Scope and impact of artificial intelligence and machine learning & deep learning in biology

doi:10.15406/jbmoa.2025.13.00403

Journal of

eISSN: 2469 - 2786

Bacteriology & Mycology: Open Access

Mini Review Volume 13 Issue 2

Scope and impact of artificial intelligence and machine learning & deep learning in biology

Akbar S Khan

Chemical Biological Defense Department, Defense Threat Reduction Agency, USA

Correspondence: Akbar S Khan, Chemical Biological Defense Department, Defense Threat Reduction Agency, Fort Belvoir, Virginia, USA

Received: May 20, 2025 | Published: June 4, 2025

Citation: Khan AS. Scope and impact of artificial intelligence and machine learning & deep learning in biology. J Bacteriol Mycol Open Access. 2025;13(2):86-87. DOI: 10.15406/jbmoa.2025.13.00403

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Abstract

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming the landscape of biological and biomedical research. These cutting-edge technologies are enabling faster, more accurate data interpretation, drug discovery, and personalized treatment approaches. AI/ML/DL tools can efficiently analyze large-scale datasets from genomics, proteomics, and imaging, while also supporting predictive modeling for protein structure, drug efficacy, and gene editing. From enhancing precision medicine to enabling advances in synthetic biology and CRISPR technologies, AI-driven approaches are becoming integral to innovation in life sciences. Despite their immense potential, challenges related to data quality, interpretability, and ethical concerns remain. This paper highlights key applications, current challenges, and future directions, emphasizing the need for integration with experimental biology and the development of a skilled workforce to fully leverage these tools in biological research.

Keywords: artificial intelligence, machine learning, deep learning, drug discovery, bioinformatics, genomics, proteomics, synthetic biology, systems biology, computational biology

Introduction

Artificial intelligence (AI), Machine Learning (ML) & Deep Learning (DL) currently a cutting-edge concept, have the huge potential to improve the quality of life of human beings. The fields of AI, ML & DL (1, 2 and 3) and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being extracted refined. Basically AI is used to create intelligent machines. ML then allowing machines to learn from data, and deep learning is a specific type of ML that uses powerful neural networks to learn complex patterns. As the field of AI, ML & DL matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI, ML & DL (1, 2 and 3) together now are being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies.

Here's a breakdown of their impact:

Data analysis and interpretation

High-throughput data analysis: AI/ML algorithms can handle the massive datasets generated by genomics, proteomics, and other high-throughput technologies, identifying patterns and insights that would be impossible to discern manually.
Image analysis: AI can automate and enhance the analysis of biological images from microscopy and medical imaging, improving speed and accuracy.
Network analysis: AI helps unravel complex biological networks (e.g., gene regulatory networks, protein-protein interaction networks) by identifying key nodes and pathways.

Predictive modeling and drug discovery

Protein structure prediction: Tools like AlphaFold use deep learning to predict the 3D structures of proteins with high accuracy, crucial for understanding protein function and designing new drugs.
Drug discovery and development: AI/ML models can analyze large datasets of chemical compounds and biological data to predict drug efficacy and toxicity, speeding up the drug discovery process and reducing costs.
Personalized medicine: AI/ML can tailor treatments to individual patients based on their genetic makeup and other clinical data, improving treatment efficacy and reducing side effects.

Research and innovation

Genome editing: AI can aid in the design and optimization of CRISPR-based genome editing tools, enhancing precision and efficiency.
Synthetic biology: AI/ML can be used to design and optimize synthetic biological systems, including novel enzymes, pathways, and cells.
Accelerating research: AI/ML can help scientists develop hypotheses, design experiments, and analyze data more efficiently, accelerating the pace of biological research.
Examples of AI/ML in specific areas:
Genomics: Gene prediction, variant calling, identification of regulatory elements, and prediction of gene expression.
Proteomics: Protein structure prediction, protein function prediction, and protein-protein interaction analysis.
Microscopy: Cell segmentation, cell tracking, and analysis of cell morphology.
Challenges and Future Directions:
Data quality and quantity: AI/ML models require large, high-quality datasets, which may not always be available.
Interpretability: Many AI/ML models are "black boxes," making it difficult to understand how they arrive at their predictions.
Ethical considerations: The use of AI/ML in biology raises ethical concerns about data privacy, bias, and the potential for misuse.
Integration with experimental biology: AI/ML models need to be integrated with experimental workflows to validate their predictions and guide future research.

To tackle grand challenging problems across the biological sciences, researchers increasingly are turning to the development and adoption of AI/ML methods. AI/ML includes any computational tool that mimics intelligence and the ability to learn from data to derive inferences. These methods are powerful tools for analyzing, synthesizing, and integrating large and complex datasets, developing predictive models, and designing and deploying bio-inspired innovations (Figure 1). Unique aspects of information processing in biological systems and the complexity of biological data can also inform and inspire new developments in AI/ML. In addition, AI-enabled research requires a trained workforce prepared to use, develop, and validate appropriate AI/ML approaches and supporting technologies tailored for biological systems.