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- 360-Degree Small-Molecule Developability Profiling from a Single SMILES String in Seconds by characterizing and calculating 99 critical molecular features<\/i><\/li>\n
- Capabilities are Fully Integrated into withZeta.ai\u00ae<\/sup> for Rare Cancer Drug Development<\/i><\/li>\n
- Seeking Early Collaborations with Pharma Partners to Integrate Molecular Intelligence into Discovery Workflows<\/i><\/li>\n<\/ul>\n
DALLAS–(BUSINESS WIRE<\/a>)–Lantern Pharma Inc. (NASDAQ: LTRN)<\/b>, a clinical-stage biotechnology company advancing precision oncology through its proprietary RADR\u00ae<\/sup> AI platform, today announced the expansion of predictBBB.ai into a Large Quantitative Model (LQM)<\/b> \u2014 a real-time quantitative intelligence engine now accessible to any researcher or drug development team, anywhere in the world, through a single browser interface.<\/p>\n
\nFor the first time, pharmaceutical scientists and medicinal chemists can access a unified, benchmark-validated LQM \u2014 not as a batch software installation, not as a siloed cheminformatics tool \u2014 but as an on-demand molecular intelligence engine delivered as a web service. By submitting a single SMILES string, users receive a comprehensive, 360-degree developability profile of any small molecule in seconds: a capability that previously required hours of computation across multiple platforms and specialized bioinformatics personnel to integrate \u2014 removing the computational barriers that have historically limited rigorous molecular characterization to large, well-resourced pharmaceutical organizations, and making that same analytical power available to biotech innovators, academic drug hunters, and rare disease programs worldwide.<\/p>\n
What Is a Large Quantitative Model \u2014 and Why Does It Matter?<\/b><\/p>\n
\nUnlike Large Language Models trained on patterns in text, a Large Quantitative Model is trained on the quantitative language of science itself. The predictBBB\u2122 LQM was built on thousands of characterized drug candidates and small molecules, with their physicochemical properties (thousands of properties or features for each molecule) encoded as vector representations and molecular fingerprints<\/b>. This architecture enables simultaneous, high-dimensional property prediction at computational speeds orders of magnitude faster than conventional cheminformatics workflows \u2014 while preserving the scientific rigor of chemistry-grounded modeling.<\/p>\n
\nThe result is a system that does not approximate molecular behavior from literature patterns. It calculates it, in real time, from the underlying quantitative structure of matter \u2014 capable of screening tens of thousands of molecules per day<\/b> through a single, unified web interface. No installation. No data integration overhead. No specialized infrastructure required.<\/p>\n
Benchmark-Validated Performance \u2014 and a Continued Commitment to Improvement<\/b><\/p>\n
\nLantern\u2019s BBB permeability algorithms were contributed to the Therapeutic Data Commons (TDC)<\/b> leaderboard \u2014 one of the most rigorous open benchmarking platforms in computational drug discovery \u2014 where five of Lantern\u2019s algorithms rank among the top 12 by accuracy and predictive performance<\/b>. Since that contribution, Lantern has continued to refine and improve the underlying models, with performance advances beyond what is currently reflected on the public leaderboard. The TDC ranking represents a validated baseline \u2014 the current state of the platform exceeds it.<\/p>\n
A Unified Molecular Intelligence Panel \u2014 Delivered in Seconds<\/b><\/p>\n
\nThe predictBBB\u2122 LQM expands far beyond its origins as a blood-brain barrier permeability predictor. By submitting a single SMILES string through the web interface, researchers receive an exhaustive molecular profile across four integrated dimensions:<\/p>\n
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- Physicochemical Overview: <\/b>Instant calculation of lipophilicity (logP), polar surface area (TPSA), molecular weight, and ionization state \u2014 the properties that determine whether a molecule can reach its target, survive in circulation, and penetrate the right biological membranes.\n<\/li>\n
- Comprehensive Drug-Likeness: <\/b>An integrated scoring panel combining Lipinski\u2019s Rule of Five with four additional developability assessments \u2014 flagging bioavailability liabilities, metabolic vulnerabilities, and toxicity risk before a molecule ever reaches the lab.\n<\/li>\n
- Structural Architecture: <\/b>Real-time calculation of 25 molecular descriptors covering electronic distribution and functional group composition \u2014 enabling chemists to identify which parts of a molecule will be metabolically degraded and to engineer those weaknesses out at the design stage.\n<\/li>\n
- Topological Mapping: <\/b>Quantification of molecular shape, size, and branching complexity to support Structure-Activity Relationship (SAR) modeling \u2014 ensuring a candidate\u2019s three-dimensional architecture is optimized to fit its intended protein target.\n<\/li>\n<\/ul>\n
Beyond the BBB: A Universal Engine for All of Small-Molecule Drug Discovery<\/b><\/p>\n
The name predictBBB.ai reflects where this platform was born \u2014 but not the boundaries of what it can do.<\/i><\/p>\n
\nWhile CNS drug development demands exceptionally tight physicochemical control for blood-brain barrier penetration, the underlying calculations are the fundamental parameters of all small-molecule drug design. For non-CNS programs, the platform\u2019s utility is arguably broader: the acceptable physicochemical envelope for peripheral targets is wider, and the ability to predict oral bioavailability, P-glycoprotein efflux, intestinal absorption, and hepatic metabolic clearance in real time is equally critical across oncology, cardiovascular medicine, and rare disease indications.<\/p>\n
\nThe platform\u2019s transporter models \u2014 including P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) predictions \u2014 carry particular strategic value in oncology, where tumor overexpression of efflux pumps is a well-established driver of chemotherapeutic resistance. Identifying substrate liability at the molecular design stage, before physical synthesis, is a material competitive advantage that the predictBBB\u2122 web service now makes accessible without infrastructure barriers.<\/p>\n
This positions predictBBB.ai as a universal early-stage decision platform<\/b> \u2014 applicable from CNS programs to kinase inhibitors in oncology to novel chemical entities for rare disease indications \u2014 and as the first web-native molecular intelligence service of its class grounded in independently benchmarked, quantitative model performance.<\/p>\n
\u201cpredictBBB.ai began as a CNS permeability predictor \u2014 what it has evolved into is a quantitative intelligence engine that speaks the universal language of medicinal chemistry, now accessible to any drug developer in the world as a web service. Five of our core algorithms rank among the top 12 on the Therapeutic Data Commons leaderboard \u2014 that is a measurable scientific standard, not a marketing claim \u2014 and our team has continued to advance the platform well beyond that baseline. The earliest decisions in a program are the most consequential ones, and we are inviting partners to integrate this capability and work with us to redefine what rational drug design looks like at scale. Developing and making available ground-breaking computational and AI tools for drug development has the potential to introduce new therapies and cures for patients at a velocity that is needed in medicine.\u201d<\/i><\/p>\n
\u2014 Panna Sharma, Chief Executive Officer, Lantern Pharma Inc.<\/b><\/p>\n
Strategic Integration with withZeta.ai<\/b><\/p>\n
\nThe predictBBB LQM is fully integrated into Lantern\u2019s withZeta.ai\u00ae<\/sup> multi-agentic AI co-scientist platform, where it directly addresses a persistent challenge in rare oncology: the absence of large historical datasets makes rational, fail-fast molecular design not a preference but a necessity. Within withZeta.ai, the platform enables Lantern and its partners to architect candidates optimized simultaneously for target potency and pharmacokinetic viability \u2014 compressing analytical workflows that previously required days of iterative computation into seconds, and embedding that intelligence directly into the broader co-scientist ecosystem.<\/p>\n
Part of a Growing Portfolio of Commercially Leverageable AI Assets<\/b><\/p>\n
\nThe expanded predictBBB LQM represents the latest addition to Lantern Pharma\u2019s growing portfolio of proprietary AI technologies developed for drug discovery \u2014 a portfolio that includes the RADR\u00ae<\/sup> genomic intelligence platform and the withZeta.ai\u00ae<\/sup> multi-agentic co-scientist ecosystem. Consistent with Lantern\u2019s strategy of building AI assets with both internal pipeline and external commercial value, the Company intends to leverage and monetize these capabilities through partnerships, licensing arrangements, and direct platform access \u2014 creating potential revenue streams that complement its clinical development programs.<\/p>\n
Collaboration Opportunities and a Subscription-Based Roadmap<\/b><\/p>\n
\nLantern Pharma is actively inviting pharmaceutical companies and biotech innovators to explore early integration of the predictBBB\u2122 LQM web service into their existing discovery workflows. The platform is particularly well-suited for organizations with active small-molecule programs in oncology, CNS, or rare disease seeking to compress hit-to-lead and lead optimization cycle times, reduce dependence on fragmented computational infrastructure, and implement a rigorous, benchmark-validated developability screen at the earliest stages of candidate selection. API-level access for enterprise integration into partner computational pipelines is in active development. Interested organizations are invited to contact Lantern\u2019s business development team to initiate a conversation.<\/p>\n
\nThis release marks an early milestone in a broader product roadmap for predictBBB.ai. Additional molecular intelligence features and predictive models are planned for integration in the coming months, with the platform expected to launch as part of a subscription-based service for scientists and drug developers globally<\/b>. Lantern intends for this service to be accessible to the full spectrum of the drug development community \u2014 from individual medicinal chemists and academic researchers to enterprise pharmaceutical teams \u2014 establishing predictBBB.ai as an ongoing, commercially scalable AI platform asset.<\/p>\n
About predictBBB.ai<\/b><\/p>\n
\npredictBBB.ai is an AI-powered molecular developability web service developed by Lantern Pharma \u2014 the first platform of its class to deliver real-time, unified physicochemical and structural characterization of small molecules through a browser-accessible interface, without specialized infrastructure requirements. Capable of processing tens of thousands of compounds per day, the platform\u2019s core algorithms are independently benchmarked on the Therapeutic Data Commons (TDC) leaderboard, with five algorithms ranking among the top 12 for BBB permeability prediction by accuracy and performance. Additional features and predictive models are planned for integration in the coming months as part of a broader subscription-based service launch. predictBBB.ai is fully integrated within the withZeta.ai\u00ae<\/sup> multi-agentic co-scientist ecosystem.<\/p>\n
About Lantern Pharma Inc.<\/b><\/p>\n
\nLantern Pharma Inc. (NASDAQ: LTRN) is a clinical-stage biotechnology company using its proprietary RADR\u00ae<\/sup> AI platform and withZeta.ai\u00ae<\/sup> multi-agentic AI co-scientist to transform the speed, cost, and precision of oncology drug development. Lantern\u2019s pipeline includes LP-184, LP-284, LP-300 (HARMONIC trial), and STAR-001, developed through its wholly owned CNS subsidiary, Starlight Therapeutics. For more information, visit www.lanternpharma.com<\/a>.<\/p>\n
Forward-looking Statements<\/b><\/p>\n
This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These forward-looking statements include, among other things, statements relating to: future events or our future financial performance; the potential advantages of our RADR\u00ae platform and withZeta.ai platform in identifying drug candidates, accelerating drug development, and generating revenue through software licensing and subscription models; our strategic plans to advance the development of our drug candidates and antibody drug conjugate (ADC) development program; the planned commercialization of our AI platforms including withZeta.ai and the expected market opportunity for AI co-scientist platforms; estimates regarding the development timing for our drug candidates and ADC development program; expectations and estimates regarding clinical trial timing and patient enrollment; our research and development efforts of our internal drug discovery programs and the utilization of our RADR\u00ae platform to streamline the drug development process; our intention to leverage artificial intelligence, machine learning and genomic data to streamline and transform the pace, risk and cost of oncology drug discovery and development and to identify patient populations that would likely respond to a drug candidate; estimates regarding patient populations, potential markets and potential market sizes; sales estimates for our drug candidates and our plans to discover and develop drug candidates and to maximize their commercial potential by advancing such drug candidates ourselves or in collaboration with others.<\/i><\/p>\n
Any statements that are not statements of historical fact (including, without limitation, statements that use words such as \u201canticipate,\u201d \u201cbelieve,\u201d \u201ccontemplate,\u201d \u201ccould,\u201d \u201cestimate,\u201d \u201cexpect,\u201d \u201cintend,\u201d \u201cseek,\u201d \u201cmay,\u201d \u201cmight,\u201d \u201cplan,\u201d \u201cpotential,\u201d \u201cpredict,\u201d \u201cproject,\u201d \u201ctarget,\u201d \u201cmodel,\u201d \u201cobjective,\u201d \u201caim,\u201d \u201cupcoming,\u201d \u201cshould,\u201d \u201cwill,\u201d \u201cwould,\u201d or the negative of these words or other similar expressions) should be considered forward-looking statements. There are a number of important factors that could cause our actual results to differ materially from those indicated by the forward-looking statements, such as (i) the risk that we may not be able to secure sufficient future funding when needed and as required to advance and support our existing and planned clinical trials and operations, (ii) the risk that observations in preclinical studies and early or preliminary observations in clinical studies do not ensure that later observations, studies and development will be consistent or successful, (iii) the risk that our research and the research of our collaborators may not be successful, (iv) the risk that we may not be successful in licensing potential candidates or in completing potential partnerships and collaborations, (v) the risk that none of our product candidates has received FDA marketing approval, and we may not be able to successfully initiate, conduct, or conclude clinical testing for or obtain marketing approval for our product candidates, (vi) the risk that no drug product based on our proprietary RADR\u00ae AI platform has received FDA marketing approval or otherwise been incorporated into a commercial product, (vii) the risk that our AI platform commercialization efforts, including withZeta.ai, may not generate the anticipated revenue or achieve the expected market adoption, and (viii) those other factors set forth in the Risk Factors section in our Annual Report on Form 10-K for the year ended December 31, 2025, filed with the Securities and Exchange Commission on March 30, 2026.<\/i><\/p>\n
You may access our Annual Report on Form 10-K for the year ended December 31, 2025 under the investor SEC filings tab of our website at <\/i>www.lanternpharma.com<\/i><\/a> or on the SEC\u2019s website at <\/i>www.sec.gov<\/i><\/a>. Given these risks and uncertainties, we can give no assurances that our forward-looking statements will prove to be accurate, or that any other results or events projected or contemplated by our forward-looking statements will in fact occur, and we caution investors not to place undue reliance on these statements. All forward-looking statements in this press release represent our judgment as of the date hereof, and, except as otherwise required by law, we disclaim any obligation to update any forward-looking statements to conform the statement to actual results or changes in our expectations.<\/i><\/p>\n
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View source version on businesswire.com: <\/span>https:\/\/www.businesswire.com\/news\/home\/20260429726045\/en\/<\/a><\/span><\/p>\n
- Comprehensive Drug-Likeness: <\/b>An integrated scoring panel combining Lipinski\u2019s Rule of Five with four additional developability assessments \u2014 flagging bioavailability liabilities, metabolic vulnerabilities, and toxicity risk before a molecule ever reaches the lab.\n<\/li>\n
- Capabilities are Fully Integrated into withZeta.ai\u00ae<\/sup> for Rare Cancer Drug Development<\/i><\/li>\n
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