Modern <scp>machine‐learning</scp> for binding affinity estimation of <scp>protein–ligand</scp> complexes: Progress, opportunities, and challenges

Harren, Tobias; Gutermuth, Torben; Grebner, Christoph; Hessler, Gerhard; Rarey, Matthias

doi:10.1002/wcms.1716

Cited by 2 publications

References 266 publications

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Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years

Sultan,

Sieg,

Mathea

et al. 2024

J. Chem. Inf. Model.

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Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science. Over the last decades, diverse computational techniques have been developed, from using simple physical and chemical properties and molecular fingerprints in statistical models and classical machine learning to advanced deep learning approaches. In this review, we aim to distill insights from current research on employing transformer models for MPP. We analyze the currently available models and explore key questions that arise when training and fine-tuning a transformer model for MPP. These questions encompass the choice and scale of the pretraining data, optimal architecture selections, and promising pretraining objectives. Our analysis highlights areas not yet covered in current research, inviting further exploration to enhance the field’s understanding. Additionally, we address the challenges in comparing different models, emphasizing the need for standardized data splitting and robust statistical analysis.

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Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years

Sultan,

Sieg,

Mathea

et al. 2024

J. Chem. Inf. Model.

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GEMS: A Generalizable GNN Framework For Protein-Ligand Binding Affinity Prediction Through Robust Data Filtering and Language Model Integration

Graber,

Stockinger,

Meyer

et al. 2024

Preprint

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The field of computational drug design requires accurate scoring functions to predict binding affinities for protein-ligand interactions. However, train-test data leakage between the PDBbind database and the CASF benchmark datasets has significantly inflated the performance metrics of currently available deep-learning-based binding affinity prediction models, leading to overestimation of their generalization capabilities. We address this issue by proposing PDBbind CleanSplit, a training dataset curated by a novel structure-based filtering algorithm that eliminates train-test data leakage as well as redundancies within the training set. Retraining the current best-performing model on CleanSplit caused its benchmark performance to drop to uncompetitive levels, indicating that the performance of existing models is largely driven by data leakage. In contrast, our graph neural network model for efficient molecular scoring (GEMS) maintains high benchmark performance when trained on CleanSplit. Leveraging a sparse graph modeling of protein-ligand interactions and transfer learning from language models, GEMS is able to generalize to strictly independent test datasets.

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Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges

Cited by 2 publications

References 266 publications

Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years

Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years

GEMS: A Generalizable GNN Framework For Protein-Ligand Binding Affinity Prediction Through Robust Data Filtering and Language Model Integration

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