Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach

• Main Authors: Suman Kumar Mandal, Parthapratim Munshi
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Molecules
• Subjects: lead structure; molecular docking; scoring function; kernel energy method; quantum crystallography; protein-ligand interaction
• Online Access: https://www.mdpi.com/1420-3049/26/9/2605

Optimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) can improve the accuracy by and large. We select human aldose reductase at 0.66 Å, cyclin dependent kinase 2 at 2.0 Å and estrogen receptor β at 2.7 Å resolutions with active site environment ranging from highly hydrophilic to moderate to highly hydrophobic and several of their known ligands. Overall, the use of KEM-CP alongside the GoldScore resulted superior prediction than the GoldScore alone. Unlike GoldScore, the KEM-CP approach is neither environment-specific nor structural resolution dependent, which highlights its versatility. Further, the ranking of the ligands based on the KEM-CP results correlated well with that of the experimental IC₅₀ values. This computationally inexpensive yet simple approach is expected to ease the process of virtual screening of potent ligands, and it would advance the drug discovery research.