化学信息学
化学空间
化学
杠杆(统计)
随机森林
人工智能
机器学习
图形
深度学习
代表(政治)
药物发现
计算机科学
理论计算机科学
计算化学
政治
生物化学
法学
政治学
作者
Evan N. Feinberg,Elizabeth Joshi,Vijay S. Pande,Alan C. Cheng
标识
DOI:10.1021/acs.jmedchem.9b02187
摘要
The absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of drug candidates are important for their efficacy and safety as therapeutics. Predicting ADMET properties has therefore been of great interest to the computational chemistry and medicinal chemistry communities in recent decades. Traditional cheminformatics approaches, using learners such as random forests and deep neural networks, leverage fingerprint feature representations of molecules. Here, we learn the features most relevant to each chemical task at hand by representing each molecule explicitly as a graph. By applying graph convolutions to this explicit molecular representation, we achieve, to our knowledge, unprecedented accuracy in prediction of ADMET properties. By challenging our methodology with rigorous cross-validation procedures and prognostic analyses, we show that deep featurization better enables molecular predictors to not only interpolate but also extrapolate to new regions of chemical space.
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