From Bench to Algorithms: Artificial Intelligence Applications in the Chemical Engineering of Coumarin-Based Therapeutics

Abstract

Problem: Coumarin derivatives exhibit broad pharmacological activities and substantial therapeutic potential, yet the integration of artificial intelligence (AI)with chemical engineering across their discovery, synthesis, optimization, and pharmaceutical manufacturing remains fragmented. Aim: This review critically evaluates AI-assisted methodologies and chemical engineering strategies across the development continuum of coumarin-based therapeutics, from molecular discovery to process optimization and intelligent manufacturing. Methods: A comprehensive literature review examined applications of machine learning, deep learning, generative AI, chemoinformatics, reinforcement learning, digital twins, molecular modeling, reaction optimization, and AI-assisted pharmaceutical manufacturing relevant to coumarin chemistry and drug development. Results: Current evidence indicates that AI can support prediction of biological activity, absorption, distribution, metabolism, excretion, and toxicity properties, molecular interactions, and synthetic feasibility. Machine learning and generative AI facilitate virtual screening, scaffold design, and lead optimization, whereas reinforcement learning and digital twins offer opportunities for reaction control, continuous-flow synthesis, process optimization, and manufacturing. However, translation remains constrained by limited curated datasets, insufficient model interpretability and experimental validation, and weak integration between computational predictions and industrial workflows. Conclusion: Integrating AI with chemical engineering provides a framework for accelerating coumarin therapeutic development. Progress will depend on high-quality experimental data, explainable models, rigorous validation, multidisciplinary collaboration, and stronger integration of computational approaches with scalable pharmaceutical manufacturing. 

Received: 22 May 2026 | Revised: 28 July 2026 | Accepted: 23 August 2026

Conflicts of Interest

The author declares that he has no conflicts of interest to this work. 

Data Availability Statement

The data that support the findings of this study are openly available in PubChem at https://pubchem.ncbi.nlm.nih.gov/, in ChEMBL at https://www.ebi.ac.uk/chembl/, in DrugBank at https://go.drugbank.com/,in Cambridge Structural Database at https://www.psds.ac.uk/csd and https://www.ccdc.cam.ac.uk/solutions/software/csd/, in Crystallography Open Database at https://www.crystallography.net/cod/index.php, in ADMETlab at https://admetlab3.scbdd.com/,and in GitHub at https://github.com/shap/shap. 

Author Contribution Statement

Yasser Fakri Mustafa: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.