Datagrok vs D360
title: "Datagrok vs D360" sidebar_label: D360 mdx: format: mdx description: Compares Datagrok and Certara's D360 across architecture, data integration, visualization, cheminformatics, and extensibility for life-science R&D. keywords:
- certara vs datagrok
- d360 alternative
- certara alternative
- drug discovery R&D platform
- cheminformatics platform comparison last_update: date: November 3, 2026
D360 brings discovery data together. The question now is how much scientific work your team can complete in that environment, and how quickly it can introduce new capabilities as the science changes.
Public accounts identify several sources of additional work around the existing environment:
- Analytical hand-offs: for example, exporting data to JMP and Spotfire for advanced analysis.
- Application development: D360’s REST services support custom applications built around its server; those applications still need interfaces and supporting infrastructure.
- Separate products: broader workflows span products such as Chemaxon Design Hub, Chemaxon Compound Registration, and D360 Partner.
These boundaries add work between retrieving data and making a scientific decision: maintaining analytical context across exports, supporting custom applications, and coordinating integrations and upgrades.
Certara’s stated direction is to unify its early-stage discovery software, including a long-term plan to converge D360 and Chemaxon Design Hub into one design-and-analysis application. For customers planning their next investment, that makes this a useful moment to evaluate the architecture they want to build on.
The business case is faster design decisions through fewer analytical hand-offs, a smaller application estate to support, and AI that can act within the scientific workflow.
Datagrok provides that shared scientific environment today. Scientists work with sequences, molecular structures, assay results, and predictions in one dataset, and analyze, model, and share them on one browser-based platform. Developers extend that environment with shared scientific components. Each new capability can become available to other applications and workflows. That architecture also gives AI access to scientific operations and analytical context. Scientists can ask in plain language, and agents can invoke and combine platform functions within the configured permissions and execution controls.
At a glance
| Decision | D360 | Datagrok | Business implication |
|---|---|---|---|
| Platform integration | D360–Design Hub convergence is a stated long-term roadmap | Scientific applications share a common platform today | Build on an available architecture |
| Analysis | Discovery analytics, including SAR; documented use of external tools (e.g., JMP, Spotfire) for advanced analysis | Scientific exploration, custom models, and linked views within one browser workspace | Fewer hand-offs between tools and teams |
| Cross-modality support | Small-molecule and sequence analysis capabilities | Scientific types share a dataframe, analytical state, and extensible function framework | Analyze representations and measurements together |
| Software stack | D360 plus integrations with products such as Chemaxon Design Hub, Chemaxon Compound Registration, and D360 Partner | Scientific analytics, models, and custom applications share one governed platform | Fewer applications and integration dependencies |
| New capabilities | REST/Python access to data and server operations; custom applications can be built around the server | Extend data handling, scientific functions, viewers, and applications using shared components | Less development effort and infrastructure to maintain for each new capability |
| AI | Publicly documents REST-based data supply to AI/ML | Agents operate on platform functions and analytical context | Use natural language to access data and execute scientific workflows |
Faster decisions through shared scientific context
A discovery decision often requires several representations of the same candidate: a sequence, a chemical structure, experimental measurements, and predicted properties. Keeping those connected matters more than opening each in a separate specialist tool.
Datagrok treats molecules and sequences as scientific types within its data model. Their meaning determines rendering, applicable functions, and contextual tools. Different scientific columns can coexist with numerical measurements and metadata in one dataframe. Linked viewers share selection, filtering, and the current record.
For a modified-peptide project, that means a team can examine sequence variation alongside atomic structures, potency, stability, and model outputs. Datagrok provides sequence-to-structure conversion, peptide SAR, chemistry operations, and general analytics that can be assembled around that question. A scientist can move between representations while preserving the selected candidates and analytical context.
D360 also supports sequence analysis and peptide design. The distinction is the common data and execution framework that Datagrok exposes across scientific tools and custom applications. Documented D360 workflows include exporting data to JMP and Spotfire for advanced analysis. Datagrok provides an environment in which interactive exploration and custom Python or R computations can remain part of the same workflow.
Business outcome: in Datagrok, the investigation happens in one working context, reducing the exports, reconstruction of analyses, and coordination that otherwise separate a scientific question from a decision.
Fewer applications to build, integrate, and support
The cost of research software extends beyond its license. Each additional application may introduce another deployment, interface, integration, permission model, and upgrade dependency.
Certara's discovery offering spans distinct products. D360 integrates with Chemaxon Design Hub, Chemaxon Compound Registration, and other tools; D360 Partner provides external-partner access. These product boundaries matter when assessing the complete deployment and its commercial scope.
Datagrok provides a common platform for scientific applications, including registration and design workflows, analytics, and collaboration. Packages contribute functionality to the same environment, allowing an application to reuse chemistry, biology, data access, and UI components already present.
Its browser workspace also removes the need to distribute and update a desktop analytical client on each scientist’s computer. Local filtering, visualization, and selected chemistry computations use the browser's resources; server services handle managed access and server-side computation.
Business outcome: opportunities to consolidate applications and reduce repeated engineering and administration.
New capabilities without another standalone application
A REST API lets developers access a system. Building a usable scientific application around that access still requires substantial work: inputs, rendering, visualizations, event handling, execution, and integration into the scientist's workflow.
D360's REST services and Python wrapper expose data and server operations. Datagrok exposes the analytical workspace itself: dataframes, functions, events, viewers, scientific renderers, contextual panels, and complete applications. These are different levels of programmability.
Consider an internal peptide-scoring method. In Datagrok, the team can register it as a function with typed inputs and outputs, generate an interface, return predictions to the analytical dataset, and incorporate it into a shared workflow through code or the user interface. The platform supports execution, versioning, permissions, and traceability.
The user concentrates on the method and its scientific use instead of recreating the surrounding application infrastructure. The same function can be reused in another application or a multi-step workflow. When workflow inputs change, the platform helps identify affected downstream steps. Public source for scientific packages provides implementations teams can inspect and adapt.
Business outcome: research IT has less to build and maintain for each new request. Scientists can assemble analyses and use published methods through the UI without writing code. A method, viewer, or scientific data type developed for one project becomes a building block for subsequent projects.
AI with access to the scientific workflow
The useful question about AI is what it can do with the organization's data and tools.
Certara's current D360 page describes using REST services to supply cleaned and aggregated data to AI and ML.
Datagrok exposes structured information about data, functions, viewers, and UI state. The distinction here is the AI’s ability to operate within the analytical workspace itself. AI uses that context to select applicable methods, chain operations into executable workflows, and interact with the current analysis. The underlying function framework gives an agent executable scientific operations alongside the data and context needed to use them.
For example, a scientist can ask AI to convert sequences into molecular structures, calculate properties, and visualize the results alongside activity measurements. AI executes the workflow through the same platform functions available to the scientist, within the user’s permissions. The operations and results remain available for inspection and comparison with a manually performed analysis.
Business outcome: teams across the organization can use AI in their daily scientific work, with shared tools, consistent permissions, and reviewable results.
A platform investment that compounds
For a long-term decision, the practical question is how much work survives the next change in research direction. Can an internal model serve another team? Can a new modality use existing analytics? Can a new application reuse scientific components and governance?
Datagrok's proposition is a common scientific platform. Data access, scientific types, computation, visualization, and application development form the foundation on which domain capabilities are delivered. Datagrok's package and function architecture is designed to support that reuse. It gives the organization a direct way to extend its environment as its requirements change.
The strategic advantage is cumulative: each new method, viewer, and workflow adds to a shared scientific environment. Teams gain capability without rebuilding the supporting infrastructure each time and retain more control over how their software evolves with their science.