high complexity in protein engineering. The limited success rate with only nine MsAbs approved thus far in more than two decades of research highlights the need to consider new approaches [5]. While much of the low MsAb approval rate can be attributed to sub‑ optimal prediction of the biology, this is likely due to the complexity of these new and non‑native quaternary structures and challenges in target identication. Furthermore, the developability of these NMEs largely remains a signicant challenge [6]. Since the advent of AlphaFold2 in the 14th Critical Assessment of Protein Structure Prediction (CASP14) [7], machine learning (ML)‑based methods have emerged as the next‑gen‑ eration of technologies with the potential to increase the success rate in developing MsAbs. Currently, an increasing number of new startups specializing in developing articial intelligence (AI)/ML models to address the discovery and engineering of bio‑ logics are emerging, attracting strong interest from the pharmaceutical industry. The main difference between AI and physics‑based approaches lies in their methodology. Physics‑based models rely on fundamental principles of physics like Newton’s equa‑ tions of motion, while knowledge‑based AI/ML models learn from patterns in data and incorporate expert insights. Large language models (LLMs) initially gained traction in protein modeling with the use of long short‑term memory (LSTM) architectures, includ‑ ing specialized variations like the multiplicative LSTM (mLSTM) [8,9] (Figure13.1). A key advance was the development of the UniRep representation, which allowed LSTMs to learn complex protein sequence patterns more effectively. Models built on top of UniRep achieved several breakthroughs, including predicting the stability of natural and designed proteins, the function of diverse mutants, and even accelerating protein engineering tasks with vastly improved efciency [10]. One intriguing nding was the mLSTM’s ability to enhance protein characteristics using zero‑shot or few‑shot learning techniques, even with limited data (low N) [11]. The advent of transformers revolution‑ ized natural language processing by introducing parallelizable self‑attention mecha‑ nisms, enabling them to process entire sequences simultaneously and capture long‑range dependencies within text, vastly outperforming the sequential limitations of recurrent neural networks (RNNs) and LSTMs [12]. Building upon this, Meta’s Evolutionary Scale Modeling (ESM) model, utilizing transformer‑based models, further advanced LLM capabilities in this domain [13]. Further advancements led to the development of larger LLMs, such as ESM‑1b with 650million parameters and ESM‑2 with a sig‑ nicant increase to 15 billion parameters. These larger models demonstrated improved