Protein-ligand interaction prediction: an improved chemogenomics approach

By Laurent Jacob, Jean-Philippe Vert. Bioinform., 2008

376 citations recorded by the source corpus.

Motivation: Predicting interactions between small molecules and proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. When no detailed 3D structure of the protein target is available, ligand-based virtual screening allows the construction of predictive models by learning to discriminate known ligands from non-ligands. However, the accuracy of ligand-based models quickly degrades when the number of known ligands decreases, and in particular the approach is not applicable for orphan receptors with no known ligand. Results: We propose a systematic method to predict ligand–protein interactions, even for targets with no known 3D structure and few or no known ligands. Following the recent chemogenomics trend, we adopt a cross-target view and attempt to screen the chemical space against whole families of proteins simultaneously. The lack of known ligand for a given target can then be compensated by the availability of known ligands for similar targets. We test this strategy on three important classes of drug targets, namely enzymes, G-protein-coupled receptors (GPCR) and ion channels, and report dramatic improvements in prediction accuracy over classical ligand-based virtual screening, in particular for targets with few or no known ligands. Availability: All data and algorithms are available as Supplementary Material. Contact: laurent.jacob@ensmp.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cf20dcbfc0fd9ffeb737ef1b65d3c95c7fd849fe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Min Wu
2. Peilin Zhao
3. Artem Cherkasov
4. Chunyan Miao
5. Hiroshi Mamitsuka
6. Dejing Dou
7. Martin Ester
8. Tong He

In Molecular Biology

1. Min Wu
2. Peilin Zhao
3. Artem Cherkasov
4. Chunyan Miao
5. Hiroshi Mamitsuka
6. Dejing Dou
7. Martin Ester
8. Tong He

Citations in the corpus

• Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “similarities using kernels [13, 14, 17, 29] or factorizing the similarity matrices [27].”
• Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, methodology): “SVM in general needs a similarity matrix (kernel) of instances with labels [28].”
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “As a crucial stage in drug discovery, predicting protein-ligand binding affinity has been intensively studied for a long time [11, 34], which is of great importance for efficient and accurate drug screening.”
• SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, methodology): “In similarity-based methods [3, 27–32], similarity matrices for both the drug–drug pairs and the target–target pairs are generated.”
• Pairwise input neural network for target-ligand interaction prediction (2014, methodology): “[8] also constructed a prediction model, except that they adopted EC (Enzyme Commission) numbers rather then the protein sequences themselves to describe the targets.”