Cross-Species Insights: Transforming Drug Efficacy from Rats to Humans Using Tissue-Specific Generative Models

Within the realm of drug development, the transition from successful animal trials to human clinical efficacy remains a daunting challenge. While initial outcomes may appear promising in animal studies, ensuring similar effectiveness in humans, especially across specific target tissues, presents a significant obstacle. To address this pressing concern, we introduce a novel generative model tailored to optimize molecules that have demonstrated efficacy in rats for enhanced performance in specific human tissues. Central to our solution is the transformer architecture, enhanced with intricate mechanisms such as molecule self-attention within the encoder and a novel dedicated tissue-specific generator. Intuitively, by learning to generate molecules simultaneously from multiple tissues, the generative model enhances its ability to perform the necessary adaptations from rats to humans. Through rigorous empirical evaluation across various tissues, our model consistently exhibits remarkable efficacy compared to existing methods. We anticipate that this model has the potential to minimize the requirement for lengthy and inconclusive trials, thereby streamlining the drug development process.

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Publication Details

Published
2025-04-04
DOI
https://doi.org/10.1145/3690624.3709389
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Cross-Species Insights: Transforming Drug Efficacy from Rats to Humans Using Tissue-Specific Generative Models

Kira Radinsky, Sally Turutov
Computational Drug Discovery Methods
article

Cross-Species Insights: Transforming Drug Efficacy from Rats to Humans Using Tissue-Specific Generative Models

Kira Radinsky, Sally Turutov
article en

Abstract

Within the realm of drug development, the transition from successful animal trials to human clinical efficacy remains a daunting challenge. While initial outcomes may appear promising in animal studies, ensuring similar effectiveness in humans, especially across specific target tissues, presents a significant obstacle. To address this pressing concern, we introduce a novel generative model tailored to optimize molecules that have demonstrated efficacy in rats for enhanced performance in specific human tissues. Central to our solution is the transformer architecture, enhanced with intricate mechanisms such as molecule self-attention within the encoder and a novel dedicated tissue-specific generator. Intuitively, by learning to generate molecules simultaneously from multiple tissues, the generative model enhances its ability to perform the necessary adaptations from rats to humans. Through rigorous empirical evaluation across various tissues, our model consistently exhibits remarkable efficacy compared to existing methods. We anticipate that this model has the potential to minimize the requirement for lengthy and inconclusive trials, thereby streamlining the drug development process.

Technion – Israel Institute of Technology (IL)
Zero hunger
Openalex Percentile: Top 92%
Computational Drug Discovery Methods
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