AI Tools for Designing Phytopharmacological Agents

Artificial intelligence (AI) is revolutionizing phytopharmacology by addressing the complexity, diversity, and labor-intensive processes associated with plant-based drug discoveries. The traditional method of phytopharmacology utilizes intensive screening and disjointed data integration, making it difficult to reproduce the results and translate them into new applications. However, AI makes it easier to process big data sets, making it possible to quickly identify phytochemicals and to determine their potency, molecular targets, and safety profiles. Machine learning algorithms and deep learning provide assistance in identifying patterns from disorganized phytochemical databases, making it possible to rank new bioactive leads and understand their mode of action. Together with advancements in this area, new tools and computational models assisted by AI have been developed to aid pharmacological research. Omics-based databases integrate genomics, transcriptomics, proteomics, and metabolomics, thus provide assistance in understanding complex interactions between plants and their compounds. Whereas computing algorithms helps in model-based prediction of drug target interaction, pharmacokinetics, and toxicities. De novo molecular design tools helps in improving plant-based lead compounds to be safer, easier to absorb, and more efficacious while maintaining their plant-based sources. Instead of separate tools, novel AI frameworks work in uniting concepts of system biology, network pharmacology, and omics-based analysis into single predictive models to be used in phytopharmacology research. These frameworks allow understanding of phytochemical-based drugs that target multiple sites simultaneously, making it possible to understand plant drugs comprehensively. Though many advancements have been achieved through AI and phytopharmacology, techniques such as interpretability of AI models, standardizing big data, and addressing ethical questions must be considered to make it relevant to human health. Despite these limitations, the union of AI and phytopharmacology has introduced a new philosophy known as prediction-based innovation in comparison to traditional description-based approaches associated with plant-based drugs. This chapter discusses progress in the application of AI tools such as generative chemistry and omics-based databases to comprehensively reform phytopharmacology-based innovation for safer, faster, and more personalized approaches to drug production.