An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction

Ye, Zhuyifan; Yang, Yu-Bin; Li, Xiaoshan; Cao, Dong‐Sheng; Ouyang, Defang

doi:10.1021/acs.molpharmaceut.8b00816

Cited by 83 publications

(59 citation statements)

References 74 publications

(114 reference statements)

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“…The multitask (MT) learning technique has achieved much success in qualitative Merck and Tox21 prediction challenges. [45][46][47][48] In the MT framework, multiple tasks share the same hidden layers. However, the output layer is attached to different tasks.…”

Section: Iid Multitask Deep Neural Network (Mt-dnn)mentioning

confidence: 99%

Are 2D fingerprints still valuable for drug discovery?

Gao

Nguyen

Sresht

et al. 2020

Phys. Chem. Chem. Phys.

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: Iid Multitask Deep Neural Network (Mt-dnn)mentioning

confidence: 99%

Are 2D fingerprints still valuable for drug discovery?

Gao

Nguyen

Sresht

et al. 2020

Phys. Chem. Chem. Phys.

101

View full text Add to dashboard Cite

show abstract

“…More recently, there have been promising applications of AI methods to ADMET prediction. For example, the recent use of transfer and multitask learning in predicting pharmacokinetic parameters [38] or the application of DL to ADMET prediction [39]. Moreover, public-private partnerships such as the eTOX consortium have been created to recover legacy toxicological data from big pharmaceutical companies and develop better predictive models [40].…”

Section: Admet Modellingmentioning

confidence: 99%

Transforming cancer drug discovery with Big Data and AI

Workman

Antolín

Al‐Lazikani

2019

Expert Opinion on Drug Discovery

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“…Zhuyifan et al developed an integrated transfer learning and multitask learning approach to predict pharmacokinetic parameters for small molecule drugs on the market. 13 Compared with other machine learning approaches, Yilong et al demonstrated that deep learning methods can achieve the highest accuracy to predict the in vitro performances of pharmaceutical formulations. 14 Run et al established a prediction model for the disintegration time of oral disintegrating tablets, and the model achieved 80% accuracy.…”

Section: Introductionmentioning

confidence: 99%