The Essential AI Toolkit for Modern Biology

If you’ve been hearing about how artificial intelligence is changing drug discovery, genomics, and more, you might wonder: How do scientists actually build and use these AI models? Thankfully, there’s an ever-growing set of tools and platforms that make AI accessible to biology labs of all sizes.

First, there are general-purpose frameworks like TensorFlow (from Google) and PyTorch (from Meta). These give researchers the core building blocks to develop custom AI models—everything from neural networks for image recognition to sequence-based models for genomics. Both are open-source and have huge communities, which means lots of tutorials and active support. Keras, now part of TensorFlow, is known for its user-friendly interface, making it easier for beginners to quickly prototype ideas.

Next, there’s scikit-learn, which is a go-to library in Python for traditional machine learning methods. Not every problem requires a deep neural network; sometimes a random forest or a support vector machine can do the job just fine. Scikit-learn offers a bunch of well-optimized algorithms, plus tools for data splitting, validation, and evaluation.

But what about tasks that are more specific to biology, like molecule screening or protein-ligand docking? That’s where specialized libraries come in. DeepChem is one such platform designed for deep learning in chemistry and the life sciences. It has built-in functions for molecular featurization, plus benchmarks so researchers can easily compare their models.

For genomics, the Broad Institute’s GATK toolkit remains a standard, but newer AI-driven variant callers like DeepVariant can be integrated into GATK pipelines. In single-cell transcriptomics, tools like Scanpy (Python) or Seurat (R) are widely used to handle large datasets, cluster cells, and even apply AI-based algorithms for dimension reduction. Meanwhile, BioPython and Bioconductor help with more general bioinformatics tasks, from parsing sequence files to analyzing expression data.

Protein folding had its own revolution with AlphaFold and RoseTTAFold. Both have open-source versions that scientists can run locally or via cloud services. These tools allow anyone to get a predicted protein structure just by inputting its amino acid sequence—something once considered impossible. Online servers also exist, so even smaller labs without major computing power can benefit.

Finally, platforms like Terra, DNAnexus, and Seven Bridges offer cloud-based environments where labs can store big data and run AI pipelines at scale. They often host preconfigured workflows for tasks like whole-genome sequencing analysis, making it even easier to set up robust computational biology projects.

All of this means that AI isn’t just for computer scientists anymore. Thanks to these tools and platforms, even a small biology lab can explore machine learning solutions. Sure, there’s still a learning curve, but the barrier to entry has dropped dramatically in recent years. With a little coding know-how, you can tap into powerful AI to analyze data, predict protein structures, or find that next blockbuster drug. And that’s exactly why so many young scientists are excited about the future of computational biology.