Accurate Physical Property Predictions via Deep Learning

Hou, Yuanyuan; Wang, Shiyu; Bai, Bing; Chan, H.C.; Yuan, Shuguang

doi:10.3390/molecules27051668

Cited by 16 publications

(10 citation statements)

References 47 publications

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“…Moreover, the training data sets were augmented 10 times by SMILES enumeration . The augmentation of SMILES during the training phase effectively enriches the sample size and improves the predictive performances of the machine learning models. , However, both the validation and test sets retained their SMILES.…”

Section: Resultsmentioning

confidence: 99%

Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ

Cheng,

Hwang,

Bhave

et al. 2023

J. Chem. Inf. Model.

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Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug−drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure−activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.

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Section: Resultsmentioning

confidence: 99%

Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ

Cheng,

Hwang,

Bhave

et al. 2023

J. Chem. Inf. Model.

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show abstract

“…In addition, using a closely related data set allowed the model to learn structural details of molecules that might be transferable to oral bioavailability. We adopted a solubility data set obtained from Hou et al 15 and the identical train, validation, and test split was used in this study. It contained a total of 9943 non-redundant molecules.…”

Section: Solubility Data Set and Oral Bioavailability Data Setmentioning

confidence: 99%

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction

Ng,

2023

J. Chem. Inf. Model.

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Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, determining the type of molecular descriptors requires domain expert knowledge and time for feature selection. With the emergence of the graph neural network (GNN), models can be trained to automatically extract features that they deem important. In this article, we exploited the automatic feature selection of GNN to predict oral bioavailability. To enhance the prediction performance of GNN, we utilized transfer learning by pretraining a model to predict solubility and obtained a final average accuracy of 0.797, an F1 score of 0.840, and an AUC-ROC of 0.867, which outperformed previous studies on predicting oral bioavailability with the same test data set.

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“… MDM 1.05 0.77 GNN 1.07 0.76 SMILES 1.14 0.73 SCHNET 1.23 0.69 Hou et al . 131 SMILES 9,943 Cui et al . BCSA 0.8 0.88 GCN AttentiveFP MPNN Lee et al .…”

Section: Introductionmentioning

confidence: 99%

Will we ever be able to accurately predict solubility?

Llompart,

Minoletti,

Baybekov

et al. 2024

Sci Data

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Accurate prediction of thermodynamic solubility by machine learning remains a challenge. Recent models often display good performances, but their reliability may be deceiving when used prospectively. This study investigates the origins of these discrepancies, following three directions: a historical perspective, an analysis of the aqueous solubility dataverse and data quality. We investigated over 20 years of published solubility datasets and models, highlighting overlooked datasets and the overlaps between popular sets. We benchmarked recently published models on a novel curated solubility dataset and report poor performances. We also propose a workflow to cure aqueous solubility data aiming at producing useful models for bench chemist. Our results demonstrate that some state-of-the-art models are not ready for public usage because they lack a well-defined applicability domain and overlook historical data sources. We report the impact of factors influencing the utility of the models: interlaboratory standard deviation, ionic state of the solute and data sources. The herein obtained models, and quality-assessed datasets are publicly available.

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Accurate Physical Property Predictions via Deep Learning

Cited by 16 publications

References 47 publications

Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ

Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction

Will we ever be able to accurately predict solubility?

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