MOTIVATION: Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed. RESULTS: Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in GitHub at https://github.com/labnams/IC50evaluation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Protein structure refinement is a necessary step for the study of protein function. In particular, some nuclear magnetic resonance (NMR) structures are of lower quality than X-ray crystallographic structures. Here, we present NMRe, a web-based server for NMR structure refinement. The previously developed knowledge-based energy function STAP (Statistical Torsion Angle Potential) was used for NMRe refinement. With STAP, NMRe provides two refinement protocols using two types of distance restraints. If a user provides NOE (Nuclear Overhauser Effect) data, the refinement is performed with the NOE distance restraints as a conventional NMR structure refinement. Additionally, NMRe generates NOE-like distance restraints based on the inter-hydrogen distances derived from the input structure. The efficiency of NMRe refinement was validated on 20 NMR structures. Most of the quality assessment scores of the refined NMR structures were better than those of the original structures. The refinement results are provided as a three-dimensional structure view, a secondary structure scheme, and numerical and graphical structure validation scores. AVAILABILITY AND IMPLEMENTATION: NMRe is available at http://psb.kobic.re.kr/nmre/.