Drug molecules binding to off-targets can lead to undesirable side effects, which often result in the failure of drug candidates during the development process. Therefore, it is crucial to identify the propensity of off-target binding at early stages to prioritize molecules from a toxicity perspective. As computational toxicologists, our goal is to provide accurate predictions and insights to guide the selection and optimization of lead molecules, ultimately improving the success rate of drug development. By integrating computational toxicology into the drug discovery process, we can reduce the likelihood of unexpected side effects and increase the efficiency of identifying safe and effective drug candidates.
In this presentation, I will highlight our strategies for identifying off-target binding propensity and how it aids in molecular discovery. Through case studies involving a few off-targets, we will demonstrate how our computational toxicology strategies have helped in molecular design. By analyzing the binding interactions and structural characteristics of those off target binding, we can propose modifications to improve selectivity and reduce the risk of adverse effects.
By leveraging computational methods, we can prioritize molecules to a safer chemical space. In addition, we can provide strategies to design out molecules that is likely to have safer profiles as it advances towards final candidate selection.
Overall, our presentation will provide insights into the role of computational toxicology in prioritizing chemotypes and guiding molecular design during lead identification and optimization stages. By integrating ligand and structure-based design principles, we can enhance the efficiency and safety of drug discovery processes.