Let’s Give you a quick tour on the Top 25 Best AI Apps for Biology in 2025. This will help in the Unlocking Biological Mysteries with the Power of AI

Biology, once a field dominated by painstaking experimentation and meticulous observation, is undergoing a profound transformation. At the heart of this revolution lies the powerful integration of Artificial Intelligence (AI). From deciphering the intricacies of the genome to predicting protein structures with unprecedented accuracy, AI applications are rapidly accelerating biological research and driving breakthroughs across diverse areas, including drug discovery, personalized medicine, and environmental science.

List Of Top 25 Best AI Apps for Biology in 2025:

This article delves into the exciting world of AI in biology, exploring the groundbreaking tools and techniques that are empowering researchers to unlock complex biological mysteries and pave the way for a future where AI-driven insights lead to a healthier, more sustainable world.

We will examine key applications of AI in analyzing vast biological datasets, simulating complex biological processes, and ultimately, pushing the boundaries of our understanding of life itself.

Protein Structure Prediction and Analysis

These AI tools predict protein structures and analyze molecular interactions, accelerating research in structural biology and drug discovery.

1. AlphaFold

   • Description: Developed by DeepMind, AlphaFold predicts 3D protein structures with near-experimental accuracy, revolutionizing structural biology. It has cataloged over 200 million protein structures.

   • Best For: Researchers in protein modeling and drug design.

   • Pricing: Open-source; free for academic use.

   • Founders: Demis Hassabis, John Jumper (DeepMind).

   • Devices: Web, Linux, cloud platforms.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

2. RoseTTAFold

   • Description: An AI tool for protein structure prediction, offering high accuracy and integration with molecular dynamics simulations for drug discovery.

   • Best For: Computational biologists.

   • Pricing: Open-source; free for non-commercial use.

   • Founders: David Baker (University of Washington).

   • Devices: Web, Linux, cloud platforms.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

3. Rosetta

   • Description: An AI-driven platform for protein structure prediction, folding, and interaction modeling, widely used in drug design and enzyme engineering.

   • Best For: Drug discovery researchers.

   • Pricing: Free for academic use; commercial licenses vary.

   • Founders: David Baker.

   • Devices: Web, Linux, Windows, macOS.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Genomic Data Analysis and Bioinformatics

These tools use AI to process and analyze large genomic datasets, aiding in sequence alignment, variant detection, and gene expression studies.

4. DeepVariant

   • Description: A deep learning-based variant caller by Google, analyzing high-throughput sequencing data to detect genomic changes with high accuracy.

   • Best For: Genomic researchers.

   • Pricing: Open-source; free.

   • Founders: Google Research Team (no individual founders listed).

   • Devices: Web, Linux, cloud platforms.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

5. Bioconductor

   • Description: An open-source R-based platform with AI-enhanced tools for genomic data analysis, including gene expression and sequence alignment.

   • Best For: Bioinformaticians analyzing high-throughput data.

   • Pricing: Free.

   • Founders: Robert Gentleman, Vincent Carey.

   • Devices: Web, Windows, macOS, Linux.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

6. BioPython

   • Description: An open-source Python library with AI modules for sequence analysis, phylogenetic tree construction, and data visualization.

   • Best For: Computational biology students and researchers.

   • Pricing: Free.

   • Founders: Community-driven (no specific founders listed).

   • Devices: Web, Windows, macOS, Linux.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

7. EMBOSS

   • Description: An open-source suite with AI tools for sequence analysis, nucleotide pattern matching, and molecular biology tasks.

   • Best For: Molecular biologists.

   • Pricing: Free.

   • Founders: Community-driven (no specific founders listed).

   • Devices: Web, Linux, Windows, macOS.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

8. BLAST

   • Description: A widely used AI tool for comparing biological sequences, identifying similarities in DNA or proteins for gene function studies.

   • Best For: Sequence analysis researchers.

   • Pricing: Free.

   • Founders: Stephen Altschul, Warren Gish.

   • Devices: Web, Linux, Windows, macOS.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Literature Review and Research Assistance

These AI tools summarize scientific papers, extract data, and assist with literature reviews, saving time for biologists.

9. Scholarcy

   • Description: An AI-powered tool that summarizes articles, extracts key findings, and generates flashcards for quick review of biology research.

   • Best For: Students and researchers conducting literature reviews.

   • Pricing: Free plan; Premium from $8.25/month.

   • Founders: Phil Gooch, Emma Warren-Jones.

   • Devices: Web.

   • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

10. Elicit

    • Description: An AI research assistant that analyzes papers, extracts data, and synthesizes findings, ideal for biology literature reviews.

    • Best For: Academic researchers.

    • Pricing: Free plan; Plus from $12/month.

    • Founders: Jungwon Byun, Andreas Stuhlmüller.

    • Devices: Web.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

11. SciSpace (Typeset.io)

    • Description: Offers AI tools for literature reviews, PDF querying, and paraphrasing, streamlining biology research workflows.

    • Best For: Researchers and publishers.

    • Pricing: Free plan; Premium from $12/month.

    • Founders: Saikiran Chandha, Shanu Kumar.

    • Devices: Web.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

12. Unriddle

    • Description: An AI tool that simplifies research papers, annotates key points, and answers biology questions with source citations.

    • Best For: Students reading complex papers.

    • Pricing: Free plan; Pro from $20/month.

    • Founders: Not publicly listed.

    • Devices: Web.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

13. ChatPDF

    • Description: An AI-driven Q&A tool for querying biology research papers, allowing users to ask questions about uploaded PDFs.

    • Best For: Quick paper analysis.

    • Pricing: Free plan; Plus from $15.99/month.

    • Founders: Not publicly listed.

    • Devices: Web.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Educational and Study Tools

These apps use AI to support biology education, offering interactive learning, exam prep, and personalized study aids.

14. BiologyBuddy.ai

    • Description: An AI study tool for A-Level biology, providing past papers, model answers, and personalized study tips powered by GPT-4.

    • Best For: A-Level biology students.

    • Pricing: Free (MVP stage); paid version planned.

    • Founders: Not publicly listed.

    • Devices: Web.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

15. Mindgrasp

    • Description: An AI tool for biology students, generating summaries, flashcards, and quizzes from study materials for effective learning.

    • Best For: College biology students.

    • Pricing: Free trial; Premium from $9.99/month.

    • Founders: Not publicly listed.

    • Devices: Web, iOS, Chrome extension.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

16. Socratic by Google

    • Description: A free AI app that answers biology questions using text or camera inputs, providing explanations and resources for students.

    • Best For: High school biology students.

    • Pricing: Free.

    • Founders: Google (no individual founders listed).

    • Devices: iOS, Android.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Image Analysis and Visualization

These AI tools enhance biological image analysis, from cell segmentation to 3D modeling, supporting research and education.

17. CellProfiler

    • Description: An open-source AI tool for analyzing biological images, automating cell segmentation and feature extraction for microscopy data.

    • Best For: Cell biology researchers.

    • Pricing: Free.

    • Founders: Anne Carpenter, Thouis Jones.

    • Devices: Windows, macOS, Linux.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

18. Ilastik

    • Description: An AI-powered tool for interactive image classification and segmentation, ideal for analyzing microscopy images in biology.

    • Best For: Biologists with imaging data.

    • Pricing: Free.

    • Founders: Not publicly listed (Max Planck Institute).

    • Devices: Windows, macOS, Linux.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

19. Corinth

    • Description: A 3D visualization tool with AI-enhanced models for exploring cells, tissues, and organisms, ideal for biology education.

    • Best For: Teachers and students.

    • Pricing: Contact for pricing.

    • Founders: Not publicly listed.

    • Devices: Web, Windows, macOS.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Drug Discovery and Biotechnology

These AI tools accelerate drug discovery, optimize molecular design, and enhance biotech research.

20. OpenCRISPR-1

    • Description: An AI-designed CRISPR-Cas system by Profluent AI, offering precise gene editing with reduced off-target effects.

    • Best For: Gene editing researchers.

    • Pricing: Open-source; free for academic use.

    • Founders: Ali Madani (Profluent AI).

    • Devices: Web, Linux, cloud platforms.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

21. Cancerappy

    • Description: An AI platform for drug discovery, analyzing compound databases to identify oncological targets and optimize lead candidates.

    • Best For: Oncology researchers.

    • Pricing: Contact for pricing.

    • Founders: Not publicly listed.

    • Devices: Web, cloud platforms.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

22. Gigabiome

    • Description: An AI-powered platform analyzing microbiome data to identify antimicrobial resistance and design small molecules.

    • Best For: Microbiome researchers.

    • Pricing: Contact for pricing.

    • Founders: Not publicly listed.

    • Devices: Web, cloud platforms.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Machine Learning Frameworks for Biology

These frameworks enable custom AI model development for biological data analysis.

23. TensorFlow

    • Description: A versatile AI framework used in biology for neural network training, gene expression modeling, and cell classification.

    • Best For: Bioinformaticians building custom models.

    • Pricing: Free.

    • Founders: Google Brain Team (no individual founders listed).

    • Devices: Web, Windows, macOS, Linux, cloud platforms.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

24. PyTorch

    • Description: A scalable AI framework for biological data analysis, supporting neural networks for gene expression and protein modeling.

    • Best For: Researchers developing predictive models.

    • Pricing: Free.

    • Founders: Adam Paszke, Sam Gross, Soumith Chintala.

    • Devices: Web, Windows, macOS, Linux, cloud platforms.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Conversational AI for Biology Research

These tools use conversational AI to answer biology questions and assist with research tasks.

25. HyperWrite Biology Assistant

    • Description: An AI-powered research tool that answers biology questions using scholarly articles, ideal for students and researchers.

    • Best For: Quick biology inquiries.

    • Pricing: Free trial; Premium from $19.99/month.

    • Founders: Matt Shumer, Jason Kuperberg.

    • Devices: Web.

    • 𝐕𝐈𝐒𝐈𝐓 𝐖𝐄𝐁𝐒𝐈𝐓𝐄𝚌𝚕𝚒𝚌𝚔 𝚑𝚎𝚛𝚎

Key Considerations for Choosing an AI Biology App

• Accuracy: AlphaFold and DeepVariant offer near-experimental precision for protein and genomic analysis.

• Cost: Open-source tools like BioPython and Bioconductor are free; premium tools like Elicit and Scholarcy offer advanced features for a fee.

• Device Compatibility: Most tools are Web or cloud-based; CellProfiler and Ilastik support desktop platforms.

• Use Case: Choose tools like Rosetta for drug discovery, Scholarcy for literature reviews, or BiologyBuddy for education.

• Learning Curve: Tools like Socratic and Mindgrasp are beginner-friendly; TensorFlow and PyTorch require coding expertise.

• Market Trends: AI in biology is expanding, with 60% of biotech firms adopting AI for drug discovery, per industry reports.

• Limitations: AI tools may require manual validation for complex datasets; computational resources can be a barrier for tools like Rosetta.

Responsible Use Note

AI tools enhance biological research but should be used with critical oversight. Always verify outputs against primary sources and consult domain experts for high-stakes applications like drug development or clinical research.