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Optimization Beyond the Lab: AI/ML in Antibody Lead Identification

Fifty years after the discovery of monoclonal antibodies (mAbs), the field is undergoing a transformation. Antibodies are no longer confined to traditional Fab and IgG scaffolds. New therapeutic demands are driving the adoption of multispecific constructs, antibody–drug conjugates (ADCs), and advanced engineering strategies, all while artificial intelligence (AI) and machine learning (ML) begin to reshape discovery pipelines. The challenge today is not simply identifying a “tight binder,” but selecting leads that are potent, stable, manufacturable, and format-ready for next-generation therapies [1].

AI/ML in Antibody Discovery

In recent years, AI/ML has shifted from theoretical promise to practical application. Large-scale antibody language models trained on paired heavy and light chains have demonstrated superior performance in binding prediction compared with earlier protein models [2]. High-throughput experimentation combined with active learning loops has further accelerated discovery, enabling smaller experimental sets to yield richer insights [3].

Generative frameworks—such as variational autoencoders, diffusion models, and transformers—are now being applied to generate de novo complementarity-determining region (CDR) variants that balance affinity, stability, and developability [4]. At the same time, ML-based tools can predict aggregation and viscosity, helping to flag potential formulation liabilities at high concentration before costly scale-up [5,6].

Despite this progress, limitations remain. Current models often fail with novel or out-of-distribution antigens, and they still struggle to account for conformational flexibility, glycosylation, and induced fit [7,8,9]. As a result, AI/ML has not replaced the lab, but it is increasingly recognized as a force multiplier that narrows search spaces, reduces attrition, and accelerates cycles.

New Demands for Multispecifics and ADCs

The requirements for antibody leads now vary substantially depending on modality.

Multispecifics

For bispecific and multispecific antibodies, the lead must tolerate chain-pairing controls, geometry constraints, and the presence of multiple functional domains while maintaining individual specificity and stability. Early-stage developability screening is essential to anticipate mispairing, aggregation, or high viscosity risks, as these issues are magnified in complex architectures [1].

ADCs

Leads intended for ADCs face a different set of constraints. The choice of conjugation strategy directly influences the drug-to-antibody ratio (DAR), pharmacokinetics, and safety [10]. The interplay between conjugation site and linker chemistry determines plasma stability and whether cytotoxic payloads are released selectively within target cells [11]. Advances in linker design and chemo-enzymatic conjugation methods are broadening the spectrum of payload classes and improving overall therapeutic windows [12,13].

In both multispecifics and ADCs, leads must be selected with extra biophysical headroom. A molecule that is only marginally stable as a naked IgG is unlikely to survive the additional stress imposed by linkers, payloads, or inter-domain fusions [1].

Beyond Fab and IgG

While Fab and IgG formats remain foundational due to established manufacturing infrastructure, alternative scaffolds are becoming increasingly attractive. Nanobodies, scFv-based fragments, engineered scaffolds, and hybrid Fc constructs offer compactness, modularity, and geometric flexibility [1]. These non-traditional building blocks enable novel mechanisms of action—such as receptor clustering or intracellular delivery—that conventional IgG architectures cannot always support.

The decision between IgG-based or alternative scaffolds is increasingly mechanism-driven and CMC-aware. Small modular domains may be used for rapid functional testing, with stabilization into IgG-like backbones at later stages, while in other cases, non-IgG scaffolds may represent the optimal final therapeutic format.

Rethinking Optimization: It’s Not Just About Affinity

Historically, antibody optimization was dominated by affinity maturation. Today, the optimization landscape has broadened to encompass multiple axes:

  • Affinity and potency: While crucial, excessively tight binding can impair tissue penetration or receptor cycling [14].

  • Specificity and safety: Avoiding off-target interactions is particularly critical in multispecific and ADC contexts [1].

  • Stability and viscosity: Aggregation and high viscosity remain major liabilities for manufacturability and subcutaneous delivery, with computational tools now aiding early prediction [6].

  • Pharmacokinetics and distribution: FcRn tuning, glycosylation, and molecular size influence exposure, half-life, and tissue penetration [14].

  • Manufacturability: High expression yields, purification robustness, and tolerance to chemical modification are vital for both multi-specifics and ADCs [10].

The “tightest binder” is no longer the gold standard. Instead, the most successful leads are those that achieve a balanced profile across potency, stability, developability, and modality compatibility.

Outlook

The field of monoclonal antibody discovery has entered a new era. AI and ML are enabling faster, smarter decision-making. Multispecifics and ADCs are pushing design constraints beyond what naked IgGs demanded. And optimization is no longer about affinity alone but about resilience, balance, and manufacturability.

The next fifty years of monoclonals will likely be defined not by a single dominant scaffold, but by the ability to match building blocks and optimization strategies to therapeutic intent, supported by increasingly powerful computational tools.

References

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