MoleculeNet: a benchmark for molecular machine learning.
MoleculeNet: a benchmark for molecular machine learning.
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DOI：
10.1039/c7sc02664a
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发表时间：
2018-01-14
期刊：
影响因子：
8.4
通讯作者：
Pande V
中科院分区：
文献类型：
--
作者：
Wu Z;Ramsundar B;Feinberg EN;Gomes J;Geniesse C;Pappu AS;Leswing K;Pande V
分子机器学习的大规模基准测试，包括多个公共数据集，指标，特征化和学习算法。分子机器学习在过去几年中迅速成熟。改进的方法和更大数据集的存在使机器学习算法能够对分子特性做出越来越准确的预测。然而，由于缺乏一个标准的基准来比较所提出的方法的有效性，算法的进展受到了限制;大多数新算法都是在不同的数据集上进行基准测试的，这使得衡量所提出的方法的质量具有挑战性。这项工作介绍了MoleculeNet，一个大规模的分子机器学习基准。MoleculeNet管理多个公共数据集，建立评估指标，并提供多个先前提出的分子特征化和学习算法的高质量开源实现（作为DeepChem开源库的一部分发布）。MoleculeNet基准测试表明，可学习的表示是分子机器学习的强大工具，并广泛提供最佳性能。然而，这一结果带来了警告。可学习表示仍然难以在数据稀缺和高度不平衡的分类下处理复杂任务。对于量子力学和生物物理数据集，使用物理感知的特征化可能比选择特定的学习算法更重要。
A large scale benchmark for molecular machine learning consisting of multiple public datasets, metrics, featurizations and learning algorithms. Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.
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影响因子：
5.6
作者：
Durrant JD;McCammon JA
通讯作者：
McCammon JA
影响因子：
6.1
作者：
Gražulis S;Chateigner D;Downs RT;Yokochi AF;Quirós M;Lutterotti L;Manakova E;Butkus J;Moeck P;Le Bail A
通讯作者：
Le Bail A
影响因子：
8.6
作者：
Behler, Joerg;Parrinello, Michele
通讯作者：
Parrinello, Michele
DOI：
10.1021/ci940128y
发表时间：
1997-07-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Baskin, II;Palyulin, VA;Zefirov, NS
通讯作者：
Zefirov, NS
影响因子：
4.5
作者：
Friedman, JH
通讯作者：
Friedman, JH