We apply AI-driven analysis to interpret the complexity of disease with precision, establishing a foundation for informed decision-making in early drug discovery.
MOGAM's Early Discovery platform integrates multi-omics and large-scale clinical data to precisely characterize disease mechanisms and quantitatively identify promising therapeutic targets. This AI-powered infrastructure facilitates translational research, ensuring that every discovery is grounded in potential clinical applications. We support data-driven decision-making across the entire spectrum, from target validation to lead discovery and preclinical strategy. Our AI framework unifies the understanding of disease mechanisms, target identification, and clinical application. With this powerful framework, we systematically design and validate development strategies to maximize the probability of success from the earliest stages of drug discovery.
AI-driven Drug Target Discovery
Leveraging multi-omics data and AI-driven predictive models, we identify novel therapeutic targets and prioritize drug candidates by integrating patient and clinical data to ensure seamless clinical translation.
System-level understanding of Disease Mechanism
We perform integrated analysis of large-scale multi-omics data to quantitatively characterize molecular mechanisms and system-level changes in disease. By linking these insights with clinical phenotypes and patient subtypes, we provide a rigorous scientific foundation for establishing effective therapeutic strategies.
Neoantigen Discovery
In oncology, for example, our approach is applied to identify patient-specific neoantigens, supporting the development of immune therapies through immunogenicity prediction and clinical integration.
New Therapeutic Modalities
New Therapeutic Modalities
New Therapeutic Modalities
Through AI-driven design, we optimize leads across diverse therapeutic modalities.
We apply AI-driven design to precisely optimize lead candidates across diverse therapeutic modalities. At the lead screening and optimization phase, our platform integrates chemical and biological data to generate novel candidates and refine their performance. By combining generative models with quantitative evaluation algorithms, we expand into design spaces that are difficult to explore through conventional experimental approaches. This enables the rapid discovery of promising lead structures while considering key development criteria such as efficacy, safety, and synthetic feasibility. This approach reduces uncertainty and iterative trial-and-error in the early discovery phase, supporting the efficient delivery of high-quality lead compounds with strong potential for clinical translation.
mRNA Sequence Optimization
Across the broad sequence space of the 5'UTR–CDS–3'UTR regions of mRNA, we explore and design mRNA sequences with enhanced translational efficiency, stability, and immune response characteristics. Expanding beyond conventional models, we develop next-generation mRNA optimization approaches using deep learning architectures. This work supports an integrated generative platform spanning mRNA's CDS and UTR regions, 5' cap, and poly(A) tail for mRNA vaccine and therapeutic development.
mRNA-LNP Co-Design AI
We forecast and calibrate the essential attributes of lipid nanoparticles (LNP), encompassing composition, physicochemical traits, and transport efficiency, which are vital to the efficacy of mRNA therapeutics. Our co-design framework accounts for the intricate interplay between mRNA sequences and LNP formulations, facilitating the discovery of breakthrough LNP combinations with enhanced tissue-specific targeting and structural integrity. By conceptualizing the mRNA and LNP complex as a singular, integrated design space, this methodology diminishes the necessity for repetitive benchwork and promotes holistic, system-level optimization.
Antibody Engineering AI
We assess and optimize the critical developability profiles of antibodies, encompassing binding affinity, specificity, stability, and expression, by utilizing both sequence data and structural insights. Through the integration of deep learning-powered CDR design with structural refinement and immunogenicity reduction, we streamline the generation of high-caliber antibody candidates. Furthermore, we sophisticatedly simulate antigen-antibody interaction to enhance the speed and precision of the entire antibody engineering process.
Small Molecule & New Modality Generation
We architect and evolve therapeutic candidates across a spectrum of modalities, ranging from small molecules to PROTACs and RNA binders, by deploying diffusion models, reinforcement learning, and LLM-based generative engines. Through the simultaneous evaluation of activity, toxicity, physicochemical traits, and synthetic viability, we traverse vast domains of chemical space that are often inaccessible through standard experimental methods. This facilitates the identification of pioneering scaffolds and novel structures. Ultimately, our approach catalyzes the enrichment of therapeutic pipelines with unprecedented mechanisms of action (MoA), pushing past the constraints of conventional chemistry-based methods.
Translational Research
Translational Research
Translational Research
Through an integrated workflow, we accelerate the translation of preclinical research into clinical development.
We accelerate the transition from preclinical research to clinical development through a cohesive, unified workflow. Our translational research platform harmonizes reliable ADMET prediction, optimized molecular design, and biomarker identification, bridging experimental datasets and AI-powered analytics. By synthesizing molecular properties, structural data, and patient-derived indicators, we conduct rigorous quantitative risk assessments to facilitate prompt and precise Go/No-Go decisions for clinical advancement.
Reliable ADMET
We apply advanced uncertainty quantification methods within an integrated predictive architecture combining LLM-based and graph-based models, leveraging complementary multi-view representations of molecular and biological data. Approaches such as conformal prediction enable calibrated confidence estimates, delivering ADMET profiling. This framework allows quantitative management of predictive uncertainty during candidate evaluation, supporting more accurate and reliable decision-making while reducing risk throughout drug development.
Molecular Design and Optimization
We assemble and fine-tune molecular frameworks by leveraging generative AI engines rooted in LLMs and diffusion-based models. These computational models internalize fundamental chemical patterns, producing original candidates tailored with specific therapeutic attributes. Through the application of reinforcement learning, we expedite the refinement process toward predefined goals, encompassing potency, pharmacokinetics, and safety, which ultimately enhances the overall productivity of candidate discovery.
Biomarker Discovery
We discover clinically relevant biomarkers by integrating multi-omics datasets, longitudinal clinical records, and real-world patient evidence. Our AI enables precise disease diagnosis, prognosis, and therapeutic response evaluation. By establishing a robust framework for patient stratification, we guide the development of tailored treatment strategies, including the advancement of companion diagnostics (CDx) to realize precision medicine.
Reverse Translational Research
Reverse Translational Research
Reverse Translational Research
We transform patient data into mechanistic insights that bridge research and care.
Reverse translational research begins with clinical patient data to uncover disease mechanisms and pinpoint novel therapeutic targets. We interrogate electronic medical records (EMR) and diverse clinical datasets to extract innovative research hypotheses from observed patient symptoms and treatment outcomes. These insights are subsequently channeled back into the early drug discovery pipeline, fostering more rigorous and data-informed development strategies.
EMR & Multi-Omics Integration
We integrate EMR with multi-omics datasets across genomics, transcriptomics, proteomics, and metabolomics to deconstruct the fundamental biological drivers of disease. This multidimensional approach is pivotal for uncovering novel mechanisms, advancing biomarker discovery, and accelerating therapeutic innovation. By modeling diverse omics layers with AI-powered analytics, we extract profound biological insights, sharpen patient stratification, and catalyze the identification of disease-associated genetic and multi-omics signatures.
Multi-Agent AI
In the evolving healthcare AI landscape, multi-agent systems are gaining prominence for their ability to extract and interpret complex insights from vast EMR and multi-omics datasets. These systems function through the seamless collaboration of specialized autonomous agents, each dedicated to roles such as data acquisition, diagnostic support, and risk forecasting. MOGAM is pioneering a multi-agent AI framework to orchestrate the entire research process, from EMR standardization and multi-omics profiling to mechanistic discovery and prognostic prediction. This sophisticated architecture ensures the structured management of clinical data while enabling the rigorous validation of research hypotheses.