In the field of drug discovery, identifying new drug-target (DT) interactions is crucial. While many computational methods have been proposed to predict DT interactions, most focus on binary classification - determining whether a DT pair interacts or not. However, protein-ligand interactions involve a continuum of binding strength values, known as binding affinity, which remains a challenge to predict accurately. With the increase in available affinity data in DT knowledge-bases, advanced learning techniques such as deep learning architectures can be used to predict binding affinities. This study proposes a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. Unlike previous studies that use either 3D structures of protein-ligand complexes or 2D features of compounds for prediction, this work models protein sequences and compound 1D representations with convolutional neural networks (CNNs). The results show that the proposed deep learning-based model using 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The authors also compare their model's performance against two state-of-the-art methods: KronRLS algorithm and SimBoost. In one larger benchmark dataset, their model achieved the best Concordance Index (CI) performance by constructing high-level representations of a drug and a target via CNNs. This outperformed both KronRLS algorithm and SimBoost. Overall, this study presents a novel approach for predicting DT interaction binding affinities using deep learning architecture with only sequence information. The results demonstrate its effectiveness compared to other state-of-the-art methods and highlight its potential application in drug discovery research.
Scientists need to find new ways to make medicine. They use computers to help them. The computer tries to guess if a medicine will work with a certain part of the body. This is hard because sometimes it's not a perfect match. But now, they have a new way to guess better using special computer programs that learn from lots of examples.
Definitions
- Drug-target interactions: When medicine works with a certain part of the body.
- Computational methods: Using computers to help solve problems.
- Binary classification: Sorting things into two groups (yes or no).
- Binding affinity: How well medicine and the body part fit together.
- Deep learning architectures: Special computer programs that can learn from lots of examples and get better at guessing over time.
- Convolutional neural networks (CNNs): A type of deep learning program that can understand patterns in information like pictures or sequences of letters/numbers.
- Concordance Index (CI): A way to measure how good the computer program is at guessing if medicine will work with a certain part of the body.
Drug Discovery: Predicting Drug-Target Interaction Binding Affinities with Deep Learning
In the field of drug discovery, identifying new drug-target (DT) interactions is crucial. While many computational methods have been proposed to predict DT interactions, most focus on binary classification - determining whether a DT pair interacts or not. However, protein-ligand interactions involve a continuum of binding strength values, known as binding affinity, which remains a challenge to predict accurately. With the increase in available affinity data in DT knowledge-bases, advanced learning techniques such as deep learning architectures can be used to predict binding affinities.
Deep Learning Model for Prediction
This study proposes a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. Unlike previous studies that use either 3D structures of protein-ligand complexes or 2D features of compounds for prediction, this work models protein sequences and compound 1D representations with convolutional neural networks (CNNs).
Performance Evaluation
The results show that the proposed deep learning-based model using 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The authors also compare their model's performance against two state-of-the-art methods: KronRLS algorithm and SimBoost. In one larger benchmark dataset, their model achieved the best Concordance Index (CI) performance by constructing high-level representations of a drug and a target via CNNs. This outperformed both KronRLS algorithm and SimBoost.
Conclusion
Overall, this study presents a novel approach for predicting DT interaction binding affinities using deep learning architecture with only sequence information. The results demonstrate its effectiveness compared to other state-of-the art methods and highlight its potential application in drug discovery research.
The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark data sets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.
