AI-Enabled Personalized Therapeutics: Integrating Patient Data, Molecular Modelling and Adaptive Treatment Design

Jens Meiler

AI-Enabled Personalized Therapeutics: Integrating Patient Data, Molecular Modelling and Adaptive Treatment Design

Author(s)	Dr. Jens Meiler
Country	United States
Abstract	Personalized therapeutics seeks to identify treatments that correspond to the biological characteristics, clinical history, environmental exposures, and changing health status of an individual patient. Conventional treatment selection frequently depends on population-level evidence that may not adequately represent variation in molecular drivers, comorbidities, pharmacokinetics, treatment tolerance, or disease progression. Artificial intelligence provides computational methods for integrating electronic health records, genomic profiles, molecular measurements, medical images, and longitudinal monitoring data. Molecular modelling adds mechanistic information about drug–target interactions, pathway activity, resistance, and toxicity, while adaptive treatment design allows therapeutic strategies to be revised as new patient evidence becomes available. This simulation-based study develops a framework that combines these capabilities within a clinically governed decision-support process. Four configurations were compared: clinical-data-only prediction, clinical-plus-genomic modelling, multimodal artificial intelligence, and adaptive molecularly informed treatment design. Simulated results indicate that increasing data completeness improved treatment-response prediction, although the magnitude of improvement depended on the model’s ability to use heterogeneous information. At complete simulated data availability, the adaptive model achieved an illustrative response-prediction score of 96, compared with 74 for the clinical-only model, 85 for the clinical-plus-genomic model, and 92 for multimodal artificial intelligence. These values are analytical simulations and do not represent clinical-trial results. The findings suggest that personalized treatment design benefits from integrating patient-level evidence with molecular mechanisms and repeated clinical feedback. However, clinical translation requires representative data, external validation, uncertainty estimation, privacy safeguards, explainability, prospective trials, and qualified professional oversight. Artificial intelligence should support rather than independently replace clinical judgment.
Keywords	personalized therapeutics, precision medicine, artificial intelligence, molecular modelling, treatment-response prediction, adaptive therapy, multimodal patient data, clinical decision support
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-09

View / Download PDF File

About AJAMRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamri.com

American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Enabled Personalized Therapeutics: Integrating Patient Data, Molecular Modelling and Adaptive Treatment Design

Share this