BENCHMARKING SMALL-DATASET STRUCTURE-ACTIVITY-RELATIONSHIP MODELS FOR PREDICTION OF WNT SIGNALING INHIBITION

• Main Author: Kokabi, Mahtab
• Format: Others
• Published: ScholarWorks@UMass Amherst 2021
• Subjects: Bioactivity prediction; drug discovery; machine learning; molecular fingerprint; quantitative structure-activity relationship; Wnt signaling; Biomedical; Computational Engineering; Computer Engineering; Electrical and Computer Engineering
• Online Access: https://scholarworks.umass.edu/masters_theses_2/1139; https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2153&context=masters_theses_2

Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.