Machine learning and ligand binding predictions: A review of data, methods, and obstacles

Publication date: Available online 10 February 2020Source: Biochimica et Biophysica Acta (BBA) - General SubjectsAuthor(s): Sally R. Ellingson, Brian Davis, Jonathan AllenAbstractComputational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.
Source: Biochimica et Biophysica Acta (BBA) General Subjects - Category: Biochemistry Source Type: research