The yellow quadrant in the image above relates to Basic Strategy 3, ligand-based drug design (LBDD). In this case, the ligand’s structure is known (e.g., a substrate or inhibitor), but the protein target’s experimental structure is unavailable. The principle of this method suggests that knowing the structure of one or more ligands that bind to a protein allows us to create additional ligands with similar structures and equal or greater binding affinity. In this scenario, we can, for example, use the protein’s AlphaFold model to obtain details of the protein’s ligand-binding site, and use the known ligand (or ligands) structure to build a pharmacophore model based on the ligand’s shape and the presence of charged, polar, and hydrophobic groups. When several known ligands are available, their common features can be overlaid to improve the accuracy of the pharmacophore model. We can also use a computational method called ligand hopping. In this case, computational chemistry, machine learning, and AI methods, together with medicinal chemistry expertise, are employed to identify new chemical structures with similar activity. The ligand-hopping method in drug design is often used to obtain a new structure (chemotype), e.g., to overcome patent protection, improve the characteristics of a known ligand, or even to rescue failed leads with some degree of activity. The AlphaFold model in combination with the pharmacophore model can also be used, e.g., to perform virtual screening of a chemical library to identify similar compounds and to conduct quantitative structure-activity relationship (QSAR) analysis. Subsequently, the hits are ranked, and the best binders are selected for biochemical and cell-based assay analysis. This process is repeated until good-quality lead compounds are obtained. It should be noted that computational methods, even when combined with an AlphaFold model, may yield inaccurate results. The lack of experimental 3D structures in LBDD presents several challenges. One major limitation, as noted above, is that this method relies heavily on high-quality ligand data and does not provide direct insights into ligand binding to their targets. Consequently, this can significantly slow down the optimization of initial hits. In contrast, traditional SBDD offers direct mechanistic insights into ligand-binding interactions and facilitates the rational design of new ligands. This explains the benefits of structure-based drug design. LBDD can be useful in the early stages of a project, while the target’s experimental structure and its ligand complexes are being determined using traditional techniques such as X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy (cryo-EM). An experimental 3D structure in complex with a ligand will enable the implementation of Basic Strategy 1, ultimately accelerating the drug discovery process.
