title: Large-scale learning of structure-activity relationships using a linear support vector machine and problem-specific metrics.
abstract: The goal of this study was to adapt a recently proposed linear large-scale support vector machine to large-scale binary cheminformatics classification problems and to assess its performance on various benchmarks using virtual screening performance measures. We extended the large-scale linear support vector machine library LIBLINEAR with state-of-the-art virtual high-throughput screening metrics to train classifiers on whole large and unbalanced data sets. The formulation of this linear support machine has an excellent performance if applied to high-dimensional sparse feature vectors. An additional advantage is the average linear complexity in the number of non-zero features of a prediction. Nevertheless, the approach assumes that a problem is linearly separable. Therefore, we conducted an extensive benchmarking to evaluate the performance on large-scale problems up to a size of 175000 samples. To examine the virtual screening performance, we determined the chemotype clusters using Feature Trees and integrated this information to compute weighted AUC-based performance measures and a leave-cluster-out cross-validation. We also considered the BEDROC score, a metric that was suggested to tackle the early enrichment problem. The performance on each problem was evaluated by a nested cross-validation and a nested leave-cluster-out cross-validation. We compared LIBLINEAR against a Naïve Bayes classifier, a random decision forest classifier, and a maximum similarity ranking approach. These reference approaches were outperformed in a direct comparison by LIBLINEAR. A comparison to literature results showed that the LIBLINEAR performance is competitive but without achieving results as good as the top-ranked nonlinear machines on these benchmarks. However, considering the overall convincing performance and computation time of the large-scale support vector machine, the approach provides an excellent alternative to established large-scale classification approaches.
authors.affiliation: Center for Bioinformatics (ZBIT), University of Tübingen, Tübingen, Germany. georg.hinselmann@uni-tuebingen.de
authors.forename: Georg
authors.initials: G
authors.lastname: Hinselmann
authors.forename: Lars
authors.initials: L
authors.lastname: Rosenbaum
authors.forename: Andreas
authors.initials: A
authors.lastname: Jahn
authors.forename: Nikolas
authors.initials: N
authors.lastname: Fechner
authors.forename: Claude
authors.initials: C
authors.lastname: Ostermann
authors.forename: Andreas
authors.initials: A
authors.lastname: Zell
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008958:Models, Molecular; D008968:Molecular Conformation; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship; D013997:Time Factors; D014584:User-Computer Interface