Drug Off-Target Effect Prediction Service
Most drugs interact with unintended, often unknown biological targets. These off-target drug interactions and the dangerous side effects they cause are the leading cause of clinical trial failures. Understanding and predicting drug off-target effects is critical in the complex drug development process. CD ComputaBio provides comprehensive off-target drug effect prediction service based on computer-aided drug design (CADD) to help minimize these risks and increase the success rate of drug development.
Introduction to Drug Off-Target Effect Prediction
Drug off-target effect refers to the fact that in addition to interacting with its intended primary target, a drug may also interact with other unintended biological targets. These off-target interactions may lead to unexpected biological effects, induce side effects or adverse drug reactions (ADRs), and may even cause harm. In the early stages of drug development, understanding the off-target effects of drugs not only helps drug development but also avoids costly failures in subsequent stages. However, the economic burden of detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is huge.
Fig. 1 In silico drug off-target prediction. (Liu J, et al.; 2022)
In silico drug off-target effects prediction has become an important tool in the field of precision medicine and new drug development. It is a method that uses technical means such as computational biology, cheminformatics, and machine learning to predict and identify which unexpected targets a drug may interact with. The results obtained by building a predictive model can be used as key information for the characterization of compounds. This information can distinguish drugs under different anatomical therapeutic chemical (ATC) classifications and classify the toxicity of compounds, thereby discovering potential risks in the early stages of drug development. In addition, the predicted off-target spectrum can also be used for adverse drug reaction (ADR) enrichment analysis, which helps to infer the potential ADR of the drug.
Our Services
At CD ComputaBio, our scientists have created a highly innovative platform by deeply integrating three-dimensional (3D) protein structure data, nucleic acid databases, and chemical databases with artificial intelligence (AI) technology. This platform is capable of large-scale association searches and off-target effect predictions between hundreds of thousands of proteins, nucleic acid fragments, and drug molecules. Through precise modeling and analysis of active sites and drug molecular structures, we can comprehensively predict possible off-target effects of drugs.
Comprehensive Off-Target Effect Prediction
The plan uses advanced computational biology algorithms and artificial intelligence technology to conduct comprehensive off-target effect predictions for various types of compounds, including small molecule drugs, peptides, antibodies, and DNA/RNA fragments. Based on compound structure, known targets, and biological pathway data, the unintended targets that may act on them will be predicted.
Drug-Target Interaction Simulation
Based on the comprehensive prediction of potential off-target targets, CD ComputaBio provides drug-target interaction simulation services. Using advanced computational chemistry and molecular simulation techniques, we conduct in-depth research on the interaction between the compound and the predicted unexpected targets, comprehensively evaluate the safety and efficacy of the compound, and accelerate the customer's drug development process.
Adverse Reactions (ADR) Prediction and Analysis
This service is optional. If necessary, CD ComputaBio will also perform adverse reaction enrichment analysis based on predicted off-target targets to help our customers infer the side effects that the compounds may cause and provide mechanistic explanations for potential adverse reactions.
Customized Solutions
Based on your specific needs, we provide tailored off-target effect prediction and data analysis services, including data preprocessing, model customization, result interpretation, and report writing.
Methods for Drug Off-Target Effect Prediction
Ligand-Based Methods
Utilizing the structure and biological activity data of known compounds to establish a quantitative structure-activity relationship (QSAR) model to predict the compound's possible target and off-target effects and provide suggestions for structural optimization to reduce toxicity.
Structure-Based Methods
Using molecular dynamics to simulate the process of compound binding to protein, predict which proteins the compound may bind to, and evaluate off-target effects. Alternatively, using the molecular docking to dock a single compound to a large number of protein targets, identify possible binding targets, and predict potential off-target targets of the compound.
AI and Big Data-Based Methods
Using deep learning models, including convolutional neural networks (CNN), graph neural networks (GNN), etc., to automatically extract features, and capture complex nonlinear relationships to predict off-target effects of unknown compounds.
Service Highlights
Efficient AI Platform
We use an artificial intelligence platform that deeply integrates 3D protein structure, nucleic acid, and chemical databases to provide high-precision large-scale off-target effect prediction.
Multi-Level Prediction
Utilizing ligand-based, structure-based, AI, and big data methods, we comprehensively predict the off-target effects of various compounds such as small molecules, peptides, antibodies, and DNA/RNA fragments.
Efficient Result Delivery
We are committed to providing accurate and reliable prediction results in the shortest time, accelerating your R&D process, and helping you make decisions faster.
CD ComputaBio's drug off-target effect prediction services provide a valuable resource for pharmaceutical companies seeking to optimize the drug development process. Through tailored services, innovative approaches, and a commitment to excellence, we help our clients make informed decisions and accelerate the development of safe and effective therapies. Please don't hesitate to contact us, if you are interested in our services.
References:
- Liu J, et al. In silico off-target profiling for enhanced drug safety assessment. Acta Pharm Sin B. 2024;14(7):2927-2941.
- Rao M, et al. Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics. Toxics. 2023; 11(10):875.
* For Research Use Only.
Related Services
