One limitation of current data-driven approaches to lead optimization is the reliance on existing datasets, which may not fully capture the diversity of chemical space. This can lead to the generation of molecules that are similar to those in the training data, limiting the novelty of the generated compounds. To address this limitation, researchers can explore techniques such as data augmentation, where small perturbations are applied to the training data to increase diversity and encourage the model to generate more novel molecules.
Another challenge is ensuring the synthetical feasibility of the generated molecules. While deep learning models can propose structurally complex molecules, it is essential to validate their synthesizability in the laboratory. Integrating chemical rules and constraints into the optimization process can help ensure that the generated molecules are chemically feasible and can be synthesized using known synthetic routes.
Moreover, collaboration between computational chemists and synthetic chemists is crucial to validate the synthesizability of the generated molecules. By incorporating expert knowledge and feedback from experimentalists, data-driven approaches can be refined to generate molecules that are not only novel but also practical for synthesis and testing.