Modeling Biochemistry: Decoding the language of nature with AI

In the world of biochemistry, understanding the complexity of biological systems is no small feat. The interactions between proteins, enzymes, genes, and metabolites govern life itself, and their behavior often defies simple prediction. There is a consistently small number of drug candidates passing trials, despite the large amount of investment, research and development in the field, a phenomenon known as Eroom's Law (Moore’s Law in reverse).  [1]

However, in recent years, artificial intelligence (AI) has emerged as a powerful tool to help decode this intricate language of nature. By simulating biochemical processes and predicting molecular behavior, AI is unlocking new possibilities for drug discovery, disease understanding, and beyond. Let’s take a look at the main ways AI is being used to model biochemistry.

1. Drug Discovery and Design

AI is playing an increasingly pivotal role in drug discovery. Many companies have joined the race for AI discovered drugs, with some even reaching final clinical trials. It's an exciting time for the field as new possibilities are being unlocked. [2]

Traditional methods of identifying potential drug candidates can take years, but AI algorithms are speeding up the process. By analyzing vast datasets of biochemical interactions, AI can help scientists identify novel compounds that might interact with disease-related targets in the body. Machine learning models can also predict how small molecules will bind to their target proteins, optimizing the drug design process by narrowing down potential candidates.

Generative models, like those used in protein engineering, can design new molecules with specific properties, such as enhanced stability or improved bioavailability. This is particularly important for developing drugs targeting complex diseases like cancer or Alzheimer’s, where precision and effectiveness are critical. 

2. Predicting Protein Structure and Function

Proteins are the building blocks of life, and their structure dictates their function. For decades, researchers have worked tirelessly to understand how proteins fold into their three-dimensional structures—a puzzle that has significant implications for drug development, diagnostics, and disease understanding. Traditionally, solving the structure of a protein was an arduous and expensive process. 

However, AI-driven tools like AlphaFold by DeepMind [3] have completely transformed the field, accurately predicting protein structures from their amino acid sequences. The impact of this breakthrough was so profound that the developers were awarded the Nobel Prize in Chemistry. [4] As Max Jaderberg, Chief AI Officer at Isomorphic Labs, highlighted at TEDAI 2024, a single model like AlphaFold has saved an incredible billion years of discovery time—ushering in a new era of scientific advancement. 

AlphaFold’s success demonstrates the potential of AI to model biochemistry at a fundamental level, offering insights that can lead to faster drug development and a deeper understanding of disease mechanisms. This technology can not only predict the shape of proteins but also forecast how they will interact with other molecules, making it an indispensable tool in biochemistry.

3. Predicting Toxicity and ADMET Properties

One of the most important tasks in drug development is predicting whether a compound will be toxic to humans or animals. AI is used to model the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of compounds. By analyzing chemical structures and historical data, AI can predict how a drug will behave in the body and whether it will cause adverse effects. This type of modeling is especially important in the early stages of drug discovery, as it helps eliminate compounds that are likely to fail in clinical trials, saving both time and resources. [5]

4. AI in Genomic Data Interpretation

Genetic information is the source code of biological systems. The advances in technology for sequencing DNA and RNA have opened up new possibilities for understanding biology at the genetic level. However, the massive amounts of genomic data generated by sequencing technologies are difficult to interpret without advanced tools. 

AI is being used to mine this genomic data, identifying patterns that can reveal new biological insights. Machine learning models can predict the function of genes, identify regulatory elements, and even suggest how genetic variations contribute to diseases. These insights are crucial for understanding the genetic basis of diseases and for developing gene therapies. [6]

5. Metabolic Pathway Modeling

Metabolic pathways are the series of chemical reactions that occur within a cell, and they are crucial for maintaining cellular function and homeostasis. Modeling these pathways is a complex task due to the vast number of interacting enzymes, substrates, and products involved. AI is used to create predictive models of these biochemical networks, helping to understand how cells process nutrients, produce energy, and respond to environmental changes.

AI models can simulate the flow of metabolites through various pathways, predict how perturbations in these pathways might lead to disease, and help identify biomarkers for disease diagnosis or treatment. This type of modeling is vital for synthetic biology, where researchers are engineering microbes to produce valuable chemicals, biofuels, or even pharmaceuticals. [7]

The Takeaway

AI is transforming the field of biochemistry by providing powerful tools to model complex biological systems, predict molecular behavior, and accelerate drug discovery. Whether it's predicting protein structures, simulating metabolic pathways, or designing novel drugs, AI is helping scientists unlock the secrets of life at an unprecedented scale. 

These systems have unlocked a whole new world of possibilities for science. As AI technologies continue to evolve, their role in biochemistry will only grow, leading to new breakthroughs in healthcare, environmental sustainability, and beyond. It’s a fun time to be around as we are starting to decode the language of nature using AI.

References

[1] Zhavoronkov, A., PhD. (2022, August 23). When will AI beat the Eroom’s law in the pharmaceutical industry? Forbes. https://www.forbes.com/sites/alexzhavoronkov/2022/08/22/when-will-ai-beat-the-erooms-law-in-the-pharmaceutical-industry/ 

Modeling Biochemistry

[2] KP Jayatunga, M., Ayers, M., Bruens, L., Jayanth, D., & Meier, C. (2024). How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons. In Drug Discovery Today (Vol. 29, Issue 6, p. 104009). Elsevier BV. https://doi.org/10.1016/j.drudis.2024.104009

[3] Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

[4] Callaway, E. (2024). Chemistry Nobel goes to developers of AlphaFold AI that predicts protein structures. Nature, 634(8034), 525–526. https://doi.org/10.1038/d41586-024-03214-7

[5] Guo, W., Dong, Y., & Hao, G. (2024). Transfer learning empowers accurate pharmacokinetics prediction of small samples. Drug Discovery Today, 29(4), 103946. https://doi.org/10.1016/j.drudis.2024.103946 

[6] Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321–332. https://doi.org/10.1038/nrg3920

[7] Cuperlovic-Culf, M., Nguyen-Tran, T., & Bennett, S. a. L. (2022). Machine learning and hybrid methods for metabolic pathway modeling. Methods in Molecular Biology, 417–439. https://doi.org/10.1007/978-1-0716-2617-7_18