TY - JOUR AU - Pu, Limeng AU - Naderi, Misagh AU - Liu, Tairan AU - Wu, Hsiao-Chun AU - Mukhopadhyay, Supratik AU - Brylinski, Michal PY - 2019 DA - 2019/01/08 TI - eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates JO - BMC Pharmacology and Toxicology SP - 2 VL - 20 IS - 1 AB - The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery. SN - 2050-6511 UR - https://doi.org/10.1186/s40360-018-0282-6 DO - 10.1186/s40360-018-0282-6 ID - Pu2019 ER -