A novel attention-based convolution transpositional interfusion network (ACTIN) has shown promising results for drug discovery with limited data. Utilizing just 393 training instances, ACTIN achieved state-of-the-art performance by leveraging graph convolution and transformer mechanisms to analyze drug and transcriptome data. It identifies pharmacophores that may benefit surgical patients, aiming to reduce complications and expedite recovery. Furthermore, validation using COVID-19 patient data led to the development of novel lead chemicals congruent with clinical evidence.
• Why it matters: Improves drug discovery efficiency for surgical treatment applications.
Journal Article by Fan Z, Zhao H (…) Ji S et 4 al. in Int J Surg
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