In the early stages of drug development, time and accuracy are paramount. Identifying effective compounds for a specific target within a limited timeframe directly impacts research efficiency. This is where HyperLab, an AI-driven drug development platform, stands out.
In this article, we introduce two case studies from published papers that demonstrate how HyperLab was used to optimize derivatives and identify effective compounds in real-world research. Let’s explore how researchers leveraged HyperLab to achieve their desired outcomes.
This study aimed to develop a novel anticancer drug effective against various cancer cells, including brain tumors, EGFR-overexpressing cancers, and multiple myeloma. The target protein was NDUFA12, and while the existing drug Ertredin interacts with this protein, its anticancer activity was limited in certain cancer cells.
To address this, the research team utilized HyperLab to perform molecular docking and AI-driven hybrid virtual screening. As a result, they predicted that 7MeERT, a new derivative with a methyl group introduced to Ertredin, would exhibit superior binding affinity to NDUFA12. Additionally, HyperLab’s ADME/T prediction and structure-activity relationship (SAR) analysis features were used to evaluate drug-likeness and optimization strategies.
Experimental results showed that 7MeERT demonstrated stronger binding affinity than Ertredin, with a lower binding energy (−3.7 vs. −3.3 kcal/mol, where lower negative values indicate stronger binding). It also exhibited superior antiproliferative effects and apoptosis induction capabilities.
This case demonstrates how HyperLab’s AI predictions enabled early candidate selection, significantly reducing the cost and time required for drug development.
Source: Biomolecules 2024 paper (https://www.mdpi.com/2218-273X/14/9/1197)
The paper “Discovery of Selective LATS Inhibitors via Scaffold Hopping,” published last year, represents a successful application of HyperLab’s Hyper Binding function in real-world research. In this study, researchers utilized HyperLab’s Hyper Binding function to identify candidate compounds with high activity against the target protein.
The research team first analyzed the key interactions between the protein and the ligand based on the binding structure of the known reference compound, ‘Truli.’ Subsequently, they leveraged HyperLab’s Hyper Binding function to predict the binding structures and affinities of new molecules, evaluating whether the core interactions were maintained.
As a result, candidate compounds 5k and 5l, which exhibited high binding scores and maintained core interactions, were identified. Furthermore, using HyperLab’s 2D and 3D structure viewer functions, researchers confirmed that the hydrogen bonding interactions with Met154 and Glu156 in the ligand structure were preserved, consistent with the reference compound.
In conclusion, candidate compounds predicted by HyperLab to have high activity demonstrated excellent activity in experiments. This case validates that considering both binding energy and binding structure significantly enhances the success rate of Hyper Binding-based analysis.
Source: ResearchGate 2024 paper ("Discovery of selective LATS inhibitors via scaffold hopping")
This article has explored how HyperLab’s Hyper Binding and Hyper ADME/T functions were applied in real-world research through two published papers. From predicting binding affinities between target proteins and molecules to analyzing binding structures via 2D and 3D viewers, HyperLab offers a range of features to support the early stages of drug development.
Beyond these, HyperLab’s intuitive UI/UX ensures accessibility for all users. However, new users may find the setup or analysis process challenging. To assist, we offer free consulting services. For those looking to integrate AI into their drug development process, we recommend scheduling a consultation through the inquiry link below. As validated by published research, we are confident that HyperLab can significantly enhance the efficiency and accuracy of drug development.
AI-Powered Drug Discovery Platform: HyperLab
