AI Driven Drug Discovery Pipeline for Lead Optimization: from Virtual Screening to Preclinical Validation of Brain-Penetrant AD Therapeutics

Summary In this proposed research, we aim to develop AI-driven virtual screening pipelines to screen billion-scale compound libraries and predict blood-brain barrier (BBB) permeability, which are critical steps for identifying drug-like lead compounds against AD/ADRD targets, as the current computational methods do not adequately address these crucial needs. First, we will incorporate small molecule foundation models, Bayesian ensemble learning, and active learning to accelerate the docking-based virtual screening pipeline, enabling efficient virtual screening of billion-scale compound libraries. Second, we will incorporate Affinity Selection Mass Spectrometry (ASMS)-based screening and advanced AI/Computational tools to enable similarity-based virtual screening (SBVS) for AD/ADRD targets when their ligand-bound structures are not known. Finally, we will construct a large- scale ΔGSolv dataset and take advantage of the dataset to develop AI/ML models that predict Kp,uu and Pgp ER, providing a more nuanced understanding of brain penetration for small-molecule brain penetrant drugs. Project Number: 1R01AG098161-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute on Aging (NIA) | Principal Investigator: Yijie Wang (+3 co-PIs) | Institution: TRUSTEES OF INDIANA UNIVERSITY, BLOOMINGTON, IN | Award Amount: $1,232,416 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 NV-L (52)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11293986