Ginkgo Bioworks Partners with Inductive Bio and Tangible Scientific to Launch AI-Driven Drug Discovery Platform
核心洞察
Ginkgo Bioworks announced a strategic partnership with Inductive Bio (搜索) and Tangible Scientific (搜索) to make AI-driven lab-in-the-loop drug discovery broadly accessible across the pharmaceutical industry.
The collaboration integrates three core capabilities: Ginkgo Datapoints' rapid ADME (搜索) profiling, Inductive Bio (搜索)'s model-driven chemistry design platform, and Tangible Scientific (搜索)'s API-enabled compound management platform.
The partnership aims to reduce time and uncertainty in early-stage drug development by moving from in-silico compound designs to validated experimental results in one digitally-enabled workstream.
Ginkgo Bioworks (NYSE: DNA) announced on August 20, 2025, that its Datapoints offering has entered into a strategic partnership with Inductive Bio (搜索) and Tangible Scientific (搜索) to make AI-driven lab-in-the-loop drug discovery broadly accessible across the pharmaceutical industry. The collaboration aims to deliver capabilities that previously required massive platform investments by combining high-throughput experimental workflows, streamlined compound management, and predictive chemistry AI models into a seamless integrated service.
Addressing Industry Fragmentation
The partnership addresses a critical challenge in modern drug discovery, which has evolved far beyond single-facility operations into a global logistics challenge. According to John Androsavich, General Manager at Ginkgo Datapoints, "The image of scientists sketching molecules on a whiteboard and then walking down the hall to synthesize and test them is long outdated." The current process often involves moving compounds across vendors and continents, introducing delays and creating coordination bottlenecks that make it harder to take advantage of AI-driven design and lab-in-the-loop workflows.
Integrated Platform Capabilities
The new collaboration connects three core capabilities designed to streamline the drug discovery process:
Ginkgo Datapoints' ADME (搜索) Profiling: Building on its earlier announcement of cost-competitive, fast-turnaround ADME services, Ginkgo delivers high-quality readouts including microsomal stability, kinetic solubility, P450 inhibition, and permeability, with optional in vitro toxicity readouts on both lead candidates and library-scale compound sets. The services benefit from accelerated US-based logistics local to Boston's biopharma hub, in-house data infrastructure, and lab automation capabilities.
Tangible Scientific (搜索)'s Compound Management: Strategically located near Boston, Tangible Scientific orchestrates the secure storage, handling, and rapid movement of customer compounds, with delivery to local service providers including Ginkgo. The company's tech-enabled platform unifies compounds, data, and decision-making into a single, automated pipeline, allowing researchers to submit orders for Ginkgo's ADME (搜索) services directly from their digital environments and receive structured, metadata-rich assay results in days rather than weeks.
Inductive Bio (搜索)'s AI Platform: The Compass platform allows chemists to explore and rank design ideas using fine-tuned ADMET (搜索) prediction models trained on a broad consortium dataset. Designs can then be sent to Tangible and Ginkgo for experimental validation, which are then fed back into the models, creating a lab-in-the-loop cycle that combines predictive accuracy with real-world results.
Streamlined Workflow Benefits
By tightly coupling model-driven design with streamlined logistics to deliver AI/ML-ready assay data in real-time, the partnership enables scientists to iterate quickly and take advantage of cutting-edge digital tools. The integrated approach aims to move from in-silico compound designs to validated experimental results in one digitally-enabled workstream, with fewer synthesis cycles, faster turnarounds, and lower overall drug discovery costs.
Regulatory Alignment and Availability
The partnership supports the FDA's push for faster, more efficient development timelines, onshored manufacturing and services, greater use of AI tools, and adoption of non-animal approaches in preclinical testing. By aligning design, experiment, and data delivery into a single connected process, the collaboration aims to reduce the time and uncertainty in early-stage drug development.
The services are available now and are tailored for pharmaceutical and biotech teams looking to improve ADMET (搜索) optimization and reduce iteration times on programs and across portfolios. This customer-first model eliminates the friction of traditional, fragmented operations, reducing errors, increasing speed, and freeing scientists to focus on discovery instead of internal and external coordination.