Computational approaches are nding increasing applications toward discovery and development of biotherapeutics in recent years. This chapter has attempted to pro­vide an overview of this emerging eld, we call as biopharmaceutical informatics. However, several major challenges remain toward increasing the contribution of biopharmaceutical informatics andthereby increasingthe probability ofsuccessful translation of biologic drug candidates into medicines available in the clinic. One of the most signicant hurdles for greater acceptance for biopharmaceutical informat­ics is the predictability and validation of the computational tools. Validation via self-consistent experimental data is important for the development of these emerg­ing computational tools, but self-consistent experimental data sets on biologic mac­romolecules are rarely available in public domain. A second hurdle is that the use of antibody-based biologics as medicines is more recent when compared to the small molecule drugs. Therefore, the number of biologic drug products currently available in the market is small and our overall pharmaceutical development experience with these macromolecules is not as extensive as in thecase of the small molecule drug products. This situation is further complicated by the pervasive nature of empiri­cism inherent to both experimental and computational tools used to study biologic drug candidates, and with the arrival of novel biotherapeutic formats such as bi­specic and multi-specic antibodies. A third hurdle for the progress of biopharma­ceutical informatics is created if the computational tools are not available easily to everyone, and the underlying core programming codes are not published. A fourth hurdle is the general unavailability of the data in digital format in central reposito­ries within and outside the biopharmaceutical companies. The need for digital trans­formation of biopharmaceutical industry cannot be understated. However, digital transformation of biopharmaceutical industry is hampered by the lack of digitiza­tion of the experimental data. Overcoming the digitization hurdle that makes the experimental data available to computational biophysicists, data scientists, and machine learners is the foundational step toward digital transformation. Once this is accomplished, the scientists shall then be able to use the data and connect them with sequence and structural characteristics of biologic drug candidates. Discovery of correlations among “microscopic” (properties/descriptors derived from amino acid sequence, protein structure and molecular simulations) and “macroscopic” (experi­mental biochemical and biophysical experiments) attributes of biologic drug candi­dates will prove crucial for navigating developability of biologic drugs and will truly enable biopharmaceutical informatics. As summarized in this article, these are still early days for this eld as computational as well as hybrid studies containing computational and experimental data are beginning to emerge. Overall, this eld is expected to grow exponentially in the coming years.