molecules (Basel, Switzerland) · 2024Review
- Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification.Beilstein journal of nanotechnology · 2024Article
- The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.Journal of computer-aided molecular design · 2023Article
- A data-driven machine learning approach for discovering potent LasR inhibitors.Bioengineered · 2023Article
- Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases.Nanoscale advances · 2023Review
- The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.Research square · 2023Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Review
- Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction.International journal of molecular sciences · 2023Review
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The trade-off between a machine learning (ML) and deep learning (DL) model's predictability and its interpretability has been a rising concern in central nervous system-related quantitative structure-activity relationship (CNS-QSAR) analysis. Many state-of-the-art predictive modeling failed to provide structural insights due to their black box-like nature. Lack of interpretability and further to provide easy simple rules would be challenging for CNS-QSAR models. To address these issues, we develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power. A data set of 940 market drugs (315 CNS-active, 625 CNS-inactive) with support vector machine and graph convolutional network algorithms were used. Individual ML/DL modeling methods were also constructed for comparison. The performance of these models was evaluated using an additional external dataset of 117 market drugs (42 CNS-active, 75 CNS-inactive). Fingerprint-split validation was adopted to ensure model stringency and generalizability. The resulting novel hybrid ensemble model outperformed other constituent traditional QSAR models with an accuracy of 0.96 and an F1 score of 0.95. With the power of the interpretability provided with this protocol, our model laid down a set of simple physicochemical rules to determine whether a compound can be a CNS drug using six sub-structural features. These rules displayed higher classification ability than classical guidelines, with higher specificity and more mechanistic insights than just for blood-brain barrier permeability. This hybrid protocol can potentially be used for other drug property predictions.
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