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- W4249021602 abstract "Abstract Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies[1-3]. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies including Roche, routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies.Hereby we present a machine learning framework aiming at the prediction of our in-house 50 off-target panel[4] activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and accelerate drug discovery. It incorporates different ML approaches such as deep learning and automated machine learning. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed." @default.
- W4249021602 created "2022-05-12" @default.
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- W4249021602 date "2021-10-11" @default.
- W4249021602 modified "2023-09-27" @default.
- W4249021602 title "Machine Learning Tools For off-Target Early Safety Assessment of Small Molecules In Drug Discovery (Single Task Neural Networks Vs Automated Machine Learning)" @default.
- W4249021602 doi "https://doi.org/10.21203/rs.3.rs-957525/v1" @default.
- W4249021602 hasPublicationYear "2021" @default.
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