Multi-Drug Transfer Learning Models for Efficient Pre-clinical Drug Discovery

Pre-clinical drug discovery faces the low efficiency dilemma. One of the reasons is the lack of cross-drug efficacy evaluation infrastructure at patient level. Here we propose Patient multiple drug learning(P-MDL) task, and construct the P-MDL dataset and model zoo. The best P-MDL model DSN-adv achieve SOTA in all of the 13 tumor types.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-14
DOI
https://doi.org/10.5281/zenodo.8021166
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Multi-Drug Transfer Learning Models for Efficient Pre-clinical Drug Discovery

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

Multi-Drug Transfer Learning Models for Efficient Pre-clinical Drug Discovery

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Abstract

Pre-clinical drug discovery faces the low efficiency dilemma. One of the reasons is the lack of cross-drug efficacy evaluation infrastructure at patient level. Here we propose Patient multiple drug learning(P-MDL) task, and construct the P-MDL dataset and model zoo. The best P-MDL model DSN-adv achieve SOTA in all of the 13 tumor types.

Zenodo (CERN European Organization for Nuclear Research)
Beijing Academy of Artificial Intelligence (CN), Artificial Intelligence in Medicine (Canada) (CA), Tsinghua University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 51%
Computational Drug Discovery Methods
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