scholarly journals Multitask Prediction of Site Selectivity in Aromatic C-H Functionalization Reactions

2019 ◽  
Author(s):  
Thomas J. Struble ◽  
Connor W. Coley ◽  
Klavs F. Jensen
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Convolutional Neural Network ◽  
High Selectivity ◽  
Accurate Prediction ◽  
Process Chemistry ◽  
Site Selectivity ◽  
Synthetic Routes ◽  
Validation Testing ◽  
Reciprocal Rank

Aromatic C-H functionalization reactions are an important part of the synthetic chemistry toolbox. Accurate prediction of site selectivity can be crucial for prioritizing target compounds and synthetic routes in both drug discovery and process chemistry. However, selectivity may be highly dependent on subtle electronic and steric features of the substrate. We report a generalizable approach to prediction of site selectivity that is accomplished using a graph-convolutional neural network for the multitask prediction of 123 C-H functionalization tasks. In an 80/10/10 training/validation/testing pseudo-time split of about 58,000 aromatic C-H functionalization reactions from the Reaxys database, the model achieves a mean reciprocal rank of 92%. Once trained, inference requires approximately 200 ms per compound to provide quantitative likelihood scores for each task. This approach and model allow a chemist to quickly determine which C-H functionalization reactions-if any-might proceed with high selectivity.

scholarly journals Multitask Prediction of Site Selectivity in Aromatic C-H Functionalization Reactions

2019 ◽  
Author(s):  
Thomas J. Struble ◽  
Connor W. Coley ◽  
Klavs F. Jensen
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Convolutional Neural Network ◽  
High Selectivity ◽  
Accurate Prediction ◽  
Process Chemistry ◽  
Site Selectivity ◽  
Synthetic Routes ◽  
Validation Testing ◽  
Reciprocal Rank

Aromatic C-H functionalization reactions are an important part of the synthetic chemistry toolbox. Accurate prediction of site selectivity can be crucial for prioritizing target compounds and synthetic routes in both drug discovery and process chemistry. However, selectivity may be highly dependent on subtle electronic and steric features of the substrate. We report a generalizable approach to prediction of site selectivity that is accomplished using a graph-convolutional neural network for the multitask prediction of 123 C-H functionalization tasks. In an 80/10/10 training/validation/testing pseudo-time split of about 58,000 aromatic C-H functionalization reactions from the Reaxys database, the model achieves a mean reciprocal rank of 92%. Once trained, inference requires approximately 200 ms per compound to provide quantitative likelihood scores for each task. This approach and model allow a chemist to quickly determine which C-H functionalization reactions-if any-might proceed with high selectivity.

scholarly journals Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249404
Author(s):  
Jeongtae Son ◽  
Dongsup Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Discovery ◽  
Ligand Binding ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Docking Simulation ◽  
Computational Time ◽  
Binding Affinities

Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding affinity. Graph convolutional neural networks reduce the computational time and resources that are normally required by the traditional convolutional neural network models. In this technique, the structure of a protein-ligand complex is represented as a graph of multiple adjacency matrices whose entries are affected by distances, and a feature matrix that describes the molecular properties of the atoms. We evaluated the predictive power of GraphBAR for protein-ligand binding affinities by using PDBbind datasets and proved the efficiency of the graph convolution. Given the computational efficiency of graph convolutional neural networks, we also performed data augmentation to improve the model performance. We found that data augmentation with docking simulation data could improve the prediction accuracy although the improvement seems not to be significant. The high prediction performance and speed of GraphBAR suggest that such networks can serve as valuable tools in drug discovery.

scholarly journals DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization

2021 ◽  
Author(s):  
Harrison Green ◽  
David Ryan Koes ◽  
Jacob D Durrant
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Lead Optimization ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Computer Aided

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is...

scholarly journals Predicting Successes and Failures of Clinical Trials With Outer Product–Based Convolutional Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Sangwoo Seo ◽  
Youngmin Kim ◽  
Hyo-Jeong Han ◽  
Woo Chan Son ◽  
Zhen-Yu Hong ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Drug Discovery ◽  
Convolutional Neural Network ◽  
Area Under The Curve ◽  
Good Prediction ◽  
Outer Product ◽  
Drug Candidates ◽  
Good Prediction Performance ◽  
Data Driven Approach

Despite several improvements in the drug development pipeline over the past decade, drug failures due to unexpected adverse effects have rapidly increased at all stages of clinical trials. To improve the success rate of clinical trials, it is necessary to identify potential loser drug candidates that may fail at clinical trials. Therefore, we need to develop reliable models for predicting the outcomes of clinical trials of drug candidates, which have the potential to guide the drug discovery process. In this study, we propose an outer product–based convolutional neural network (OPCNN) model which integrates effectively chemical features of drugs and target-based features. The validation results via 10-fold cross-validations on the dataset used for a data-driven approach PrOCTOR proved that our OPCNN model performs quite well in terms of accuracy, F1-score, Matthews correlation coefficient (MCC), precision, recall, area under the curve (AUC) of the receiver operating characteristic, and area under the precision–recall curve (AUPRC). In particular, the proposed OPCNN model showed the best performance in terms of MCC, which is widely used in biomedicine as a performance metric and is a more reliable statistical measure. Through 10-fold cross-validation experiments, the accuracy of the OPCNN model is as high as 0.9758, F1 score is as high as 0.9868, the MCC reaches 0.8451, the precision is as high as 0.9889, the recall is as high as 0.9893, the AUC is as high as 0.9824, and the AUPRC is as high as 0.9979. The results proved that our OPCNN model shows significantly good prediction performance on outcomes of clinical trials and it can be quite helpful in early drug discovery.

scholarly journals Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning

2019 ◽  
Vol 98 (1) ◽  
Author(s):  
Ruben Hemelings ◽  
Bart Elen ◽  
João Barbosa-Breda ◽  
Sophie Lemmens ◽  
Maarten Meire ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Accurate Prediction ◽  
Fundus Images
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