Artificial intelligence is rapidly transforming biologic drug discovery from a slow, experimental process into a data-driven engineering discipline. A new review traces how advances in deep learning – from protein language models that interpret sequence “grammar” to structure predictors like AlphaFold and next-generation generative models – are enabling researchers to decode, predict, and even create complex biologic molecules with unprecedented precision. By learning from vast biological datasets, these systems can uncover patterns linking sequence, structure, and function, shifting the field away from serendipitous discovery toward rational, design-led innovation.
The impact is already being felt across the development pipeline. AI can design entirely new proteins, antibodies, peptides, and nucleic acids with tailored functions, while simultaneously optimizing critical properties such as binding affinity, stability, and manufacturability. Generative approaches, including diffusion models and autoregressive architectures, are particularly powerful, allowing scientists to explore vast regions of molecular “design space” that would be inaccessible through conventional screening or directed evolution. In parallel, machine learning is improving delivery strategies – such as lipid nanoparticles, viral vectors, and antibody–drug conjugates – by predicting performance, guiding formulation, and even proposing novel components, helping biologics reach their targets more effectively in the body.
These advances are also beginning to translate into real-world progress. Early AI-designed biologics, including peptide therapeutics, antibodies, and mRNA-based candidates, are entering clinical evaluation, demonstrating that computational design can move beyond theory into practice. At the same time, AI-guided optimization is accelerating traditionally labor-intensive steps such as affinity maturation and stability engineering, reducing the need for extensive experimental screening and shortening development timelines.
However, the review emphasizes that significant hurdles remain. Current models often excel at predicting molecular structure but struggle to capture the complexity of biological systems, leading to a persistent gap between in silico predictions and in vivo outcomes. Factors such as immunogenicity, pharmacokinetics, and cellular context remain difficult to model accurately. Progress is also constrained by limited access to high-quality, task-specific datasets and the difficulty of optimizing multiple drug properties simultaneously without trade-offs.
To address these challenges, the authors highlight the need for tighter integration between computation and experiment, particularly through closed-loop, AI-driven workflows in which automated experiments continuously generate data to refine models. Building such “AI-native” experimental ecosystems – combined with more interpretable and controllable models – could help bridge the gap between prediction and performance. If successful, this approach may usher in a new era of faster, more reliable, and increasingly autonomous biologic drug discovery.
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