Machine Learning-Based Bioactivity Prediction and Descriptor-Guided Rational Design of Amyloid-β Aggregation...

Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which amyloid-β (Aβ) aggregation plays a pivotal role in its onset and progression. Inhibiting Aβ aggregation is a promising therapeutic strategy; however, its intrinsically disordered and conformationally flexible nature hinders both conventional and computational inhibitor design. Moreover, experimental development of Aβ inhibitors, encompassing molecular design, synthesis, and biological evaluation through repeated assays, is a slow, labor-intensive, and resource-intensive process. Therefore, robust design guidelines and predictive tools are essential for accelerating the discovery of Aβ inhibitors. To overcome these limitations, we developed a machine-learning-based, user-friendly web platform, Amylo-IC₅₀Pred (https://amyloic50pred.vercel.app/), for rapid virtual screening of small molecules targeting Aβ aggregation. The platform integrates two classification models and one regression model, trained on 584 biologically validated compounds. For inhibitor-decoy discrimination, the Random Forest algorithm achieved perfect accuracy (100%). Potency classification into potent, moderately potent, and poor inhibitors was best achieved using Histogram-based Gradient Boosting (81% accuracy). The IC₅₀ regression model, also based on Random Forest, achieved a coefficient of determination (R²) of 0.93, demonstrating strong predictive performance. 2D and 3D key molecular properties such as hydrophobicity, shape and charge distribution, and molecular symmetry were identified as critical contributors to model performance. Importantly, these identified properties provide valuable insights into the molecular features that govern Aβ aggregation inhibition and can serve as a foundation for rational design of potent and selective Aβ aggregation inhibitors. Amylo-IC₅₀Pred thus represents a valuable resource for accelerating AD drug discovery.