ArticleNature communications2024
Application of machine learning models for property prediction to targeted protein degraders.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi-scenario applications, and translational prospects.Smart molecules : open access · 2026Review
- A multi-fidelity tabular prior-data fitted network model for accurate prediction and uncertainty quantification.Nature communications · 2026Article
- Machine learning-driven discovery of therapeutic nucleoside hydrogels for periodontitis.International journal of oral science · 2026Article
- Integrating AI into next-generation PROTAC Engineering: a comprehensive toolkit for rational PROTAC design.Molecular diversity · 2026Review
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- SiCmiR Atlas: Single-Cell miRNA Landscape Reveals Hub-miRNA and Network Signatures in Human Cancers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- PROTAC-Splitter: a machine learning framework for automated identification of PROTAC substructures.Journal of cheminformatics · 2026Article
- A machine learning-based pharmacokinetics predictor (EGFR-PROPK) for EGFR-targeting PROTACs.Communications chemistry · 2026Article
- Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.Journal of chemical information and modeling · 2026Article
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Design, Synthesis, Bioactivity, and Docking Studies of Isatin-Hydrazone Derivatives as Potential CDK-2 Inhibitors.Anti-cancer agents in medicinal chemistry · 2026Article
- Exploring Novel E3 Ligases and Neosubstrates for Molecular Glue Degraders and Therapeutic Applications in Cancer.Oncology research · 2026Review
- Advancing PROTAC Discovery Through Artificial Intelligence: Opportunities, Challenges, and Future Directions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Prediction of Permeability and Efflux Using Multitask Learning.ACS omega · 2025Article
- Targeted protein degradation for cancer therapy.Nature reviews. Cancer · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs' property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs' design, to potentially accelerate drug discovery.
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