Using machine learning to improve ensemble docking for drug discovery

Chandak, Tanay; Mayginnes, John P.; Mayes, Howard; Wong, Chung F.

doi:10.1002/prot.25899

Cited by 25 publications

(30 citation statements)

References 32 publications

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“…Chandak et al used machine learning to improve ensemble docking for drug discovery and compared it with other classical machine learning. 25 The predicted outcome was poor prognosis if the ControlNet's poor prognosis score was greater than the cutoff value of 0.50 in the current study. This made the ensemble FCNN model easier to use.…”

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confidence: 51%

Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

Feng¹,

Ren²,

Zhou³

et al. 2021

RMHP

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Background: Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide. Although there are many predictors of death for CAP, there are still some limitations. This study aimed to build a simple and accurate model based on available and common clinical-related feature variables for predicting CAP mortality by adopting machine learning techniques. Methods: This was a single-center retrospective study. The data used in this study were collected from all patients (≥18 years) with CAP admitted to research hospitals between January 2012 and April 2020. Each patient had 62 clinical-related features, including clinical diagnostic and treatment features. Patients were divided into two endpoints, and by using Tensorflow2.4.1 as the modeling framework, a three-layer fully connected neural network (FCNN) was built as a base model for classification. For a comprehensive comparison, seven classical machine learning methods and their integrated stacking patterns were introduced to model and compare the same training and test data. Results: A total of 3997 patients with CAP were included; 205 (5.12%) died in the hospital. After performing deep learning methods, this study established an ensemble FCNN model based on 12 FCNNs. By comparing with seven classical machine learning methods, the area under the curve of the ensemble FCNN was 0.975 when using deep learning algorithms to classify poor from good prognosis based on available and common clinical-related feature variables. The predicted outcome was poor prognosis if the ControlNet's poor prognosis score was greater than the cutoff value of 0.50. To confirm the scientificity of the ensemble FCNN model, this study analyzed the weight of random forest features and found that mainstream prognostic features still held weight, although the model is perfect after integrating other factors considered less important by previous studies. Conclusion:This study used deep learning algorithms to classify prognosis based on available and common clinical-related feature variables in patients with CAP with high accuracy and good generalizability. Every clinical-related feature is important to the model.

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confidence: 51%

Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

Feng¹,

Ren²,

Zhou³

et al. 2021

RMHP

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“…In Results, we use several examples to illustrate how EDock‐ML can be used. In Materials and Methods, we outline the implementation of EDock‐ML, and summarize the underlying methods and benchmarking published earlier 25 …”

Section: Introductionmentioning

confidence: 99%

“…Recently, we used machine learning to resolve these problems 25,26 . Machine learning compensates for the lack of a rigorous theory by learning from known experimental data.…”

Section: Introductionmentioning

confidence: 99%

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EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery

Chandak

Wong

2021

Protein Science

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EDock‐ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock‐ML takes a bottom‐up approach in which machine‐learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine‐learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324–2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock‐ML can be accessed freely at edock‐http://ml.umsl.edu

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“…A common limitation of screening with multiple conformations, of any origin, is the challenge in comparing docking scores for molecules docked to different conformations. This has limited the adoption of earlier ensemble approaches, but machine learning techniques can mitigate this problem 43 . Importantly, conventional docking and scoring protocols cannot internalize receptor conformational variability encoded in the thousands of structurally resolved receptor-ligand complexes in, for example, the PDBbind dataset.…”

Section: Introductionmentioning

confidence: 99%

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High Throughput Screens

Stafford

Anderson

Sorenson

et al. 2021

Preprint

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Structure-based, virtual High Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking to generate an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo, ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here we introduce AtomNet PoseRanker, a graph convolutional network trained to identify, and re-rank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and non-cognate binding modes corresponding to distinct receptor conformations. AtomNet PoseRanker significantly enriched pose quality in docking to cognate and non-cognate receptors of the PDBbind v2019 dataset. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns, and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.

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Using machine learning to improve ensemble docking for drug discovery

Cited by 25 publications

References 32 publications

Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High Throughput Screens

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