Comparative analysis of molecular fingerprints in prediction of drug combination effects. Issue 6 (17th August 2021)

• Record Type: Journal Article
• Authors: Zagidullin, B; Wang, Z; Guan, Y; Pitkänen, E; Tang, J
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 6(2021)
• Publication Date: 2021-08-17
• Subjects: drug combinations -- drug synergy -- molecular fingerprints -- machine learning -- precision medicine; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab291
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects. Graphical Abstract: