How drug candidates interact with their target to treat disease is the key to drug development?
Computers and chemists have been friends for a long time. But when it comes to drug discovery, current computational methods are often too drawn out and inefficient to identify the mechanism of action of drugs. Now, a multidisciplinary team of researchers believes artificial intelligence could help out in pharmacology studies. The team’s new approach successfully identified a potent inhibitor for 5-lipoxygenase, an enzyme that is over-expressed in a range of human tumors.
Identifying how drug candidates interact with their target to treat disease is key to drug development. Artificial intelligence provides research hypotheses that need to be experimentally confirmed but in a much faster and economical way.
Machine learning can go way beyond more classical molecular docking. The new artificial intelligence tool does not depend on the structure of proteins so it can be applied in cases where molecular docking might not. This tool is also quicker, hence cheaper. ‘Docking tends to be computationally expensive, whereas we can profile one molecule against thousands of drug targets in less than 10 minutes.
Researchers relied on a huge database of compounds and drug targets they used to ‘teach’ a single desktop computer. They used two different machine learning methods: ‘One gives a bind/don’t bind answer … and the other use several decision trees to predict an affinity value. Then, the algorithm gives a prediction. In this case, it suggested likely targets for the natural product β-lapachone – among them enzyme 5-lipoxygenase. Machine learning allows us to leverage statistical patterns found in data. When high-quality datasets exist, machine learning can model these phenomena much faster and cheaper … accelerating efforts for the discovery of novel drugs.
The team’s chemists also synthesized a set of eight β-lapachone analogues, and tested their binding affinity to 5-lipoxygenase. None of them outperformed β-lapachone anticancer activity, however. The algorithm had found a perfect match. This highlights the importance of the structure and substitution pattern for bioactivity. To further analyze how β-lapachone binds to its target enzyme, they created enzyme models and carried out computational studies that confirmed what they had found in the lab – β-lapachone binds strongly to the enzyme’s active site.
In future artificial intelligence will become essential in the search for, and development of, new ligands and drug candidates. Chemistry labs will almost certainly change thanks to these new techniques. Artificial intelligence will be integrated into all aspects: simulation, experimental planning, and characterization.
For more details visit: https://bioorganic-medicinal.chemistryconferences.org
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