Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks

Wu, Kedi; Wei, Guo-Wei

doi:10.1021/acs.jcim.7b00558

Cited by 139 publications

(174 citation statements)

References 45 publications

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“…Overall, GBDT's performance is always better than that of RF, which agrees with early publication. 4 Among all the eight fingerprints we tested, Estate2, Estate1, Daylight, FP2, ECFP and MACCS usually work well on these four sets. Thus the consensus of these six fingerprints was also considered ("Top 6-cons" in Figure 3).…”

Section: Iiia1 the Performance Of Ensemble Methodsmentioning

confidence: 90%

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Are 2D fingerprints still valuable for drug discovery?

Gao

Nguyen

Sresht

et al. 2020

Phys. Chem. Chem. Phys.

Self Cite

101

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Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein-ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprintbased methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein-ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein-ligand binding affinity predictions. * Corresponding to Guo-Wei Wei.Molecular fingerprints are one way of encoding the structural features of a molecule. They play a fundamental role in QSAR/QSPR analysis, virtual screening, similarity-based compound search, target molecule ranking, drug ADMET prediction, and other drug discovery processes. Molecular fingerprints are property profiles of a molecule, usually in the form of vectors with each vector element indicating the existence, the degree or the frequency of one particular structure feature. [12][13][14] Various fingerprints have been developed for molecular feature encoding in the past few decades. [15][16][17] Most fingerprints are 2D fingerprints which can be extracted from molecular connection tables without 3D structure information. However, high dimensional fingerprints have also been developed to utilize 3D molecular structure and other information. 18 There are four main categories of 2D fingerprints, namely substructure key-based fingerprints, topological or path-based fingerprints, circular fingerprints, and pharmacophore fingerprints. Substructure key-based fingerprints are bit strings representing the presence of certain substructures or fragments from a given list of structural keys in the compound. Molecular access system (MACCS) 19 is one of the most popular substructure keybased fingerprint methods. Topological or path-based fingerprints are based on analyzing all the fragments of a molecule following a (usually linear) path up to a certain number of bonds, and then hashing every one of these paths to create one fingerprint. The most prominent ones in this category are FP2, 20 Daylight 21 and electrotopological sta...

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Section: Iiia1 the Performance Of Ensemble Methodsmentioning

confidence: 90%

“…Our results show that Estate2 is the best single fingerprint with an R 2 of 0.742, and the consensus of the top 6 fingerprints leads to an R 2 of 0.785. 4 Thus, there is a need for multitask deep learning when dealing with such a small dataset.…”

Section: Iiia1 the Performance Of Ensemble Methodsmentioning

confidence: 99%

Are 2D fingerprints still valuable for drug discovery?

Gao

Nguyen

Sresht

et al. 2020

Phys. Chem. Chem. Phys.

Self Cite

101

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“…We call this approach to small molecules multi-level persistent homology of level n where we set the distance between two atoms to infinity if the shortest path between them through the covalent bond network is at most of the length n. This treatment has led to powerful predictive tools in tasks only explicitly involving small molecules. 21,153…”

Section: Iia4 Multi-level Persistent Homologymentioning

confidence: 99%

“…Ensemble methods, such as RF, GBDT, and ET, are relatively accurate and efficient. 21,102,153,154 In particular, RF should be the method of choice for a new problem due to its fewer parameters and robustness. Due to its accuracy and robustness, RF method is often used to rank the feature importance.…”

Section: Iid Machine Learning Algorithmsmentioning

confidence: 99%

A review of mathematical representations of biomolecular data

Nguyen

Cang

Wei

2020

Phys. Chem. Chem. Phys.

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Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on the protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energy, toxicity, partition coefficient, protein folding stability changes upon mutation, etc.

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“…In addition, Wu et al recently improved traditional molecular descriptors using element specific persistent homology (ESPH) and auxiliary descriptors, where ESPH includes topological information from intermolecular interactions and homology analysis on each component of molecules. On this basis, they performed RF, Gradient Boosting Decision Tree, single-task deep learning, multi-task deep learning, multi-task deep learning methods, and achieved the highest degree of fitness and accuracy [118]. …”

Section: Chemical Structure Based Toxicity Prediction By Machine Lmentioning

confidence: 99%

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Wang

2018

IJMS

149

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Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug’s preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.

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Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks

Cited by 139 publications

References 45 publications

Are 2D fingerprints still valuable for drug discovery?

Are 2D fingerprints still valuable for drug discovery?

A review of mathematical representations of biomolecular data

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

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