Biocomputing and Drug Repositioning in Herbal Medicines

Inquiry

The development of innovative drugs is lengthy and costly, with high risks and low success rates. Drug repositioning, on the other hand, can unlock new uses for known herbs, reducing the cycle time, cost, and risk of herbal drug development. Lifeasible's research is based on new ideas, screening, and computer-based methods for drug repositioning from a technology platform built specifically for the development of drug repositioning. We focus on high-throughput and high-content technologies for screening, in addition to computer-based virtual screening and biocomputational methods. To improve the efficiency of the screening process, our current strategy is to use the ideas and methods of network pharmacology to first apply computer-based virtual screening to select promising drug candidates for further high-throughput and high-content screening.

Prediction of new potential targets or drug-target interactions based on small molecule (ligand) characterization, i.e., for a specific herb or compound.

Similarity search. Molecules with similar structures may bind to the same target and have similar biological functions. By comparing chemical similarities between ligand molecules, we can speculate that they may have similar targets and exert similar pharmacological effects. By this method, new pharmacological effects can be identified. The method can predict similarities between two targets that were originally thought to be biologically unrelated. Biomolecules with different functions (targets) may have similar drug-binding domains, so we compare the chemical characteristics of the drug bound to the target with the structure of the target molecule to predict the unknown target of the drug.
Quantitative structure-activity relationships (QSAR). We relate the structural parameters of a compound to its biological activity data in certain arithmetic. In the study of drug repositioning using the QSAR approach, we also introduce a multi-target QSAR (mt-QSAR) approach to overcome the drawbacks of the traditional approach of predicting the activity of a compound for only one protein target.

Predicting a new drug, target compound, or drug-target interaction based on the characteristics of a drug target (nucleic acid, protein, enzyme, ion channel, receptor, etc.), i.e., for a specific target.

To predict a new drug or target compound for a specific drug target, we use the ligand-receptor reverse docking method. This method can be used for drug-target affinity prediction, drug-target interaction prediction, etc. For example, potential targets can be predicted for drug candidates with known biological activity but unknown mechanisms of action. For drugs with a clear mechanism of action, we can search for secondary target information by reverse docking. This can be used for drug repositioning and prediction of proteins associated with drug toxicities, as well as for searching for small molecule ligand targets.

Prediction of drug-target interactions based on phenotypic (or network) characteristics, i.e., through drug-target networks.

Prediction of drug-target interactions based on phenotypic (or network) characteristics - Lifeasible

Network biology and network pharmacology provide new ideas and methods for drug repositioning research. Lifeasible helps you to explore drug repositioning discovery based on rational reasoning and computational modeling, screening of repositioned drugs based on high-throughput/high-content screening techniques, experimental validation of repositioned drugs based on biochemical and phenotypic assays, and pilot studies of repositioned drugs guided by new concepts by providing you with biocomputational and drug repositioning services for herbal medicines. Please feel free to contact us to start your biocomputational and drug repositioning research.

For Research Use Only. Not For Clinical Use.