Optimalisasi Prediksi Afinitas Interaksi Obat-Target dengan Graph Neural Network dan Attention Mechanism
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Abstract
Virtual screening pada obat memainkan peran penting dalam meningkatkan throughput penemuan dan mengurangi biaya R&D. Deep learning muncul sebagai solusi menjanjikan, menawarkan hasil yang efisien tanpa memerlukan keahlian domain yang luas atau detail struktural. Studi ini memperkenalkan iGanDTA, sebuah perbaikan dari model multitask yang mampu memprediksi afinitas pengikatan obat-target dengan akurasi tinggi dan mengklasifikasikan interaksi dengan performa yang sangat baik. Menggunakan residual graph neural network, iGanDTA memproses data fingerprint dari senyawa untuk membedakan tingkat pengikatan dalam urutan protein. Evaluasi pada dataset benchmark menunjukkan performa yang superior dibandingkan dengan metode yang ada, dengan iGanDTA mencapai skor MSE, CIndex, dan R2 masing-masing 0.238, 0.894, dan 0.710 pada dataset Davis serta 0.181, 0.864, dan 0.746 pada dataset KIBA
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