Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure

By Andreas Bender, J. Scheiber, Meir Glick, J. Davies, Kamal Azzaoui, J. Hamon, L. Urban, S. Whitebread, J. Jenkins. ChemMedChem, 2007

306 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/f4c600c44137c08094231d628d9c0be55b01ab6c

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In Computational Theory and Mathematics

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In Pharmacology

Citations in the corpus

Linking Biochemical Pathways and Networks to Adverse Drug Reactions (2014, background): “[5] explored the chemical space and made the first attempt to predict ADRs across hundreds of ADR categories from chemical structure alone, achieving 92% classification accuracy.”
A side effect resource to capture phenotypic effects of drugs (2010, background): “…of research in the pharmaceutical industry, which is interested in predicting the possible side effects of drug candidates based on, for example, the binding ﬁngerprint, chemical structure and other properties of the drug candidate (Krejsa et al , 2003; Bender et al , 2007; Fliri et al , 2007).”
The influence of drug-like concepts on decision-making in medicinal chemistry (2007, background): “It is time the medicinal chemistry community used its undoubted creative ability to better control physical properties, and to tackle the threat of compound-related toxicological attritio”
The influence of lipophilicity in drug discovery and design (2012, background): “If lipophilicity is too high, there is an increased likelihood of in vitro receptor promiscuity [8-11,15-17] and in vivo toxicity [13,18,19], as well as poor solubility and metabolic clearance.”
Analysis of chemical and biological features yields mechanistic insights into drug side effects. (2013, background): “Recently, such structural alerts were mined in a collection of SEs (Bender et al., 2007) and used in a follow-up study to build a chemical map of adverse events that showed a systemic organization (Scheiber et al., 2009), similar to the pharmacological organization that targets show when described…”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, methodology): “Over the last decade we and others 15–17, 58– 61 have increasingly The main aim of this study was to evaluate whether deep learning offered any improvement in testing when assessed using an array of metrics over other computational methods for drug discovery and ADME/Tox datasets.”