Prediction of off-target drug effects through data fusion
| Autor: | Ajay N. Jain, Emmanuel R. Yera, Ann E. Cleves |
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| Rok vydání: | 2014 |
| Předmět: |
Patient Package Inserts
Data Interpretation Drug-Related Side Effects and Adverse Reactions Databases Pharmaceutical Computer science Molecular Conformation computer.software_genre Article Set (abstract data type) Databases Similarity (network science) Models Humans Relevance (information retrieval) Molecular Targeted Therapy Cancer Models Statistical Modality (human–computer interaction) Surflex-Sim Off-Target Predictions Probabilistic logic Drug Repositioning Computational Biology Statistical chEMBL Sensor fusion Networking and Information Technology R&D Networking and Information Technology R&D (NITRD) Pharmaceutical Preparations Molecular similarity Data Interpretation Statistical Target drug Pharmaceutical Data mining computer |
| Zdroj: | Yera, ER; Cleves, AE; & Jain, AN. (2014). Prediction of off-target drug effects through data fusion. 19th Pacific Symposium on Biocomputing, PSB 2014, 160-171. UC San Francisco: Retrieved from: http://www.escholarship.org/uc/item/8bn506kn Pacific Symposium on Biocomputing |
| Popis: | We present a probabilistic data fusion framework that combines multiple computational approaches for drawing relationships between drugs and targets. The approach has special relevance to identifying surprising unintended biological targets of drugs. Comparisons between molecules are made based on 2D topological structural considerations, based on 3D surface characteristics, and based on English descriptions of clinical effects. Similarity computations within each modality were transformed into probability scores. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, clinical effects similarity or their combination. The methods were validated within a curated structural pharmacology database (SPDB) and further tested by blind application to data derived from the ChEMBL database. For prediction of off-target effects, 3D-similarity performed best as a single modality, but combining all methods produced performance gains. Striking examples of structurally surprising off-target predictions are presented. |
| Databáze: | OpenAIRE |
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