Targeting ion channels with ultra-large library screening for hit discovery

Abstract

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2

“…This approach nevertheless requires that the target exhibits a druggable cavity at its surface, in other words a pocket with physicochemical (good hydrophilic vs hydrophobic balance) and topological (e.g., good accessibility and buriedness) properties suitable to accommodate a drug-like compound. 5,2 For pockets not having such characteristics (e.g., ion channel pores 6 or flat protein− protein/peptide interfaces 7 ), docking methods are expected to be less appropriate, notably because of the multiplicity of potential docking solutions, and the difficulty to properly rank them and prioritize potential hits. As an alternative, one may take advantage of the pocket similarity principle stating that similar pockets bind similar ligands and simply test known ligands of closely related pockets for binding to the query cavity.…”

“…Docking is usually restricted to a cavity/pocket of interest, and many algorithms are now available to automatically detect cavities at the surface of macromolecules using geometric, energetic, and more recently data-driven approaches. , Once detected, various machine learning and deep learning models can be trained on topological and physicochemical pocket descriptors (e.g., size, curvature, hydrophobic/hydrophilic balance) to predict their structural druggability, in other words the probability to host a high-affinity low-molecular-weight ligand. This approach nevertheless requires that the target exhibits a druggable cavity at its surface, in other words a pocket with physicochemical (good hydrophilic vs hydrophobic balance) and topological (e.g., good accessibility and buriedness) properties suitable to accommodate a drug-like compound. , For pockets not having such characteristics (e.g., ion channel pores or flat protein–protein/peptide interfaces), docking methods are expected to be less appropriate, notably because of the multiplicity of potential docking solutions, and the difficulty to properly rank them and prioritize potential hits. As an alternative, one may take advantage of the pocket similarity principle stating that similar pockets bind similar ligands and simply test known ligands of closely related pockets for binding to the query cavity. , To be efficient, the pair of pockets to compare needs to be globally similar in their total shape and physicochemical properties.…”