bioRxivBioinformaticsPreprint
Precious3GPT: Multimodal Multi-Species Multi-Omics Multi-Tissue Transformer for Aging Research and Drug Discovery
Fedor GalkiniD, Vladimir NaumoviD, Stefan PushkoviD, Denis SidorenkoiD, Anatoly S. UrbaniD, Diana ZagirovaiD, Khadija M. AlawiiD, Alex AliperiD, Ruslan Gumerov, Aleksandr Kalashnikov, Sabina Mukba, Aleksandra Pogorelskaya, Feng ReniD, Anastasia ShneydermaniD, Qiuqiong TangiD, Deyong XiaoiD, Alexander TyshkovskiyiD, Kejun YingiD, Vadim N. GladysheviD, Alex A. ZhavoronkoviD
Abstract
We present a multimodal multi-species multi-omics multi-tissue transformer for aging research and drug discovery capable of performing multiple tasks such as age prediction across species, target discovery, tissue, sex, and disease sample classification, drug sensitivity prediction, replication of omics response and prediction of biological and phenotypic response to compound treatment. This model combines textual, tabular, and knowledge graph-derived representations of biological experiments to provide insights into molecular-level biological processes. We demonstrate that P3GPT has developed an intuition for the interactions between compounds, pathologies, and gene regulation in the context of multiple species and tissues. In these areas, it outperforms existing LLMs and we highlight its utility in diverse case studies. P3GPT is a general model that may be used as a target identification tool, aging clock, digital laboratory, and scientific assistant. The model is intended as a community resource available open source as well as via a Discord server.
- organism: multiple species
- research focus: drug response / drug discovery
- research focus: cell type annotation
- research focus: clustering
- approach: transformer / foundation model
- Citations
- 15
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