Applying ADMET Predictions 

Applying ADMET Predictions in Computer-Aided Drug Design

In silico ADMET predictions are very useful at different points in the process of computer-aided drug design:

Lead Identification: Quickly go through large virtual libraries of compounds to find the ones with the best ADMET profiles. This will cut down on the number of compounds that need to be tested in the lab.

Lead Optimisation: Change the chemical structure of a lead compound to make certain ADMET properties better (for example, make it more bioavailable when taken by mouth, less toxic, or better able to cross the blood-brain barrier for CNS drugs).

Dose Prediction: Even though they aren't direct, good ADMET properties lead to better pharmacokinetic profiles, which can help with early dose estimates.

Risk Assessment: Find out about possible toxicity problems early on so that medicinal chemists can plan around them before spending a lot of money.

Limitations and Considerations

In silico ADMET predictions are powerful, but they have some limits:

Model Accuracy: The models that make predictions for the ADMET properties are based on data that is already there. Predictions may be less accurate for new chemical scaffolds or compounds that are far outside the chemical space of the training set used in ADMETlab or any other web server.

Data Quality: The accuracy of predictions of ADMET properties is directly affected by the quality of the experimental data that is used to train the models.

Simplified Models: In silico models are simplified versions of complicated biological systems, and they might not show all the details of how things work in vivo. Experimental methods must be used to verify compounds of interest.

Context Dependency: ADMET properties can be affected by numerous biological factors (e.g., disease state, genetic variations) that are not entirely represented in in silico models.

Complementary Tool: In silico predictions should always be seen as a tool that works with in vitro and in vivo experiments, not as a replacement.

In silico ADMET prediction, particularly with accessible platforms such as ADMETlab 3.0, has transformed the initial phases of drug discovery.  It helps medicinal chemists make better decisions, speeds up the drug development process, and ultimately gets safer and more effective medicines to patients by giving them quick and cheap information about the pharmacokinetic and toxicological profiles of compounds.  Anyone who works in modern drug discovery and development needs to know how to use these tools well.

REFERENCES

ADMETlab 3.0. https://admetlab3.scbdd.com/

Benfenati, E. (2022). In silico methods for predicting drug toxicity. In Methods in molecular biology. https://doi.org/10.1007/978-1-0716-1960-5.

Cheng, F., Li, W., Liu, G., & Tang, Y. (2013). In Silico ADMET Prediction: recent advances, current challenges and future trends. Current Topics in Medicinal Chemistry, 13(11), 1273–1289. https://doi.org/10.2174/15680266113139990033.

Dong, J., Wang, N., Yao, Z., Zhang, L., Cheng, Y., Ouyang, D., Lu, A., & Cao, D. (2018). ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Journal of Cheminformatics, 10(1). https://doi.org/10.1186/s13321-018-0283-x.

Fu, L., Shi, S., Yi, J., Wang, N., He, Y., Wu, Z., Peng, J., Deng, Y., Wang, W., Wu, C., Lyu, A., Zeng, X., Zhao, W., Hou, T., & Cao, D. (2024). ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Research, 52(W1), W422–W431. https://doi.org/10.1093/nar/gkae236.

Interpretation-ADMElab: ADMET Prediction. https://admet.scbdd.com/home/interpretation/.

Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., & Cao, D. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 49(W1), W5–W14. https://doi.org/10.1093/nar/gkab255.