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immunogenicity is a major cause of biologics failure, often identified too late in development. this blog explains how in silico screening helps detect anti-drug antibody (ada) risks early, before costly setbacks. learn how tools like lensᵃⁱ™ enable faster, more informed decision-making by supporting early candidate evaluation, risk mitigation, and regulatory alignment. the impact of immunogenicity in early biologics discovery immunogenicity remains one of the most important and often underappreciated factors in biologics development. for researchers and drug development teams working with monoclonal antibodies or therapeutic proteins, the risk of an unwanted immune response can derail even the most promising candidates. the presence of anti-drug antibodies (adas) doesn’t always show up immediately. in many cases, the problem becomes evident only after significant investment of time and resources, often in later-stage trials. adas can reduce a drug’s effectiveness, alter its pharmacokinetics, or introduce safety risks that make regulatory approval unlikely. some programs have even been discontinued because of immunogenicity-related findings that might have been identified much earlier. to avoid these setbacks, teams are increasingly integrating predictive immunogenicity screening earlier in development. in silico tools now make it possible to evaluate ada risk during the discovery stage, before resources are committed to high-risk candidates. this proactive approach supports smarter design decisions, reduces development delays, and helps safeguard against late-stage failure. in this blog, we’ll explore how in silico immunogenicity screening offers a proactive way to detect potential ada risks earlier in the pipeline. we’ll also look at how tools like biostrand’s lensai platform are helping to simplify and scale these assessments, making immunogenicity screening a practical part of modern biologics development. why early ada risk assessment is critical immune responses to therapeutic proteins can derail even the most carefully designed drug candidates. when the immune system identifies a treatment as foreign, it may trigger the production of anti-drug antibodies (adas). these responses can alter how a drug is distributed in the body, reduce its therapeutic effect, or create safety concerns that weren't apparent during earlier studies. the consequences are often serious delays, added costs, program redesigns, or even full discontinuation. this isn’t something to be considered only when a drug is close to clinical testing. it’s a risk that needs to be addressed from the beginning. regulatory agencies increasingly expect sponsors to demonstrate that immunogenicity has been evaluated in early discovery, not just as a final check before filing. this shift reflects lessons learned from earlier products that failed late because they hadn't been properly screened. early-stage risk assessment allows developers to ask the right questions at the right time. are there t-cell epitopes likely to trigger immune recognition? is the candidate similar enough to self-proteins to escape detection? could minor sequence changes reduce the chances of immunogenicity without compromising function? immunogenicity screening provides actionable insights that can guide sequence optimization well before preclinical testing. for example, identifying epitope clustering or t-cell activation hotspots during discovery enables teams to make targeted modifications in regions such as the variable domain. these adjustments can reduce immunogenicity risk without compromising target binding, helping streamline development and avoid costly rework later in the process. beyond candidate selection, immunogenicity screening improves resource allocation. if a molecule looks risky, there is no need to invest heavily in downstream testing until it has been optimized. it’s a smarter, more strategic way to manage timelines and reduce unnecessary costs. the tools now available make this kind of assessment more accessible than ever. in silico screening platforms, powered by ai and machine learning, can run detailed analyses in a matter of hours. these insights help move projects forward without waiting for expensive and time-consuming lab work. in short, assessing immunogenicity is not just about risk avoidance. it’s about building a better, faster path to clinical success. in silico immunogenicity screening: how it works in silico immunogenicity screening refers to the use of computational models to evaluate the immune risk profile of a biologic candidate. these methods allow development teams to simulate how the immune system might respond to a therapeutic protein, particularly by predicting t-cell epitopes that could trigger anti-drug antibody (ada) formation. the primary focus is often on identifying mhc class ii binding peptides. these are the sequences most likely to be presented by antigen-presenting cells and recognized by helper t cells. if the immune system interprets these peptides as foreign, it can initiate a response that leads to ada generation. unlike traditional in vitro methods, which may require weeks of experimental setup, in silico tools deliver results quickly and at scale. developers can screen entire libraries of protein variants, comparing their immunogenicity profiles before any physical synthesis is done. this flexibility makes in silico screening particularly valuable in the discovery and preclinical stages, where multiple versions of a candidate might still be on the table. the strength of this approach lies in its ability to deliver both breadth and depth. algorithms trained on curated immunology datasets can evaluate binding affinity across a wide panel of human leukocyte antigen (hla) alleles. they can also flag peptide clusters, overlapping epitopes, and areas where modifications may reduce risk. the result is a clearer picture of how a candidate will interact with immune pathways long before preclinical and clinical studies are initiated. for teams juggling tight timelines and complex portfolios, these insights help drive smarter decision-making. high-risk sequences can be deprioritized or redesigned, while low-risk candidates can be advanced with greater confidence. how lensai supports predictive immunogenicity analysis one platform leading the charge in this space is lensai . designed for early-stage r&d, it offers high-throughput analysis with a user-friendly interface, allowing computational biologists, immunologists, and drug developers to assess risks rapidly. here’s how lensai supports smarter decision-making: multi-faceted risk scoring: rather than relying on a single predictor, lensai integrates several immunogenicity markers into one unified score. this includes predicted mhc class ii binding affinity across diverse hla alleles, epitope clustering patterns, and peptide uniqueness compared to self-proteins based on proprietary hyft technology. by combining these distinct factors, the platform provides insight into potential immune activation risk, supporting better-informed candidate selection. reliable risk prediction: lensai composite score reliably classifies candidates by ada risk, using two thresholds to define low risk: <10% and <30% ada risk. this distinction enables more confident go/no-go decisions in early development stages. by combining multiple features into a single score, the platform supports reproducible, interpretable risk assessment that is grounded in immunological relevance. early-stage design support: lensai is accessible from the earliest stages of drug design, without requiring lab inputs or complex configurations, designed for high-throughput screening of whole libraries of sequences in a few hours. researchers can quickly assess sequence variants, compare immunogenicity profiles, and prioritize low-risk candidates before investing in downstream studies. this flexibility supports more efficient resource use and helps reduce the likelihood of late-stage surprises. in a field where speed and accuracy both matter, this kind of screening helps bridge the gap between concept and clinic. it gives researchers the chance to make informed adjustments, rather than discovering late-stage liabilities when there is little room left to maneuver. case study: validating ada risk prediction with lensai in our recent case study, we applied lensai’s immunogenicity composite score to 217 therapeutic antibodies to evaluate predictive accuracy. for predicting ada incidence >10%, the model achieves an auc=0.79, indicating strong discriminative capability (auc=0.8 is excellent). for predicting ada incidence >30%, which is considered as more suitable for early-stage risk assessment purposes than the 10% cut-off, auc rises to 0.92, confirming lensai's value for ada risk classification. read the full case study or contact us to discuss how this applies to your pipeline. regulatory perspectives: immunogenicity is now a front-end issue it wasn’t long ago that immunogenicity testing was seen as something to be done late in development. but regulators have since made it clear that immunogenicity risk must be considered much earlier. agencies like the fda and ema now expect developers to proactively assess and mitigate immune responses well before clinical trials begin. this shift came after a series of high-profile biologic failures where ada responses were only discovered after significant time and money had already been spent. in some cases, the immune response not only reduced drug efficacy but also introduced safety concerns that delayed approval or halted development entirely. today, guidance documents explicitly encourage preclinical immunogenicity assessment. sponsors are expected to show that they have evaluated candidate sequences, made risk-informed design choices, and taken steps to reduce immunogenic potential. in silico screening, particularly when combined with in vitro and in vivo data, provides a valuable layer of evidence in this process. early screening also supports a culture of quality by design. it enables teams to treat immunogenicity not as a regulatory hurdle, but as a standard consideration during candidate selection and development. the regulatory landscape is shifting to support in silico innovation. in april 2025, the fda took a major step by starting to phase out some animal testing requirements for antibody and drug development. instead, developers are encouraged to use new approach methodologies (nams)—like ai models —to improve safety assessments and speed up time to clinic. the role of in silico methods in modern biologics development with the increasing complexity of therapeutic proteins and the diversity of patient populations, traditional testing methods are no longer enough. drug development teams need scalable, predictive tools that can keep up with the speed of discovery and the demand for precision. in silico immunogenicity screening is one of those tools. it has moved from being a theoretical exercise to a standard best practice in many organizations. reducing dependence on reactive testing and allowing early optimization leads these methods to helping companies move forward with greater efficiency and lower risk. when development teams have access to robust computational tools from the outset, the entire process tends to run more efficiently. these tools enable design flexibility, support earlier decision-making, and allow researchers to explore multiple design paths while maintaining alignment with regulatory expectations. for companies managing multiple candidates across different therapeutic areas, this kind of foresight can translate to faster development, fewer setbacks, and ultimately, better outcomes for patients. final thoughts: from screening to smarter development the promise of in silico immunogenicity screening lies in moving risk assessment to the earliest stages of development where it can have the greatest impact. by identifying high-risk sequences before synthesis, it helps researchers reduce late-stage failures, shorten timelines, lower overall project costs, and improve the likelihood of clinical success. in silico tools such as lensai support the early prediction of ada risk by flagging potential immunogenic regions and highlight risk patterns across diverse protein candidates, enabling earlier, more informed design decisions. see how early ada screening could strengthen your next candidate. learn more.

epitope mapping is a fundamental process to identify and characterize the binding sites of antibodies to their target antigens2. understanding these interactions is pivotal in developing diagnostics, vaccines, and therapeutic antibodies3–5. antibody-based therapeutics – which have taken the world by storm over the past decade – all rely on epitope mapping for their discovery, development, and protection. this includes drugs like humira, which reigned as the world’s best-selling drug for six years straight6, and rituximab, the first monoclonal antibody therapy approved by the fda for the treatment of cancer7. aside from its important role in basic research and drug discovery and development, epitope mapping is an important aspect of patent filings; it provides binding site data for therapeutic antibodies and vaccines that can help companies strengthen ip claims and compliance8. a key example is the amgen vs. sanofi case, which highlighted the importance of supporting broad claims like ‘antibodies binding epitope x’ with epitope residue identification at single amino acid resolution, along with sufficient examples of epitope binding8. while traditional epitope mapping approaches have been instrumental in characterizing key antigen-antibody interactions, scientists frequently struggle with time-consuming, costly processes that are limited in scalability and throughput and can cause frustration in even the most seasoned researchers9. the challenge of wet lab-based epitope mapping approaches traditional experimental approaches to epitope mapping include x-ray crystallography and hydrogen-deuterium exchange mass-spectrometry (hdx-ms). while these processes have been invaluable in characterizing important antibodies, their broader application is limited, particularly in high-throughput antibody discovery and development pipelines. x-ray crystallography has long been considered the gold standard of epitope mapping due to its ability to provide atomic-level resolution10. however, this labor-intensive process requires a full lab of equipment, several scientists with specialized skill-sets, months of time, and vast amounts of material just to crystallize a single antibody-antigen complex. structural biology researchers will understand the frustration when, after all this, the crystallization is unsuccessful (yet again), for no other reason than simply because not all antibody-antigen complexes form crystals11. additionally, even if the crystallization process is successful, this technique doesn’t always reliably capture dynamic interactions, limiting its applicability to certain epitopes12. the static snapshots provided by x-ray crystallography mean that it can’t resolve allosteric binding effects, transient interactions, or large/dynamic complexes, and other technical challenges mean that resolving membrane proteins, heterogeneous samples, and glycosylated antigens can also be a challenge. hdx-ms, on the other hand, can be a powerful technique for screening epitope regions involved in binding, with one study demonstrating an accelerated workflow with a success rate of >80%13. yet, it requires highly complex data analysis and specialized expertise and equipment, making it resource-intensive, time-consuming (lasting several weeks), and less accessible for routine use – often leading to further frustration among researchers. as the demand for therapeutic antibodies, vaccines, and diagnostic tools grows, researchers urgently need efficient, reliable, and scalable approaches to accelerate the drug discovery process. in silico epitope mapping is a promising alternative approach that allows researchers to accurately predict antibody-antigen interactions by integrating multiple computational techniques14. advantages of in silico epitope mapping in silico epitope mapping has several key advantages over traditional approaches, making it a beneficial tool for researchers, particularly at the early stage of antibody development. speed – computational epitope mapping methods can rapidly analyze antigen-antibody interactions, reducing prediction time from months to days11. this not only accelerates project timelines but also helps reduce the time and resources spent on unsuccessful experiments. accuracy – by applying advanced algorithms, in silico methods are designed to provide precise and accurate predictions11. continuous improvements in 3d modeling of protein complexes that can be used to support mapping also mean that predictions are becoming more and more accurate, enhancing reliability and success rates9. versatility – in silico approaches are highly flexible and can be applied to a broad range of targets that may otherwise be challenging to characterize, ranging from soluble proteins, multimers, to transmembrane proteins. certain in silico approaches can also overcome the limitations of x-ray crystallography as they can reliably study dynamic and transient interactions12. cost-effectiveness – by reducing the need for expensive reagents, specialized equipment, and labor-intensive experiments, and by cutting timelines down significantly, computational epitope mapping approaches lower the cost of epitope mapping considerably11,15. this makes epitope mapping accessible to more researchers and organizations with limited resources. scalability – in silico platforms can handle huge datasets and screen large numbers of candidates simultaneously, unlike traditional wet-lab methods that are limited by throughput constraints, enabling multi-target epitope mapping9. this is especially advantageous in high-throughput settings, such as immune profiling and drug discovery, and relieves researchers of the burden of processing large volumes of samples daily. ai-powered in silico epitope mapping in action meet lensai: your cloud-based epitope mapping lab imagine a single platform hosting analytical solutions for end-to-end target discovery-leads analysis, including epitope mapping in hours. now, this is all possible. meet lensai – an integrated intelligence platform hosting innovative analytical solutions for complete target-discovery-leads analysis and advanced data harmonization and integration. lensai epitope mapping is one of the platform’s applications that enables researchers to identify the amino acids on the target that are part of the epitope11. by simply inputting the amino acid sequences of antibodies and targets, the machine learning (ml) algorithm, combined with molecular modeling techniques, enables the tool to make a prediction. the outputs are: a sequence-based visualization containing a confidence score for each amino acid of the target, indicating whether that amino acid may be part of the epitope, and a 3d visualization with an indication of the predicted epitope region. lensai: comparable to x-ray crystallography, in a fraction of the time and cost to evaluate the accuracy of lensai epitope mapping, its predictions were compared to the data from a well-known study by dang et al. in this study, epitope mapping using six different well-known wet-lab techniques for epitope mapping were compared, using x-ray crystallography as the gold standard11. by comparing lensai to the epitope structures obtained by x-ray crystallography in this study, it was determined that lensai closely matches x-ray crystallography. the area under the curve (auc) from the receiver operating characteristic (roc) curve was used as a key performance metric to compare the two techniques. the roc curve plots the true positive rate against the false positive rate, providing a robust measure of the prediction’s ability to distinguish between epitope and non-epitope residues. the results demonstrated that lensai achieves consistently high auc values of approximately 0.8 and above, closely matching the precision of x-ray crystallography (figure 1). an auc of 1 would represent a perfect prediction, while an auc of 0.8 and above would be excellent, and 0.5 is not better than random. although the precision of lensai is comparable to that of x-ray crystallography, the time and cost burdens are not; lensai achieves this precision in a fraction of the time and with far fewer resources than those required for successful x-ray crystallography. figure 1. the benchmark comparison with x-ray crystallography and six other methods (peptide array, alanine scan, domain exchange, hydrogen-deuterium exchange, chemical cross-linking, and hydroxyl radical footprinting) for epitope identification in five antibody-antigen combinations the accuracy of lensai was further compared against the epitope mapping data from other widely used wet lab approaches, obtained from the dang et al., study. in this study, peptide array, alanine scan, domain exchange, hdx, chemical cross-linking, and hydroxyl radical footprinting techniques were assessed. to compare lensai with dang’s data, the epitope mapping identified by x-ray crystallography (obtained from the same study) was used as the ground truth. alongside showing near x-ray precision, lensai outperformed all wet lab methods, accurately identifying the true epitope residues (high recall combined with high precision and a low false positive rate). in addition to the high precision and accuracy shown here, lensai enables users to detect the amino acids in the target that are part of the epitope solely through in silico analysis. lensai is, therefore, designed to allow users to gain reliable and precise results, usually within hours to a maximum of 1 day, with the aim of enabling fast epitope mapping and significantly reducing the burden of technically challenging experimental approaches. this means there is no need to produce physical material through lengthy and unpredictable processes, thereby saving time and money and helping to improve the success rate. lensai also works for various target types, including typically challenging targets such as transmembrane proteins and multimers. lensai performs on unseen complexes with high accuracy a new benchmark validation demonstrates that lensai epitope mapping maintains high accuracy even when applied to entirely new antibody-antigen complexes it has never seen before. in this study, the platform accurately predicted binding sites across 17 unseen pairs without prior exposure to the antibodies, antigens, or complexes. the ability to generalize beyond training data shows the robustness of the lensai predictive model. these findings not only support broader applicability but also help reduce lab burden and timelines. you can explore both the new “unseen” case study and the original benchmark on a “seen” target for a side-by-side comparison. new case study: lensai epitope mapping on an “unseen” target[ link] previous case study: head-to-head benchmark on a “seen” target[ link] conclusion as many of us researchers know all too well, traditional wet lab epitope mapping techniques tend to be slow, costly, and not often successful, limiting their applicability and scalability in antibody discovery workflows. however, it doesn’t have to be this way – in silico antibody discovery approaches like lensai offer a faster, cost-effective, and highly scalable alternative. this supports researchers in integrating epitope mapping earlier in the development cycle to gain fine-grained insights, make more informed decisions, and optimize candidates more efficiently. are you ready to accelerate your timelines and improve success rates in antibody discovery? get in touch today to learn more about how lensai can streamline your antibody research. references 1. labmate i. market report: therapeutic monoclonal antibodies in europe. labmate online. accessed march 18, 2025. https://www.labmate-online.com/news/news-and-views/5/frost-sullivan/market-report-therapeutic-monoclonal-antibodies-in-europe/22346 2. mole se. epitope mapping. mol biotechnol. 1994;1(3):277-287. doi:10.1007/bf02921695 3. ahmad ta, eweida ae, sheweita sa. b-cell epitope mapping for the design of vaccines and effective diagnostics. trials vaccinol. 2016;5:71-83. doi:10.1016/j.trivac.2016.04.003 4. agnihotri p, mishra ak, agarwal p, et al. epitope mapping of therapeutic antibodies targeting human lag3. j immunol. 2022;209(8):1586-1594. doi:10.4049/jimmunol.2200309 5. gershoni jm, roitburd-berman a, siman-tov dd, tarnovitski freund n, weiss y. epitope mapping: the first step in developing epitope-based vaccines. biodrugs. 2007;21(3):145-156. doi:10.2165/00063030-200721030-00002 6. biology ©2025 mrc laboratory of molecular, avenue fc, campus cb, cb2 0qh c, uk. 01223 267000. from bench to blockbuster: the story of humira® – best-selling drug in the world. mrc laboratory of molecular biology. accessed march 18, 2025. https://www2.mrc-lmb.cam.ac.uk/news-and-events/lmb-exhibitions/from-bench-to-blockbuster-the-story-of-humira-best-selling-drug-in-the-world/ 7. milestones in cancer research and discovery - nci. january 21, 2015. accessed march 18, 2025. https://www.cancer.gov/research/progress/250-years-milestones 8. deng x, storz u, doranz bj. enhancing antibody patent protection using epitope mapping information. mabs. 2018;10(2):204-209. doi:10.1080/19420862.2017.1402998 9. grewal s, hegde n, yanow sk. integrating machine learning to advance epitope mapping. front immunol. 2024;15:1463931. doi:10.3389/fimmu.2024.1463931 10. toride king m, brooks cl. epitope mapping of antibody-antigen interactions with x-ray crystallography. in: rockberg j, nilvebrant j, eds. epitope mapping protocols. vol 1785. methods in molecular biology. springer new york; 2018:13-27. doi:10.1007/978-1-4939-7841-0_2 11. dang x, guelen l, lutje hulsik d, et al. epitope mapping of monoclonal antibodies: a comprehensive comparison of different technologies. mabs. 2023;15(1):2285285. doi:10.1080/19420862.2023.2285285 12. srivastava a, nagai t, srivastava a, miyashita o, tama f. role of computational methods in going beyond x-ray crystallography to explore protein structure and dynamics. int j mol sci. 2018;19(11):3401. doi:10.3390/ijms19113401 13. zhu s, liuni p, chen t, houy c, wilson dj, james da. epitope screening using hydrogen/deuterium exchange mass spectrometry (hdx‐ms): an accelerated workflow for evaluation of lead monoclonal antibodies. biotechnol j. 2022;17(2):2100358. doi:10.1002/biot.202100358 14. potocnakova l, bhide m, pulzova lb. an introduction to b-cell epitope mapping and in silico epitope prediction. j immunol res. 2016;2016:1-11. doi:10.1155/2016/6760830 15. parvizpour s, pourseif mm, razmara j, rafi ma, omidi y. epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. drug discov today. 2020;25(6):1034-1042. doi:10.1016/j.drudis.2020.03.006

the importance of an integrated end-to-end antibody discovery process drug discovery processes are typically organized in a step-by-step manner – going from target identification to lead optimization processes. this implies that data is being siloed at every process, leading to an exponential loss of quantitative and qualitative insights across the different processes. to realize the full potential of drug discovery, data integration within a data-driven automation platform is essential. the lensai™ foundation ai model powered by hyft technology is designed to solve the challenges behind ai-driven rational drug design, harnessing advanced ai and ml capabilities to navigate the complexities of drug discovery with high precision. by integrating predictive modelling, data analysis and lead optimization functionalities, lensai accelerates the end-to-end discovery and development of promising drug candidates. the lensai system uniquely integrates both structured and unstructured data, serving as a centralized graph for storing, querying, and analyzing diverse datasets, including different omics layers, chemical, and pharmacological information. with lensai, data from every phase of the drug discovery process is no longer siloed but represented as subgraphs within an interconnected graph that summarizes data across all processes. this interconnected approach enables bidirectional and cyclical information flow, allowing for flexibility and iterative refinement. for example, during in-silico lead optimization, challenges may arise regarding pharmacokinetic properties or off-target effects of lead compounds. by leveraging the integrated knowledge graph, we can navigate back to earlier phases to reassess decisions and explore alternative strategies. this holistic view ensures that insights and adjustments can be continuously incorporated throughout the drug discovery process. navigation through integrated knowledge graphs of complex biological data is made possible by the patented hyft technology. hyfts, which are amino acid patterns mined across the biosphere, serve as critical connectors within the knowledge graph by capturing diverse layers of information at both the subsequence and sequence levels. the hyfts encapsulate information about ‘syntax’ (the arrangement of amino acids), as well as ‘structure’ and ‘function,’ and connect this data to textual information at the sentence and concept levels. this hyft-based multi-modal integration ensures that we move beyond mere ‘syntax’ to incorporate ‘biological semantics,’ representing the connection between structure and function. within this single framework, detailed structural information is aligned with relevant textual metadata, providing a comprehensive understanding of biological sequences. exploring textual metadata could be very useful in the target identification stage. for example, to gather detailed information on the target epitopes: “in which species are these epitopes represented?” “can we extract from literature additional information and insights on the epitopes?”. this information can be yielded by querying the knowledge graph and harnessing the benefits of the fine-grained hyft-based approach, capturing information at the ‘subsequence’ level. indeed, at the hyft level, relevant textual concepts (sub-sentence level) are captured, which allows us to identify whether a specific hyft, represented in the target, might reveal relevant epitopes. apart from textual meta-data there is ‘flat’ metadata such as immunogenicity information, germline information, pharmacological data, developability data, and sequence liability presence. at each of the previously mentioned information layers, additional 'vector' data is obtained from various protein large language models (pllms). this means that an embedding is associated with each (sub)-sequence or concept. this allows for 'vector' searches, which, based on the embeddings, can be used to identify similar sequences, enhancing tasks like protein structure prediction and functional annotation. for a deep dive into vector search, see our vector search in text analysis blog here. this capability allows for the extraction of a wider range of features and the uncovering of hidden patterns across all these dimensions. lensai: the importance of embeddings at the sub-sequence level mindwalk lensai’s comprehensive approach in protein analytics is similar to text-based analytics. in text analysis, we refine semantic boundaries by intelligently grouping words to capture textual meaning. similarly, in protein analytics, we strategically group residue tokens (amino acids) to form sequential hyfts. just as words are clustered into synonyms in text analytics, “protein words” are identified and clustered based on their biological function in protein analytics. these “protein words,” when present in different sequences, reveal a conserved function. by leveraging this method, we gain a deeper understanding of the functional conservation across various protein sequences. thus, the lensai platform based on hyft technology analyses proteins at the sub-sequence level focusing on the hyft patterns as well as on the full-sequence level. comparable to natural language, residues might be less relevant and do not contribute to meaning, which, in case of proteins, can be translated into function. therefore, by focusing on hyfts, we obtain a more condensed information representation and noise reduction by excluding the information captured in non-critical regions. in text analysis, we can almost immediately recognize semantic similarity. we recognize sentences similar in meaning, although compiled of different words, because of our natural understanding of synonyms. in protein language to identify ‘functional similarity’, in other words, to distinguish whether two different amino acid patterns (hyfts) might yield the same function, we use a mathematical method i.e. pllms. pllms are transformer-based models that generate an embedding starting from single amino acid residues. depending on the data the pllm is trained on (typically millions of protein sequences), it tries to discover hidden properties by diving into the residue-residue connections (neighboring residues on both short or longer distances). figure 1: mindwalk's method of chunking tokens the dataset and task a pllm were trained on, determine the represented properties, which can vary from one pllm to another pllm. by stacking the embeddings from different large language model (llm) a more complete view on the protein data is generated. furthermore, we can use clustering and vector search algorithms to group sequences that are similar in a broad range of dimensions. protein embeddings are typically generated at the single amino acid level. in contrast, the hyft-based model obtains embeddings from llms at a pattern level, by concatenating residue-level embeddings. these ‘protein words or hyft’ level embeddings can be obtained from several pre-trained llms – varying from antibody-specific llms to more generic pllms. this hyft-based embedding model offers several benefits. first, this approach captures richer and more informative embeddings compared to the single residue level embeddings second, the concatenation of residue-level embeddings allows for preserving sequence-specific patterns, enhancing the ability to identify functional and structural motifs within proteins. lastly, integrating different llms ensures that these embeddings leverage vast amounts of learned biological knowledge, improving the accuracy and robustness of downstream tasks such as protein function prediction and annotation. so, if we want to identify which hyfts are ‘synonyms’, we deploy the hyft-based level embeddings. returning to the language analogy, where ‘apple’ will take a similar place in the embedding space as ‘orange’ or ‘banana’ – because they are all fruits – in protein analytics we are interested in the hyfts that take similar places in the embedding space – because they all perform the same function in a certain context. figure 2: embeddings at the sub-sequence level: concepts versus hyfts as the figure above (fig. 2) illustrates, the word "apple" can have different meanings depending on the context (referring to a phone or a fruit), the sequence hyft ‘vkkpgas’ can also appear in various contexts, representing different protein annotations and classifications. for instance, a specific hyft is found in organisms ranging from bacteria and fungi to human immunoglobulins. consequently, the embeddings for hyft vkkpgas might occupy different positions in the embedding space, reflecting these distinct functional contexts. use case: transforming antibody discovery with integrated vector search in hit expansion analysis in the lensai hit expansion analysis pipeline, outputs from phage-display, b-cell, or hybridoma technologies are combined with a large-scale enriched antibody sequence dataset sequenced by ngs. the primary goal is to expand the number and diversity of potential binders—functional antibodies from the ngs dataset that are closely related to a set of known binders. the data from the ngs repertoire set and the known binders are represented in a multi-modal knowledge graph, incorporating various modalities such as sequence, structure, function, text, and embeddings. this comprehensive representation allows the ngs repertoire set to be queried to identify a diverse set of additional hits by simultaneously exploiting different information levels, such as structural, physiochemical, and pharmacological properties like immunogenicity. a vital component of this multi-modal knowledge graph is the use of vector embeddings, where antibody sequences are represented in multi-dimensional space, enabling sophisticated analysis. these vector embeddings can be derived from different llms. for instance, in the example below, clinical antibodies obtain sequence-level embeddings from an antibody-specific llm, represented in 2d space and colored by their immunogenicity score. this immunogenicity score can be used to filter some of the antibodies, demonstrating how metadata can be utilized to select embedding-based clusters. furthermore, using vector embeddings allows for continuous data enrichment and the integration of latest information into the knowledge graph at every step of the antibody discovery and development cycle, enhancing the overall process. in protein engineering, this continuous data enrichment proves advantageous in various aspects, such as introducing specific mutations aimed at enhancing binding affinity, humanizing proteins, and reducing immunogenicity. this new data is dynamically added to the knowledge graph, ensuring a fully integrated view of all the data throughout the antibody design cycle. these modifications are pivotal in tailoring proteins for therapeutics, ensuring they interact more effectively with their targets while minimizing unwanted immune responses. figure 3. the clinical antibodies obtain sequence-level embeddings from an antibody-specific llm, represented in 2d space and colored by their immunogenicity score (1 indicating high immunogenic) conclusion the lensai platform provides a robust multi-modal approach to optimize antibody discovery and development processes. by solving the integration of sequence, structure, function, textual insights, and vector embeddings, lensai bridges gaps between disparate data sources. the platform enhances feature extraction by leveraging embedding data from various llms, capturing a wide array of biologically relevant 'hidden properties' at the sub-sequence level. this capability ensures a comprehensive exploration of nuanced biological insights, facilitating an integrated data view. by utilizing “vector search”, the platform can efficiently query and analyze these embeddings, enabling the identification of similar sequences and functional motifs across large and complex datasets. this approach not only captures the 'syntax' and 'structure' of amino acid patterns but also integrates 'biological semantics,' thereby providing a holistic understanding of protein functions and interactions. consequently, lensai improves the efficiency of antibody discovery and development from identifying novel targets to optimizations in therapeutic development, such as hit expansion analysis, affinity maturation, humanization, and immunogenicity screening processes. furthermore, lensai enables cyclical enrichment of antibody discovery and development processes by adding and integrating information into a knowledge graph at every step of the development cycle. this continuous enrichment sets a new benchmark for integrated, data-driven approaches in biotechnology, ensuring ongoing improvements and innovations. references: frontiers | many routes to an antibody heavy-chain cdr3: necessary, yet insufficient, for specific binding benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications rosario vitale, leandro a bugnon, emilio luis fenoy, diego h milone, georgina stegmayer, evaluating large language models for annotating proteins, briefings in bioinformatics, volume 25, issue 3, may 2024, bbae177

understanding immunogenicity at its core, immunogenicity refers to the ability of a substance, typically a drug or vaccine, to provoke an immune response within the body. it's the biological equivalent of setting off alarm bells. the stronger the response, the louder these alarms ring. in the case of vaccines, it is required for proper functioning of the vaccine: inducing an immune response and creating immunological memory. however, in the context of therapeutics, and particularly biotherapeutics, an unwanted immune response can potentially reduce the drug's efficacy or even lead to adverse effects. in pharma, the watchful eyes of agencies such as the fda and ema ensure that only the safest and most effective drugs make their way to patients; they require immunogenicity testing data before approving clinical trials and market access. these bodies necessitate stringent immunogenicity testing, especially for biosimilars, where it's essential to demonstrate that the biosimilar product has no increased immunogenicity risk compared to the reference product (1 ema), (2 fda). the interaction between the body's immune system and biologic drugs, such as monoclonal antibodies, can result in unexpected and adverse outcomes. cases have been reported where anti-drug antibodies (ada) led to lower drug levels and therapeutic failures, such as in the use of anti-tnf therapies, where patient immune responses occasionally reduced drug efficacy (3). beyond monoclonal antibodies, other biologic drugs, like enzyme replacement therapies and fusion proteins, also demonstrate variability in patient responses due to immunogenicity. in some instances, enzyme replacement therapies have been less effective because of immune responses that neutralize the therapeutic enzymes. similarly, fusion proteins used in treatments have shown varied efficacy, potentially linked to the formation of adas. the critical nature of immunogenicity testing is underscored by these examples, highlighting its role in ensuring drug safety and efficacy across a broader range of biologic treatments.  the challenge is to know beforehand whether an immune response will develop, ie the immunogenicity of a compound.   a deep dive into immunogenicity assessment of therapeutic antibodies  researchers rely on empirical analyses to comprehend the immune system's intricate interactions with external agents. immunogenicity testing is the lens that magnifies this interaction, revealing the nuances that can determine a drug's success or failure.  empirical analyses in immunogenicity assessments are informative but come with notable limitations. these analyses are often time-consuming, posing challenges to rapid drug development. early-phase clinical testing usually involves small sample sizes, which restricts the broad applicability of the results. pre-clinical tests, typically performed on animals, have limited relevance to human responses, primarily due to small sample sizes and interspecies differences. additionally, in vitro  tests using human materials do not fully encompass the diversity and complexity of the human immune system. moreover, they often require substantial time, resources, and materials. these issues highlight the need for more sophisticated methodologies that integrate human genetic variation for better prediction of drug candidates' efficacy. furthermore, the ability to evaluate the outputs from phage libraries during the discovery stage and optimization strategies like humanizations, developability, and affinity maturation can add significant value. being able to analyzing these strategies' impact on immunogenicity, with novel tools , may enhance the precision of these high throughput methods. . the emergence of in silico in immunogenicity screening with the dawn of the digital age, computational methods have become integral to immunogenicity testing.  in silico testing, grounded in computer simulations, introduces an innovative and less resource-intensive approach. however, it's important to understand that despite their advancements, in silico methods are not entirely predictive. there remains a grey area of uncertainty that can only be fully understood through experimental and clinical testing with actual patients. this underscores the importance of a multifaceted approach that combines computational predictions with empirical experimental and clinical data to comprehensively assess a drug's immunogenicity.   predictive role immunogenicity testing is integral to drug development, serving both retrospective and predictive purposes.  in silico analyses utilizing artificial intelligence and computational models to forecast a drug's behavior within the body can be used both in early and late stages of drug development. these predictions can also guide subsequent in vitro analyses, where the drug's cellular interactions are studied in a controlled laboratory environment. as a final step, traditionally immunogenicity monitoring in patients is crucial for regulatory approval.  the future of drug development envisions an expanded role for in silico testing through the combination with experimental and clinical data, to enhance the accuracy of predictive immunogenicity. this approach aims to refine predictions about a drug's safety and effectiveness before clinical trials, potentially streamlining the drug approval process. by understanding how a drug interacts with the immune system, researchers can anticipate possible reactions, optimize treatment strategies, and monitor patients throughout the process. understanding a drug's potential immunogenicity can inform dosing strategies, patient monitoring, and risk management. for instance, dose adjustments or alternative therapies might be considered if a particular population is likely to develop adas against a drug early on.   traditional vs. in silico methods: a comparative analysis traditional in vitro  methods, despite being time-intensive, offer direct insights from real-world biological interactions. however, it's important to recognize the limitations in the reliability of these methods, especially concerning in vitro wet lab tests used to determine a molecule's immunogenicity in humans. these tests often fall into a grey area in terms of their predictive accuracy for human responses. given this, the potential benefits of in silico analyses become more pronounced. in silico methods can complement traditional approaches by providing additional predictive insights, particularly in the early stages of drug development where empirical data might be limited. this integration of computational analyses can help identify potential immunogenic issues earlier in the drug development process, aiding in the efficient design of subsequent empirical studies. in silico methods, with their rapid processing and efficiency, are ideal for initial screenings, large datasets, and iterative testing. large amounts of hits can already be screened in the discovery stage and repeated when lead candidates are chosen and further engineered. the advantage of in silico methodologies lies in their capacity for high throughput analysis and quick turn-around times. traditional testing methods, while necessary for regulatory approval, present challenges in high throughput analysis due to their reliance on specialized reagents, materials, and equipment. these requirements not only incur substantial costs but also necessitate significant human expertise and logistical arrangements for sample storage. on the other hand, in silico testing, grounded in digital prowess, sees the majority of its costs stemming from software and hardware acquisition, personnel and maintenance.  by employing in silico techniques, it becomes feasible to rapidly screen and eliminate unsuitable drug candidates early in the discovery and development process. this early-stage screening significantly enhances the efficiency of the drug development pipeline by focusing resources and efforts on the most promising candidates. consequently, the real cost-saving potential of in silico analysis emerges from its ability to streamline the candidate selection process, ensuring that only the most viable leads progress to costly traditional testing and clinical trials.   advantages of in silico in immunogenicity screening in silico immunogenicity testing is transforming drug development by offering rapid insights and early triaging, which is instrumental in de-risking the pipeline and reducing attrition costs. these methodologies can convert extensive research timelines into days or hours, vastly accelerating the early stages of drug discovery and validation. as in silico testing minimizes the need for extensive testing of high number of candidates in vitro, its true value lies in its ability to facilitate early-stage decision-making. this early triaging helps identify potential failures before significant investment, thereby lowering the financial risks associated with drug development.   in silico immunogenicity screening in decision-making employing an in silico platform enables researchers to thoroughly investigate the molecular structure, function, and potential interactions of proteins at an early stage. this process aids in the early triaging of drug candidates by identifying subtle variations that could affect therapeutic efficacy or safety. additionally, the insights gleaned from in silico analyses can inform our understanding of how these molecular characteristics may relate to clinical outcomes, enriching the knowledge base from which we draw predictions about a drug's performance in real-world.   de-risking with informed lead nomination the earliest stages of therapeutic development hinge on selecting the right lead candidates—molecules or compounds that exhibit the potential for longevity. making an informed choice at this stage can be the difference between success and failure. in-depth analysis such as immunogenicity analysis aims to validate that selected leads are effective and exhibit a high safety profile. to benefit from the potential and efficiency of in silico methods in drug discovery, it's crucial to choose the right platform to realize these advantages. this is where lensai integrated intelligence technology comes into play. introducing the future of protein analysis and immunogenicity screening: lensai. powered by the revolutionary hyft technology, lensai is not just another tool; it's a game-changer designed for unmatched throughput, lightning-fast speeds, and accuracy. streamline your workflow, achieve better results, and stay ahead in the ever-evolving world of drug discovery. experience the unmatched potency of lensai integrated intelligence technology.   learn more: lensai in silico immunogenicity screening   understanding immunogenicity and its intricacies is fundamental for any researcher in the field. traditional methods, while not entirely predictive, have been the cornerstone of immunogenicity testing. however, the integration of in silico techniques is enhancing the landscape, offering speed and efficiency that complement existing methods. at mindwalk we foresee the future of immunogenicity testing in a synergistic approach that strategically combines in silico with in vitro methods. in silico  immunogenicity prediction can be applied in a high throughput way during the early discovery stages  but also later in the development cycle when engineering lead candidates to provide deeper insights and optimize outcomes. for the modern researcher, employing both traditional and in silico methods is the key to unlocking the next frontier in drug discovery and development. looking ahead, in silico is geared towards becoming a cornerstone for future drug development, paving the way for better therapies.   references: ema guideline on immunogenicity assessment of therapeutic proteins fda guidance for industry immunogenicity assessment for therapeutic protein products anti-tnf therapy and immunogenicity in inflammatory bowel diseases: a translational approach

reproducibility, getting the same results using the original data and analysis strategy, and replicability, is fundamental to valid, credible, and actionable scientific research. without reproducibility, replicability, the ability to confirm research results within different data contexts, becomes moot. a 2016 survey of researchers revealed a consensus that there was a crisis of reproducibility, with most researchers reporting that they failed to reproduce not only the experiments of other scientists (70%) but even their own (>50%). in biomedical research, reproducibility testing is still extremely limited, with some attempts to do so failing to comprehensively or conclusively validate reproducibility and replicability. over the years, there have been several efforts to assess and improve reproducibility in biomedical research. however, there is a new front opening in the reproducibility crisis, this time in ml-based science. according to this study, the increasing adoption of complex ml models is creating widespread data leakage resulting in “severe reproducibility failures,” “wildly overoptimistic conclusions,” and the inability to validate the superior performance of ml models over conventional statistical models. pharmaceutical companies have generally been cautious about accepting published results for a number of reasons, including the lack of scientifically reproducible data. an inability to reproduce and replicate preclinical studies can adversely impact drug development and has also been linked to drug and clinical trial failures. as drug development enters its latest innovation cycle, powered by computational in silico approaches and advanced ai-cadd integrations, reproducibility represents a significant obstacle to converting biomedical research into real-world results. reproducibility in in silico drug discovery the increasing computation of modern scientific research has already resulted in a significant shift with some journals incentivizing authors and providing badges for reproducible research papers. many scientific publications also mandate the publication of all relevant research resources, including code and data. in 2020, elife launched executable research articles (eras) that allowed authors to add live code blocks and computed outputs to create computationally reproducible publications. however, creating a robust reproducibility framework to sustain in silico drug discovery would require more transformative developments across three key dimensions: infrastructure/incentives for reproducibility in computational biology, reproducible ecosystems in research, and reproducible data management. reproducible computational biology this approach to industry-wide transformation envisions a fundamental cultural shift with reproducibility as the fulcrum for all decision-making in biomedical research. the focus is on four key domains. first, creating courses and workshops to expose biomedical students to specific computational skills and real-world biological data analysis problems and impart the skills required to produce reproducible research. second, promoting truly open data sharing, along with all relevant metadata, to encourage larger-scale data reuse. three, leveraging platforms, workflows, and tools that support the open data/code model of reproducible research. and four, promoting, incentivizing, and enforcing reproducibility by adopting fair principles and mandating source code availability. computational reproducibility ecosystem a reproducible ecosystem should enable data and code to be seamlessly archived, shared, and used across multiple projects. computational biologists today have access to a broad range of open-source and commercial resources to ensure their ecosystem generates reproducible research. for instance, data can now be shared across several recognized, domain and discipline-specific public data depositories such as pubchem, cdd vault, etc. public and private code repositories, such as github and gitlab, allow researchers to submit and share code with researchers around the world. and then there are computational reproducibility platforms like code ocean that enable researchers to share, discover, and run code. reproducible data management as per a recent data management and sharing (dms) policy issued by the nih, all applications for funding will have to be accompanied by a dms plan detailing the strategy and budget to manage and share research data. sharing scientific data, the nih points out, accelerates biomedical research discovery through validating research, increasing data access, and promoting data reuse. effective data management is critical to reproducibility and creating a formal data management plan prior to the commencement of a research project helps clarify two key facets of the research: one, key information about experiments, workflows, types, and volumes of data generated, and two, research output format, metadata, storage, and access and sharing policies. the next critical step towards reproducibility is having the right systems to document the process, including data/metadata, methods and code, and version control. for instance, reproducibility in in silico analyses relies extensively on metadata to define scientific concepts as well as the computing environment. in addition, metadata also plays a major role in making data fair. it is therefore important to document experimental and data analysis metadata in an established standard and store it alongside research data. similarly, the ability to track and document datasets as they adapt, reorganize, extend, and evolve across the research lifecycle will be crucial to reproducibility. it is therefore important to version control data so that results can be traced back to the precise subset and version of data. of course, the end game for all of that has to be the sharing of data and code, which is increasingly becoming a prerequisite as well as a voluntarily accepted practice in computational biology. one survey of 188 researchers in computational biology found that those who authored papers were largely satisfied with their ability to carry out key code-sharing tasks such as ensuring good documentation and that the code was running in the correct environment. the average researcher, however, would not commit any more time, effort, or expenditure to share code. plus, there still are certain perceived barriers that need to be addressed before the public archival of biomedical research data and code becomes prevalent. the future of reproducibility in drug discovery a 2014 report from the american association for the advancement of science (aaas) estimated that the u.s. alone spent approximately $28 billion yearly on irreproducible preclinical research. in the future, a set of blockchain-based frameworks may well enable the automated verification of the entire research process. meanwhile, in silico drug discovery has emerged as one of the maturing innovation areas in the pharmaceutical industry. the alliance between pharmaceutical companies and research-intensive universities has been a key component in de-risking drug discovery and enhancing its clinical and commercial success. reproducibility-related improvements and innovations will help move this alliance to a data-driven, ai/ml-based, in silico model of drug discovery.

in 2020, seventeen pharmaceutical companies came together in an alliance called qupharm to explore the potential of quantum computing (qc) technology in addressing real-world life science problems. the simple reason for this early enthusiasm, especially in a sector widely seen as being too slow to embrace technology, is qc’s promise to solve unsolvable problems. the combination of high-performance computing (hpc) and advanced ai more or less represents the cutting-edge of drug discovery today. however, the sheer scale of the drug discovery space can overwhelm even the most advanced hpc resources available today. there are an estimated 1063 potential drug-like molecules in the universe. meanwhile, caffeine, a molecule with just 24 atoms, is the upper limit for conventional hpcs. qc can help bridge this great divide between chemical diversity and conventional computing. in theory, a 300-qubit quantum computer can instantly perform as many calculations as there are atoms in the visible universe (1078-1082). and qc is not all theory, though much of it is still proof-of-concept. just last year, ibm launched a new 433-qubit processor, more than tripling the qubit count in just a year. this march witnessed the deployment of the first quantum computer in the world to be dedicated to healthcare, though the high-profile cafeteria installation was more to position the technology front-and-center for biomedical researchers and physicians. most pharmaceutical majors, including biogen, boehringer ingelheim, roche, pfizer, merck, and janssen, have also launched their own partnerships to explore quantum-inspired applications. if qc is the next digital frontier in pharma r&d, the combination of ai and hpc is currently the principal engine accelerating drug discovery, with in silico drug discovery emerging as a key ai innovation area. computational in silico approaches are increasingly used alongside conventional in vivo and in vitro models to address issues related to the scale, time, and cost of drug discovery. ai, hpc & in silico drug discovery according to gartner, ai is one of the top workloads driving infrastructure decisions. cloud computing provides businesses with cost-effective access to analytics, compute, and storage facilities and enables them to operationalize ai faster and with lower complexity. when it comes to hpcs, data-intensive ai workloads are increasingly being run in the cloud, a market that is growing twice as fast as on-premise hpc. from a purely economic perspective, the cloud can be more expensive than on-premise solutions for workloads that require a large hpc cluster. for some pharma majors, this alone is reason enough to avoid a purely cloud-based hpc approach and instead augment on-premise hpc platforms with the cloud for high-performance workloads. in fact, a hybrid approach seems to be the preferred option for many users with the cloud being used mainly for workload surges rather than for critical production. however, there are several ways in which running ai/ml workloads on cloud hpc systems can streamline in silico drug discovery. in silico drug discovery in the cloud the presence of multiple data silos, the proliferation of proprietary data, and the abundance of redundant/replicated data are some of the biggest challenges currently undermining drug development. at the same time, incoming data volumes are not only growing exponentially but also becoming more heterogeneous as information is generated across different modalities and biological layers. the success of computational drug discovery will depend on the industry’s ability to generate solutions that can scale across an integrated view of all this data. leveraging a unified data cloud as a common foundation for all data and analytics infrastructure can help streamline every stage of the data lifecycle and improve data usage, accessibility, and governance. as ai adoption in the life sciences approaches the tipping point, organizations can no longer afford to have discrete strategies for managing their data clouds and ai clouds. most companies today choose their data cloud platform based on the support available for ai/ml model execution. drug development is a constantly changing process and ai/ml-powered in silico discovery represents a transformative new opportunity in computer-aided drug discovery. meanwhile, ai-driven drug discovery is itself evolving dramatically with the emergence of computationally intensive deep learning models and methodologies that are redefining the boundaries of state-of-the-art computation. in this shifting landscape, a cloud-based platform enables life sciences companies to continuously adapt and upgrade to the latest technologies and capabilities. most importantly, a cloud-first model can help streamline the ai/ml life cycle in drug discovery. data collection for in silico drug discovery covers an extremely wide range, from sequence data to clinical data to real-world data (rwd) to unstructured data from scientific tests. the diverse, distributed nature of pharmaceutical big data often poses significant challenges to data acquisition and integration. the elasticity and scalability of cloud-based data management solutions help streamline access and integrate data more efficiently. in the data preprocessing phase, a cloud-based solution can simplify the development and deployment of end-to-end pipelines/workflows and enhance transparency, reproducibility, and scalability. in addition, several public cloud services offer big data preprocessing and analysis as a service. on-premise solutions are a common approach to model training and validation in ai-driven drug discovery. apart from the up-front capital expenditure and ongoing maintenance costs, this approach can also affect the scalability of the solution across an organization's entire research team, leading to long wait times and loss of productivity. a cloud platform, on the other hand, instantly provides users with just the right amount of resources needed to run their workloads. and finally, ensuring that end users have access to the ai models that have been developed is the most critical phase of the ml lifecycle. apart from the validation and versioning of models, model management and serving has to address several broader requirements, such as resilience and scalability, as well as specific factors, such as access control, privacy, auditability, and governance. most cloud services offer production-grade solutions for serving and publishing ml models. the rise of drug discovery as a service according to a 2022 market report, the increasing usage of cloud-based technologies in the global in-silico drug discovery sector is expected to drive growth at a cagr of nearly 11% between 2021 and 2030, with the saas segment forecast to develop the fastest at the same rate as the broader market. as per another report, the increasing adoption of cloud-based applications and services by pharmaceutical companies is expected to propel ai in the drug discovery market at a cagr of 30% to $2.99 billion by 2026. cloud-based ai-driven drug discovery has well and truly emerged as the current state-of-the-art in pharma r&d. at least until quantum computing and quantum ai are ready for prime time.