Publications

AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios

Date: 7.30.2025
The approach used in the study, especially the incorporation of knowledge-based features to enrich AI models, holds tremendous promise for not only assessing safety and toxicity assessments of drug candidates but also in other aspects such as target engagement and efficacy of these candidates, early in the development phase.
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Knowledge-enhanced AI to Supercharge ADC Development for Treatment of Cancer

Date: 1.9.2025
The use of Artificial Intelligence (AI) is becoming increasingly prevalent in drug discovery and development and holds the potential to address the challenges in ADC development. AI requires substantial amounts of high-quality data to make reliable predictions of a novel ADC’s safety and efficacy. However, since this therapeutic area is still fairly new, there is limited data available to meet all the challenges of ADC development through AI. In such a scenario, enriching AI with knowledge (hybrid AI) is a very promising approach and provides high-accuracy predictions even in data-limited scenarios.
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50 shades of AI in regulatory science with Dr. Weida Tong from FDA

Date: 6.6.2024
Understanding the context-of-use for each AI shade is crucial to address biases, ensure transparency, and enhance decision-making processes within regulatory frameworks. In this article, the authors emphasize the need for tailored regulatory measures to accommodate AI’s diverse roles, ensuring AI enhances rather than complicates regulatory processes.
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An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Date: 7.11.2023
Applications of Machine Learning and AI to Drug Discovery, Development, and Regulations. Originally published in The AAPS Journal (2023) 25:70
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