Decoding the mystery: AI-assisted bioinformatics and functional genomics technologies in medicinal plants

Abstract

Introduction For millennia, medicinal plants have been a cornerstone of human healthcare, providing a rich source of bioactive compounds used in both traditional and modern medicine. A diverse array of therapeutic molecules is offered by these plants, from the antimalarial artemisinin in Artemisia annua to the anticancer alkaloids in Catharanthus roseus. The integration of artificial intelligence (AI) with bioinformatics and functional genomics has revolutionized the study of these medicinal plants, enabling researchers to explore their genetic and molecular underpinnings with unprecedented accuracy. These integrated technologies are transforming the study of medicinal plants, including drug discovery, responses to abiotic stresses, and the therapeutic potential of sustainable healthcare. However, the complexity and volume of genomic data pose significant challenges, necessitating advanced computational tools. AI, incorporating machine learning (ML) and deep learning (DL) techniques, has emerged as a powerful solution, capable of processing large volumes of data, identifying patterns and making predictions that traditional methods cannot match. This opinion explores several areas in which AI models in bioinformatics and functional genomics analysis are transforming medicinal plant research. Through detailed discussions and an exploration of future trends, we highlight how AI is reshaping our approach to medicinal plants, offering new possibilities for drug development and sustainable agriculture. ML is considered a core technology in AI. Standard ML methods are overly narrow in their application to complex, natural, and high-dimensional raw data like genomic data. In contrast, DL methods are a promising and exciting area currently being is widely applied in genomics, with successful applications in image recognition, audio classification, natural language processing, online web tools, chatbots, and robotics (Alharbi and Rashid, 2023). In this regard, DL as a genomics method is well-suited for analyzing large amounts of data. Although DL is still in its infancy in genomics, it holds the potential to transform fields such as clinical genetics and functional genomics. Multiple genomic fields are leveraging the generation of high-throughput data and harnessing the power of deep learning algorithms to make complex predictions. Modern advances in DNA/RNA sequencing technologies and machine learning algorithms, particularly deep learning, have opened up a new chapter in research, enabling the translation of large biological datasets into new knowledge and discoveries across various subfields of genomics (Lee, 2023). In the field of next-generation sequencing, modern deep learning tools have been proposed to overcome the limitations of traditional interpretation pipelines (Alharbi and Rashid, 2023). It has demonstrated that combining the deep learning-based variant caller DeepVariant with traditional variant callers (such as SAMtools and GATK) can improve the accuracy scores of single-nucleotide variant and indel detection (Kumaran et al., 2019). DeepVariant relies on graphical differences in input images to perform the classification task of genetic variant calling from NGS short reads (Hall et al., 2024). It treats mapped sequencing datasets as images and transforms variant calling into an image classification task. Functional genomics aims to reveal the roles of genes and their interactions in biological systems. Traditional methods, such as gene set enrichment analysis, rely on existing genomic databases and are relatively cumbersome and time-consuming. However, many intriguing biological questions often exceed the limitations of these databases, and the introduction of AI offers new possibilities for filling these gaps. AI is reshaping the traditional way genomics research is conducted. By utilizing large language models (LLMs), scientists can significantly reduce manual analysis time and rapidly identify gene functions and inter