Computer-Aided Drug Discovery 

 

Artificial intelligence in drug discovery coupled with increasing data size and computer power

Background

Compared to traditional experimental approaches, computational modeling is a promising strategy to efficiently prioritize new drug candidates at a low cost. Zhu lab developed an automatic data mining and computational modeling workflow that was proven to be applicable by selecting new drug molecules from public big data. 

Absorption, Distribution, Metabolism and Excretion (ADME) are critical for the success of drug developments. In Zhu lab, our efforts also include the developments of new ADME models for drug molecules. Furthermore, we are specially interested in studying membrane transporters for their roles to determine drug ADME and toxicities.

 

Case study: Designing safe analgesic agents using date-driven modeling

A large opioid data set was used as the probe to automatically obtain bioassay data from the PubChem portal. 

There were 114 PubChem bioassays selected to build quantitative structure−activity relationship (QSAR) models, and good models developed for 14 bioassays were selected to fill data gaps in the probe opioids data set and to predict general drug compounds in the DrugBank data set. 

The 14 assays were mainly associated with binding affinities to different opioid receptors and the models differentiate opioids and nonopioid drug compounds. These models can generate a bioprofile for unknown compounds that can make it feasible to study the binding mechanisms to opioid receptors and other relevant receptors, leading to safer analgesic medicine design.

 This study provides a universal modeling strategy that can take advantage of large public data sets for computer-aided drug design (CADD).

Related Research Articles: 

Jia, X., Ciallella, H. L., Russo, D. P., Zhao, L., James, M. H., & Zhu, H. (2021). Construction of a virtual opioid bioprofile: a data-driven QSAR modeling study to identify new analgesic opioids. ACS sustainable chemistry & engineering, 9(10), 3909-3919.  (Case Study)

Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H., & Ekins, S. (2018). Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Molecular pharmaceutics, 15(10), 4361-4370. 

Wang, W., Kim, M. T., Sedykh, A., & Zhu, H. (2015). Developing enhanced blood–brain barrier permeability models: integrating external bio-assay data in QSAR modeling. Pharmaceutical research, 32, 3055-3065.

Kim, M. T., Sedykh, A., Chakravarti, S. K., Saiakhov, R. D., & Zhu, H. (2014). Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches. Pharmaceutical research, 31, 1002-1014.  

Review Articles:

Zhu, H. (2020). Big data and artificial intelligence modeling for drug discovery. Annual review of pharmacology and toxicology, 60, 573-589.

Zhao, L., Ciallella, H. L., Aleksunes, L. M., & Zhu, H. (2020). Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug discovery today, 25(9), 1624-1638. 

Zhang, L., Tan, J., Han, D., & Zhu, H. (2017). From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug discovery today, 22(11), 1680-1685.