Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening.

ABSTRACT: Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD₅₀), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818

SUBMITTER: Mamada H 

PROVIDER: S-EPMC10568689 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature