Innovative Approaches in Antibody Discovery Platforms

Using #generativeai, we have the ability to customize the molecular structure of antibody drugs, tailoring them to possess ideal properties. It's like engineering a car to be faster, safer, and more fuel-efficient! With this technology, we can enhance the potency of these drugs at lower doses, transforming them into highly effective medicines. The result? Fewer injections for patients, significantly improving both drug safety and reliability. In this recent paper (https://lnkd.in/eiXjqSbm), the authors used a zero-shot approach to create and screen over 1 million antibody variants. Their aim was to design all CDRs in the heavy chain of the antibody specifically for binding to human epidermal growth factor receptor 2 (HER2). The outcome? They discovered three antibodies that bind to HER2 even tighter than trastuzumab(!), along with an additional 23 antibodies that exhibit moderate affinity to HER2. What's even more impressive is that they generously made the sequences open source. I am VERY excited about how this type of research can advance antibody drug development and create a future where personalized and effective treatments are accessible to all.

This excellent, brand-new paper by Williams et. al. describes studies on a pharmacore-based method for rapid and accurate virtual screening of antibody libraries against antigens. Quoting from the abstract: "Antibody-based biotherapeutics make up an important class of biopharmaceuticals. However, their discovery requires resource- and time-consuming laboratory processes. To ameliorate this situation, several computational methods were used to predict the structures of antibody:antigen complexes (Ab:Ag) and identify potential binders, in-silico. However, there is still a general lack of rapid virtual screening methods capable of screening large antibody libraries against a given antigen or group of antigens. In this work, we explore the application of a successful small-molecule drug discovery strategy and adapt pharmacophore-based virtual screening to the world of antibody discovery. Using a nonredundant data set of 874 Ab:Ag complexes, we have developed an automated method to create pharmacophores from the antibody complementarity determining regions. Our method is 98.6% (862 out of 874) successful at reproducing the ground truth, i.e., it can recapitulate the parental antibody:antigen complexes. In a benchmarking comparison with cognate docking, using 33 Ab:Ag complexes of therapeutic interest, the pharmacophore method was not only much faster than cognate docking but also recovered all the native interfacial contacts. In addition, it can also find additional putative antibody binders to a given antigen within clusters of Ab:Ag complexes with similar interfacial structures. Our method has significant implications toward accelerating biotherapeutic drug discovery as well as drug repurposing research. This method was implemented in MOE 2024 and is available to the scientific community."

Monoclonal antibodies have emerged as key therapeutics. In particular, nanobodies, small, single-domain antibodies that are naturally expressed in camelids, are rapidly gaining momentum following the approval of the first nanobody drug in 2019. Nonetheless, the development of these biologics as therapeutics remains a challenge. Despite the availability of established in vitro directed-evolution technologies that are relatively fast and cheap to deploy, the gold standard for generating therapeutic antibodies remains discovery from animal immunization or patients. Immune-system-derived antibodies tend to have favourable properties in vivo, including long half-life, low reactivity with self-antigens and low toxicity. Here the researchers present AbNatiV, a deep learning tool for assessing the nativeness of antibodies and nanobodies, that is, their likelihood of belonging to the distribution of immune-system-derived human antibodies or camelid nanobodies. AbNatiV is a multipurpose tool that accurately predicts the nativeness of Fv sequences from any source, including synthetic libraries and computational design. It provides an interpretable score that predicts the likelihood of immunogenicity, and a residue-level profile that can guide the engineering of antibodies and nanobodies indistinguishable from immune-system-derived ones. They further introduce an automated humanization pipeline, which they applied to two nanobodies. Laboratory experiments show that AbNatiV-humanized nanobodies retain binding and stability at par or better than their wild type, unlike nanobodies that are humanized using conventional structural and residue-frequency analysis. They have made AbNatiV available as downloadable software and as a webserver. https://lnkd.in/gxNVRDNg

This new AI tool can design antibody drugs 100x faster than traditional methods - without trial and error. It’s called Chai-2. And it might quietly change how we discover drugs. Here’s why: Normally, designing an antibody drug takes months of testing in the lab - just to find one molecule that works. But Chai-2, an AI model built by Chai Discovery, skips that entire process. It watched hours of real-world data and learned to generate antibody candidates from scratch - with atomic-level precision. This new AI tool can design antibody drugs 100x faster - and with zero trial-and-error. In early tests on 52 new antigens, it produced viable hits for 50% of them - in just two weeks. That too without any screening or manual lab trials. The team calls it “Photoshop for molecules.” And the analogy makes sense - it lets researchers design antibodies with programmable control, rather than waiting for randomness to deliver results. This matters for two big reasons: ▶ 1. It’s faster and cheaper Fewer experiments means lower R&D costs and faster GTM. Especially powerful for early-stage biotechs running on tight timelines and capital. ▶ 2. It’s not just about antibodies Chai-2 can design miniproteins, explore new formats, and expand the kinds of molecules we can even consider therapeutically. As a funding coach and investor in healthtech, I see a signal here: → Founders who can compress the drug discovery loop - even slightly - will unlock investor confidence faster. Would you bet on a drug designed by AI if it meant saving months (and millions)? #entrepreneurship #healthtech #innovation