GENE ONLINE|News &
NVIDIA GTC 2025: BioMap’s xTrimo — The AI Model That’s Changing Biotech and Drug Discovery
At the 2025 NVIDIA GTC conference, BioMap introduced xTrimo, a cross-modal life sciences foundation model. This model covers seven key modalities: DNA, RNA, protein, cells, biological text, and biological systems. By employing GPU-accelerated computing, xTrimo optimizes multi-expert (MOE) large model training at FP8 precision. It uses NVIDIA’s Megatron framework to create a unified multi-modal training platform tailored to life sciences. BioMap also developed a distributed inference engine combining biology and artificial intelligence (AI), greatly enhancing the speed of industrial digital transformation.
But what does that actually mean? In simple terms, xTrimo acts as an AI-powered research assistant that can process vast amounts of biological data across multiple formats—everything from genomic sequencing to drug interactions. It speeds up drug discovery, improves genetic research, and helps scientists understand diseases faster and more accurately.
xTrimo’s Seven Specialized Modalities for Biomedical Tasks
Advances in AI-driven biology aren’t just about academic breakthroughs—they have real-world applications in medicine, pharmaceuticals, and biotech innovation. BioMap’s xTrimo could transform personalized medicine, drug design, and even vaccine development by analyzing biological data at a scale and speed that human researchers simply can’t match.
Each of xTrimo’s seven specialized models is built for a different aspect of life sciences:
- xTrimoDNA: Helps scientists read and interpret long DNA sequences, identifying genetic mutations or complex genomic structures that could be linked to diseases like cancer or rare genetic disorders.
- xTrimoRNA: Focuses on RNA transcription and structure, which is critical for understanding how genes are expressed and for developing RNA-based therapies like mRNA vaccines.
- xTrimoProtein: Aids in protein structure prediction and design, an essential tool in developing new biologic drugs and enzyme-based treatments.
- xTrimoCell: Simulates single-cell behaviors, helping researchers study cell regulation and disease mechanisms in areas like cancer research and regenerative medicine.
- xTrimoChem: Models how small molecules interact with proteins, which is key to drug discovery and designing more effective treatments with fewer side effects.
- xTrimoPPI: Simulates protein-protein interactions, which is crucial in understanding diseases like Alzheimer’s, autoimmune disorders, and viral infections.
- xTrimoSystem: Integrates all these AI-powered insights into a high-throughput validation system, allowing scientists to test hypotheses in genomics, immunology, and beyond.
Bio LLM: A Specialized Bio Model for Biomedical Applications
During the presentation, BioMap’s Vice President, Zhang Xiaoming, explained the distinctions between Bio LLM and traditional large language models (LLMs). While both models share computational requirements, their data, verification processes, and applications differ significantly. For Bio LLM, wet-lab experiments are essential for validating the accuracy and reliability of the computational models. The model must specialize in biological mechanisms, using biomedical omics data instead of general natural language text as the core training material.
In application, these specialized models are more suited to biomedical tasks such as drug design. BioMap emphasizes the importance of structuring Bio LLM models for biological mechanisms to ensure accurate predictions and insights.
Heterogeneous Computing in Protein Design
Zhang also highlighted the importance of considering global efficiency in heterogeneous computing. Protein design requires considering multiple processes together, such as multiple sequence alignment (MSA), affinity, and expression levels. Optimizing these interconnected steps ensures that the overall workflow is efficient and effective.
He stressed that AI-generated models’ precision and efficiency significantly impact subsequent stages of the design process. Visualizing results and automating high-throughput tasks are crucial for streamlining workflows. Through iterative model development and verification, researchers can develop reliable models for complex tasks, such as high-performance protein structure prediction. In one example, the time needed for designing small proteins dropped from 10.8 years to just 13 days. This approach illustrates how advancements in AI and bioinformatics are pushing the boundaries of biomedical research and accelerating the pace of scientific discovery.
AI in Biotech: The Competitive Landscape
xTrimo isn’t the only AI system tackling biotech challenges, but it offers one of the most comprehensive multi-modal approaches. Competitors like DeepMind’s AlphaFold have already revolutionized protein folding predictions, while companies like Insilico Medicine and Recursion Pharmaceuticals use AI for small-molecule drug discovery.
However, most existing AI models focus on single tasks—such as protein structure prediction or small-molecule interactions—while xTrimo integrates multiple biological data types into one system. This cross-modal capability makes it a game-changer for biotech companies looking for a one-stop AI solution.
The market for AI-driven drug discovery is booming. Analysts estimate the AI in healthcare industry will grow exponentially over the next few years, driven by technological advancements and an increasing demand for efficient healthcare solutions. According to a report by Grand View Research, the market was valued at approximately $19.27 billion in 2023 and is projected to reach $187.7 billion by 2030, reflecting a compound annual growth rate (CAGR) of 38.5% from 2024 to 2030. Similarly, MarketsandMarkets forecasts that the AI in healthcare market will grow from $10.31 billion in 2023 to $164.16 billion by 2030, with a CAGR of 49.1% during the forecast period. These projections highlight the significant expansion anticipated in the AI healthcare sector over the next decade.
With pharmaceutical giants and startups racing to leverage AI, models like xTrimo could shorten drug development timelines from years to months, saving companies billions and getting life-saving treatments to patients faster.
The Next Frontier: AI Meets Wet Lab Validation
One of the biggest challenges in AI-driven biotech is validation—just because a model predicts something in silico (on a computer) doesn’t mean it works in real life. BioMap emphasizes wet-lab validation, testing AI predictions through actual biological and chemical experiments.
In protein design, a model might suggest a structure that looks perfect computationally—but if the protein can’t be synthesized or doesn’t function as expected in the lab, researchers restart. xTrimo integrates AI predictions with high-throughput lab testing to deliver faster, more reliable results.
A Faster Future for Drug Discovery and Precision Medicine
AI is changing the way scientists approach medicine, from designing better drugs to diagnosing diseases earlier. xTrimo’s ability to analyze massive biological datasets with precision could lead to breakthroughs in cancer treatments, rare disease therapies, and even AI-driven regenerative medicine.
As biotech and AI continue to converge, tools like xTrimo will play a critical role in shaping the next generation of medical innovations. Whether it’s helping pharma companies design better drugs or enabling hospitals to develop personalized treatment plans, AI-powered life science models are here to stay. With competition heating up in the AI-driven biotech space, the race to revolutionize healthcare is just beginning.
Related posts:
Eli Lilly Joins Forces with AI Startup Genetic Leap in $409M Deal Centered Around RNA-Targeted Drug Discovery
AI-Powered Drug Development: How NVIDIA Builds a Balance Between Biotech and TechBio
First-of-its-kind AI Model Uses Brain Scans to Reveal Link Between Aging and Cognitive Decline
Stanford Uncovers Nature’s Ozempic — Same Weight Loss, No Side Effects
