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TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection

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Abstract

Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

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Data availability

The datasets used in the current study are available in the repository https://github.com/hkmztrk/DeepDTA/tree/master/data.

Notes

  1. https://pypi.org/project/padelpy/

  2. https://github.com/agemagician/ProtTrans.

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Authors and Affiliations

  1. Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India

    Tanya Liyaqat & Tanvir Ahmad

  2. The Chinese University of Hong Kong, Sha Tin, SAR, China

    Chandni Saxena

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  1. Tanya Liyaqat
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  2. Tanvir Ahmad
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TL: methodology, software, validation, formal analysis, investigation, data curation, writing—original draft. TA: validation, formal analysis, investigation, writing—review and editing. CS: writing—review and editing, formal analysis, conceptualization, validation, visualization.

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Correspondence to Tanya Liyaqat.

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Liyaqat, T., Ahmad, T. & Saxena, C. TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection. J Comput Aided Mol Des 37, 573–584 (2023). https://doi.org/10.1007/s10822-023-00533-1

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