The Blog

with biologics emerging as ‘primary engines of value creation’ for bio/pharma companies, the focus has inevitably turned to innovative ai-powered generative and predictive capabilities for targeted, accelerated, and efficient biologics development. the annual pegs boston summit focuses squarely on the intersection between the rapidly growing therapeutic modality of biologics and the latest technological innovations that are advancing its development. naturally, we were at the 2024 event to represent our approaches to innovating biologics development (lensai: empowering diversity-driven discovery, intelligent lead selection and optimization) and to network and exchange ideas with our peers in the industry. here is my big-picture commentary, starting with a few general observations followed by the broad trends and takeaways from the pegs boston summit 2024. first, the event perfectly showcased the key role of technological creativity in continuously advancing antibody design and development and positioning biologics as the next big growth-driver vis-a-vis conventional small molecules. this creativity is slowly but surely expanding to more therapeutic formats including antibody-drug conjugates (adcs), bispecific antibodies (bsabs), and cell-based therapies like chimeric antigen receptor t-cell (car-t). second, the approach to integrating ai/ml technologies seems to be centered on tempering expectations based on more realistic tasks, like advanced sequence liability analysis. there is a large emphasis on lab automation and automated screening procedures with the focus on eventually evolving these building blocks into an integrated in-silico pipeline. and finally, the continuous expansion of ai/ml applications across the entire design and development process is being driven by a strategic understanding of the value of creating unified data + information architecture that enables scalable ai. ben glogoza, client relations, shuji sato, senior director client relations, and arnout van hyfte, head of head of product & platform here, then, are some key trends/takeaways from the event. ai in antibody development: there is heightened interest in using ai for predictive modeling, sequence analysis, and discovery processes. the capabilities of ai-driven methodologies in antibody structural modeling, predicting antibody-antigen interactions, predicting and optimizing antibody developability characteristics, predicting antibody-antigen interactions, etc., are rapidly being integrated into drug development workflows. concurrently, there is an emphasis on the importance of paired data for enhancing model accuracy. next-generation discovery platforms: advanced ai tools and technologies are often being combined with traditional experimental methods to create complementary and comprehensive solutions that enhance the success rate of experimental approaches while mitigating significant challenges such as time, cost, and labor intensity. at the same time, a new generation of ai-first antibody discovery platforms is setting new standards for rapid, cost-effective, and efficient antibody discovery. a proof-of-principle work demonstrated how generative ai can help design antibodies from scratch. today, a global group of well-funded ai-driven antibody discovery startups is focused on innovations across different stages and dimensions of the biologics pipeline, including multispecific antibodies, de novo antibody discovery, full-stack antibody discovery and engineering by integrating a synthetic biology-based high-speed wet lab with machine learning technologies, and integrated in silico - in vitro - in vivo biotherapeutic research. functionality and engineering: detailed insight into molecular properties is unveiling key differences between receptor antagonistic and agonistic antibodies. engineering techniques such as affinity modulation and hinge rigidification are key focus areas. to sum up, the pegs boston summit 2024 was the perfect event to catch up on the cutting edge in biotechnological innovations and biopharmaceutical research. our own relentless focus on continuous innovation has generated significant interest from large healthcare, pharmaceutical, and technology companies. the recent commercial release of lensai api enables biologics researchers with streamlined access to our unique ai-powered capabilities and the power to easily integrate multimodal data from ehrs, company research data, and life science databases. we will, of course, continue to prioritize technological innovation and the rapid commercialization of high-demand, intelligent antibody discovery capabilities.

biopharma companies have traditionally been slow to adopt innovative technologies like ai and the cloud. today, however, digital innovation has become an industry-wide priority with drug development expected to be the most impacted by smart technologies. over the past year, we have looked at drug discovery and development from several different perspectives. no matter the context or frame of reference, the focus inevitably turns to how ai technologies can transform the entire drug discovery and development process, from research to clinical trials. from application-centric to data-centric ai technologies have a range of applications across the drug discovery and development pipeline, from opening up new insights into biological systems and diseases to streamlining drug design to optimizing clinical trials. despite the wide-ranging potential of ai-driven transformation in biopharma, the process does entail some complex challenges. the most fundamental challenge will be to make the transformative shift from an application-centric to a data-centric culture, where data and metadata are operationalized at scale and across the entire drug design and development value chain. however, creating a data-centric culture in drug development comes with its unique set of data-related challenges. to start with there is the sheer scale of data that will require a scalable architecture in order to be efficient and cost-effective. most of this data is often distributed across disparate silos with unique storage practices, quality procedures, and naming and labeling conventions. then there is the issue of different data modalities, from mr or ct scans to unstructured clinical notes, that have to be extracted, transformed, and curated at scale for unified analysis. and finally, the level of regulatory scrutiny on sensitive biomedical data means that there is this constant tension between enabling collaboration and ensuring compliance. therefore, creating a strong data foundation that accounts for all these complexities in biopharma data management and analysis will be critical to ensuring the successful adoption of ai in drug discovery and development. three key requisites for an ai-ready data foundation successful ai adoption in drug development will depend on the creation of a data foundation that addresses these three key requirements. accessibility data accessibility is a key characteristic of ai leaders irrespective of sector. in order to ensure effective and productive data democratization, organizations need to enable access to data distributed across complex technology environments spanning multiple internal and external stakeholders and partners. a key caveat of accessibility is that the data provided should be contextual to the analytical needs of specific data users and consumers. a modern cloud-based and connected enterprise data and ai platform designed as a “one-stop-shop” for all drug design and development-related data products with ready-to-use analytical models will be critical to ensuring broader and deeper data accessibility for all users. data management and governance the quality of any data ecosystem is determined by the data management and governance frameworks that ensure that relevant information is accessible to the right people at the right time. at the same time, these frameworks must also be capable of protecting confidential information, ensuring regulatory compliance, and facilitating the ethical and responsible use of ai. therefore, the key focus of data management and governance will be to consistently ensure the highest quality of data across all systems and platforms as well as full transparency and traceability in the acquisition and application of data. ux and usability successful ai adoption will require a data foundation that streamlines accessibility and prioritizes ux and usability. apart from democratizing access, the emphasis should also be on ensuring that even non-technical users are able to use data effectively and efficiently. different users often consume the same datasets from completely different perspectives. the key, therefore, is to provide a range of tools and features that help every user customize the experience to their specific roles and interests. apart from creating the right data foundation, technology partnerships can also help accelerate the shift from an application-centric to a data-centric approach to ai adoption. in fact, a 2018 gartner report advised organizations to explore vendor offerings as a foundational approach to jump-start their efforts to make productive use of ai. more recently, pharma-technology partnerships have emerged as the fastest-moving model for externalizing innovation in ai-enabled drug discovery. according to a recent roots analysis report on the ai-based drug discovery market, partnership activity in the pharmaceutical industry has grown at a cagr of 50%, between 2015 and 2021, with a majority of the deals focused on research and development. so with that trend as background, here’s a quick look at how a data-centric, full-service biotherapeutic platform can accelerate biopharma’s shift to an ai-first drug discovery model. the lensai™ approach to data-centric drug development our approach to biotherapeutic research places data at the very core of a dynamic network of biological and artificial intelligence technologies. with our lensai platform, we have created a google-like solution for the entire biosphere, organizing it into a multidimensional network of 660 million data objects with multiple layers of information about sequence, syntax, and protein structure. this “one-stop-shop” model enables researchers to seamlessly access all raw sequence data. in addition, hyfts®, our universal framework for organizing all biological data, allows easy, one-click integration of all other research-relevant data from across public and proprietary data repositories. researchers can then leverage the power of the lensai integrated intelligence platform to integrate unstructured data from text-based knowledge sources such as scientific journals, ehrs, clinical notes, etc. here again, researchers have the ability to expand the core knowledge base, containing over 33 million abstracts from the pubmed biomedical literature database, by integrating data from multiple sources and knowledge domains, including proprietary databases. around this multi-source, multi-domain, data-centric core, we have designed next-generation ai technologies that can instantly and concurrently convert these vast volumes of text, sequence, and protein structure data into meaningful knowledge that can transform drug discovery and development.