A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Matsuzaka, Yasunari; Uesawa, Yoshihiro

doi:10.21775/cimb.042.455

Cited by 7 publications

(18 citation statements)

References 87 publications

(94 reference statements)

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“…3D structures were saved in an SDF file format after “Rebuild 3D” (refer to Calculation of Molecular Descriptors). The 3D chemical structures were depicted as 3D ball-and-stick models with different colors corresponding to different atoms by Jmol, open-source Java viewer software for the 3D molecular modeling of chemical structures, as previously reported. − The 3D chemical structures were captured automatically as snapshots with user-defined angle increments with respect to the x , y , and z axes. In this study, a four-angle increment was used: (65°, 65°, 65°), (85°, 85°, 85°), (105°, 105°, 105°), and (145°, 145°, 145°).…”

Section: Experimental Sectionmentioning

confidence: 99%

“…In this study, a four-angle increment was used: (65°, 65°, 65°), (85°, 85°, 85°), (105°, 105°, 105°), and (145°, 145°, 145°). Other parameters for the DeepSnap depiction process were set based on previous studies − as follows: image pixel size, 256 × 256; molecule number per SDF file to split into, 100; zoom factor (%), 100; atom size for van der Waals radius (%), 23; bond radius (mÅ), 15; minimum bond distance, 0.4; bond tolerance, 0.8. The snapshots were saved as 256 × 256 pixel-resolution PNG files (RGB).…”

Section: Experimental Sectionmentioning

confidence: 99%

“…All the two-dimensional (2D) PNG images produced by DeepSnap were resized by utilizing NVIDIA DL GPU Training System (DIGITS) version 6.0.0 software (NVIDIA, Santa Clara, CA, USA) on four-GPU systems, Te2D sla-V100 (32 GB), with a resolution of 256 × 256 pixels as input data, as previously reported. − To rapidly train and fine-tune the highly accurate Convolutional Neural Network (CNN) using the input DeepSnap (Training) and DeepSnap (Validation) datasets based on the image classification and by building the pretrained prediction model, we used a pretrained open-source DL model, Caffe, and open-source software on the Ubuntu distribution 16.04LTS. In this study, the deep CNN architecture was GoogLeNet and Adam was used for optimization.…”

Section: Experimental Sectionmentioning

confidence: 99%

“…The probabilities for each image of one molecule captured at different angles with respect to the x , y , and z axes using DeepSnap-DL were calculated. The medians of each of these predicted values were used as the representative values for target molecules as previously reported. − …”

Section: Experimental Sectionmentioning

confidence: 99%

“…Uesawa recently reported a new method called DeepSnap, which uses images of compounds as features for DL . DeepSnap and Deep Learning (DeepSnap-DL) provided better predictions of toxicological targets including mitochondrial membrane potential disruption, constitutive androstane receptor (CAR), and aryl hydrocarbon receptor (AhR) compared with conventional ML. − However, there have been no reports on ADME parameters using DeepSnap-DL. Therefore, constructing ADME parameters using DeepSnap-DL might have good prediction accuracy similar to that for toxicological targets.…”

Section: Introductionmentioning

confidence: 99%

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Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

2021

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Some targets predicted by machine learning (ML) in drug discovery remain a challenge because of poor prediction. In this study, a new prediction model was developed and rat clearance (CL) was selected as a target because it is difficult to predict. A classification model was constructed using 1545 in-house compounds with rat CL data. The molecular descriptors calculated by Molecular Operating Environment (MOE), alvaDesc, and ADMET Predictor software were used to construct the prediction model. In conventional ML using 100 descriptors and random forest selected by DataRobot, the area under the curve (AUC) and accuracy (ACC) were 0.883 and 0.825, respectively. Conversely, the prediction model using DeepSnap and Deep Learning (DeepSnap-DL) with compound features as images had AUC and ACC of 0.905 and 0.832, respectively. We combined the two models (conventional ML and DeepSnap-DL) to develop a novel prediction model. Using the ensemble model with the mean of the predicted probabilities from each model improved the evaluation metrics (AUC = 0.943 and ACC = 0.874). In addition, a consensus model using the results of the agreement between classifications had an increased ACC (0.959). These combination models with a high level of predictive performance can be applied to rat CL as well as other pharmacokinetic parameters, pharmacological activity, and toxicity prediction. Therefore, these models will aid in the design of more rational compounds for the development of drugs.

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Section: Experimental Sectionmentioning

confidence: 99%

Section: Experimental Sectionmentioning

confidence: 99%

Section: Experimental Sectionmentioning

confidence: 99%

Section: Experimental Sectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

2021

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Identification of Potential Aldose Reductase Inhibitors Using Convolutional Neural Network-Based in Silico Screening

Kashyap,

Mahapatra,

Ahmed

et al. 2023

J. Chem. Inf. Model.

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Aldose reductase (ALR2) is a notable enzyme of the polyol pathway responsible for aggravating diabetic neuropathy complications. The first step begins when it catalyzes the reduction of glucose to sorbitol with NADPH as a coenzyme. Elevated concentrations of sorbitol damage the tissues, leading to complications like neuropathy. Though considerable effort has been pushed toward the successful discovery of potent inhibitors, its discovery still remains an elusive task. To this end, we present a 3D convolutional neural network (3D-CNN) based ALR2 inhibitor classification technique by dealing with snapshots of images captured from 3D chemical structures with multiple rotations as input data. The CNN-based architecture was trained on the 360 sets of image data along each axis and further prediction on the Maybridge library by each of the models. Subjecting the retrieved hits to molecular docking leads to the identification of the top 10 molecules with high binding affinity. The hits displayed a better blood− brain barrier penetration (BBB) score (90% with more than four scores) as compared to standard inhibitors (38%), reflecting the superior BBB penetrating efficiency of the hits. Followed by molecular docking, the biological evaluation spotlighted five compounds as promising ALR2 inhibitors and can be considered as a likely prospect for further structural optimization with medicinal chemistry efforts to improve their inhibition efficacy and consolidate them as new ALR2 antagonists in the future. In addition, the study also demonstrated the usefulness of scaffold analysis of the molecules as a method for investigating the significance of structurally diverse compounds in data-driven studies. For reproducibility and accessibility purposes, all of the source codes used in our study are publicly available.

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Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning

2022

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The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure–activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of this study was to develop a new regression model of rat clearance (CL). We constructed a regression model using 1545 in-house compounds for which we had rat CL data. Molecular descriptors were calculated using molecular operating environment, alvaDesc, and ADMET Predictor software. The classification model of DeepSnap and Deep Learning (DeepSnap-DL) with images of the three-dimensional chemical structures of compounds as features was constructed, and the prediction probabilities for each compound were calculated. For molecular descriptor-based methods that use molecular descriptors and conventional machine learning algorithms selected by DataRobot, the correlation coefficient ( R 2 ) and root mean square error (RMSE) were 0.625–0.669 and 0.295–0.318, respectively. We combined molecular descriptors and prediction probability of DeepSnap-DL as features and developed a novel regression method we called the combination model. In the combination model with these two types of features and conventional algorithms selected by DataRobot, R 2 and RMSE were 0.710–0.769 and 0.247–0.278, respectively. This finding shows that the combination model performed better than molecular descriptor-based methods. Our combination model will contribute to the design of more rational compounds for drug discovery. This method may be applicable not only to rat CL but also to other pharmacokinetic and pharmacological activity and toxicity parameters; therefore, applying it to other parameters may help to accelerate drug discovery.

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A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Cited by 7 publications

References 87 publications

Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

Identification of Potential Aldose Reductase Inhibitors Using Convolutional Neural Network-Based in Silico Screening

Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning

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