ArticleJournal of cheminformatics2026
Proteolysis-targeting Chimera efficacy prediction using a deep-learning-QSP model.
Article in Journal of cheminformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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7 authors.
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Abstract
This study presents an integrated computational modeling framework combining deep learning and Quantitative Systems Pharmacology (QSP) to predict the efficacy of PROTAC (PROteolysis Targeting Chimera) molecules. PROTACs have emerged as promising therapeutics for targeted protein degradation (TPD), offering significant advantages in addressing proteins that traditional small-molecule inhibitors cannot target. However, experimental evaluation of PROTAC efficacy is hindered by extensive variability in molecular configurations, necessitating efficient computational prediction methods. The proposed model integrates binding affinity predictions from DeepCalici, a convolutional neural network-based deep learning model, with a mechanistic QSP Hook model to estimate key pharmacodynamic parameters, notably half-maximal degradation concentration(
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