Computational Aspect of Plant Bioprospection for Active Pharmaceutical Ingredients/New Chemical Entity

Résumé fourni par la source

The application of computational biology in natural product research to identify and develop active pharmaceutical ingredients (APIs) and new chemical entities (NCEs) is revolutionizing the drug discovery and development process. A wide range of computational approaches, including cheminformatics, molecular docking, molecular dynamics simulations, pharmacophore modeling, machine learning, and systems biology, are applied to identify and validate pharmacologically active secondary metabolites. These in silico methods play a crucial role in the early stages of drug discovery and development, facilitating the identification of drug targets, elucidating biological pathways, predicting binding affinities and structure–activity relationships, screening high-throughput drug candidates, and analyzing biomolecular flexibility and dynamics. However, the inherent complexity of plant metabolomes, limitations in chemical database annotation, algorithmic biases, reproducibility, and difficulties in transitioning theoretical predictions into in vivo efficacy hinder progress. Addressing the above requires interdisciplinary collaborations to synergize bioinformatics, pharmacognosy, and medicinal chemistry to develop a more solid and reliable drug discovery pipeline. Notwithstanding, computational bioprospection has proven to be economical while accelerating the discovery and optimization of APIs and NCEs against prevalent chronic and degenerative diseases, diabetes, cancer, and microbial infections. This chapter critically discusses plant bioprospecting, relevant databases and knowledge resources, and the computational approaches used to identify APIs and new chemical entities (NCEs), while highlighting their potential and associated complexities. The need for ethical and sustainable exploration of plant biodiversity for medicinal development was appraised. Furthermore, the chapter offers future perspectives aimed at empowering pharmacologists and computational and systems biologists to identify next-generation APIs and NCEs from plants through innovative and data-driven methodologies.

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