Your Essential Guide to Software Tools Deployed in Preclinical Research

Preclinical research today is more complex, data-heavy, and fast-paced than ever before. In a world of big data and multi-faceted study designs, the integration of technology has become vital for maintaining efficiency, reproducibility, and accuracy. Purpose-built software is an indispensable tool for managing data, planning and automating workflows, and ensuring regulatory compliance. Lab management tools and electronic notebooks can boost productivity and aid in collaboration, while bespoke in vivo software can drive a fast and effective literature review. In this post, we’ll walk through the essential software categories supporting each stage of the preclinical R&D pipeline and highlight the leading tools that can help you streamline operations and speed up drug discovery.

The Preclinical Research Workflow and Where Software Fits In 

Preclinical research lays the groundwork for discovery by validating targets and generating the foundational data needed to advance a drug candidate toward clinical trials. The pipeline typically includes:

• Target identification and validation
• Lead discovery and optimization
• In vitro studies
• In vivo studies
• Data analysis and reporting

At each stage, software solutions can be integrated to support design, execution, data capture, analysis, and interpretation. 

Key Software Categories and Tools 

To help you navigate the vast array of software available to preclinical researchers, we’ve outlined the most impactful categories and tools used across preclinical workflows.

Laboratory Information Management Systems (LIMS)

LIMS are foundational platforms that streamline sample tracking, protocols, instrument utilization, data capture, and audit trails. By centralizing lab operations, they improve data integrity, minimize manual errors, and support regulatory compliance¹. These systems are especially valuable in high-throughput environments where traceability and consistency are critical. For projects that span multiple research groups or institutes, LIMS can act as a managing hub to align and standardize protocols and data management. Popular examples include:

• STARLIMS
• QBench 
• LabWare
• Labguru

Electronic Lab Notebooks (ELN) 

ELNs replace paper notebooks with digital platforms that allow researchers to record protocols, observations, and results in real-time, on the go. Unlike traditional lab notebooks, your work is instantly searchable and shareable. Shared templates and protocol libraries help standardize methods in team science and collaborative projects.  ELNs also assist in navigating regulations and the legal framework surrounding drug discovery by attaching dates and user information to work completed². Popular solutions include:

• LabArchive
• SciNote
• OneNote
• Uncountable

Data Analysis & Visualization Software 

Preclinical research generates vast amounts of data, and specialized tools are essential for analyzing, visualizing, and interpreting your results. Types of software include:

• Statistical analysis: Dedicated platforms include SPSS, while many researchers use GraphPad Prism for both visualization and stats. Complex analysis is often performed using the statistical programming language R.
• Flow cytometry analysis: Popular platforms include FlowJo and FCS Express.
• Image analysis: ImageJ/Fiji is free, open access, and has a widespread user base. Many microscope manufacturers have developed their own image processing and analysis software, such as Zen (by Zeiss) and LAS X (by Leica). 
• Sequencing and multi-omic data analysis: The processing and analysis of sequencing and other large datasets are commonly performed using R, a programming language and associated software. More general-purpose programming languages, such as Python and C++, are also used. 
• General data visualization: General-purpose software like Microsoft Excel and Tableau can be used for small datasets and quick visualization.

In Silico & Computational Tools (Drug Design & Modeling) 

Computational drug discovery tools have become vital to identifying and optimizing lead compounds³. These platforms offer features like molecular docking to predict how molecules will interact⁴, virtual screening, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction⁵. Predictive modelling features allow researchers to test hypotheses in silico before undertaking costly experiments. Popular tools include:

• FormBio
• Schrödinger
• AutoDock

Animal Management and In Vivo Experimental Design Software 

Planning and managing animal studies demands precision, compliance, and efficiency. Dedicated in vivo software helps researchers track animal colonies, assign experimental groups, manage dosing, and stay compliant with all necessary regulations (e.g., IACUC). Well-implemented in vivo software promotes animal welfare, reduces costs, and streamlines operations. Of course, ModernVivo is the go-to software for planning your in vivo models, while animal management  platforms include:

• Cayuse
• Mosaic

Emerging Trends & Future of Preclinical Software 

The future of preclinical research will be defined by increased speed, smarter automation, and better-connected work. Here’s what we can expect: 

• AI-powered insights and machine learning will become more mainstream to enhance the analysis of large datasets and predictive modelling.
• Cloud-based solutions will improve access, collaboration, and data security.
• Integration of robotics will enhance high-throughput screening and improve automation.

Choosing the Right Tools: Considerations for Your Lab 

• Needs Assessment: What are your specific research goals, budget, and lab size?
• Scalability: Can the software grow with your research?
• User-Friendliness & Training: How quickly can software be adopted, and how easily will your team onboard?
• Integration Capabilities: How well will new software integrate with your existing instruments and software?
• Vendor Support: Do software vendors offer easily accessible and ongoing support and updates?
• Data Security & Compliance: Does software enable or promote compliance with necessary regulations and maintain the security of sensitive information?

Conclusions: Accelerating Discovery with the Right Software

Well-implemented software can drive efficiency, accuracy, and innovation. By adopting purpose-built tools at every stage of the preclinical pipeline, research teams can enhance data quality, accelerate study timelines, promote effective collaboration, and generate more impactful findings.

If you’re ready to leverage AI-powered software to drive your research, learn how ModernVivo can make your in vivo model literature reviews faster, more precise, and more efficient. Watch the demo today to find out more.

AI Disclosure: Some of this content was generated with assistance from AI tools for copywriting. 

References

1. Prasad PJ, Bodhe GL. Trends in laboratory information management system. Chemom Intell Lab Syst. 2012;118:187-192. doi:10.1016/j.chemolab.2012.07.001

2. Kanza S, Willoughby C, Gibbins N, et al. Electronic lab notebooks: can they replace paper? J Cheminformatics. 2017;9(1):31. doi:10.1186/s13321-017-0221-3

3. Computational approaches streamlining drug discovery | Nature. Accessed June 30, 2025. https://www.nature.com/articles/s41586-023-05905-z

4. Pinzi L, Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci. 2019;20(18):4331. doi:10.3390/ijms20184331

5. Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today. 2012;17(1):44-55. doi:10.1016/j.drudis.2011.10.023