BioAct-Het: A heterogeneous siamese neural network for bioactivity prediction using novel bioactivity representation

Abstract

Drug failure during experimental procedures due to low bioactivity presents a significant challenge. To mitigate this risk and enhance compound bioactivities, predicting bioactivity classes during lead optimization is essential. The existing studies on structure–activity relationships have highlighted the connection between the chemical structures of compounds and their bioactivity. However, these studies often overlook the intricate relationship between drugs and bioactivity, which encompasses multiple factors beyond the chemical structure alone. To address this issue, we propose the BioAct-Het model, employing a heterogeneous siamese neural network to model the complex relationship between drugs and bioactivity classes, bringing them into a unified latent space. In particular, we introduce a novel representation for the bioactivity classes, called Bio-Prof, and enhance the original bioactivity data sets to tackle …

Mehdi Paykan Heyrati

M.Sc. graduate in Computer Science with a focus on AI and bioinformatics, with research experience in deep learning, contrastive and few-shot learning, and drug-discovery models. My research centers on developing reliable machine-learning pipelines for medical and biological data.

Zahra Ghorbanali

Assistant professor

Fatemeh Zare

Associate Professor

My research interests include bioinformatics, computational biology and artificial intelligence.